Integrated Evaluation of Vegetation Drought Stress Through Satellite Remote Sensing †

Article Integrated Evaluation of Vegetation Drought Stress through Satellite Remote Sensing †

Daniela Avetisyan 1,*, Denitsa Borisova 1 and Emiliya Velizarova 2

1 Space Research and Technology Institute of the Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl. 1, 1113 Soﬁa, Bulgaria; [email protected] 2 Ministry of Environment and Water, Maria Luisa Blvd. 22, 1000 Soﬁa, Bulgaria; [email protected] * Correspondence: [email protected] † This paper is dedicated to the memory of Prof. Roumen Nedkov, a brilliant scientist, who thrived on helping and teaching others learn.

Abstract: In the coming decades, Bulgaria is expected to be affected by higher air temperatures and decreased precipitation, which will significantly increase the risk of droughts, forest ecosystem degradation and loss of ecosystem services (ES). Drought in terrestrial ecosystems is characterized by reduced water storage in soil and vegetation, affecting the function of landscapes and the ES they provide. An interdisciplinary assessment is required for an accurate evaluation of drought impact. In this study, we introduce an innovative, experimental methodology, incorporating remote sensing methods and a system approach to evaluate vegetation drought stress in complex systems (landscapes and ecosystems) which are influenced by various factors. The elevation and land cover type are key climate-forming factors which significantly impact the ecosystem’s and vegetation’s response to drought. Their influence cannot be sufficiently gauged by a traditional remote sensing-based drought

index. Therefore, based on differences between the spectral reﬂectance of the individual natural land cover types, in a near-optimal vegetation state and divided by elevation, we assigned coefﬁcients for

Citation: Avetisyan, D.; Borisova, D.; normalization. The coefficients for normalization by elevation and land cover type were introduced Velizarova, E. Integrated Evaluation in order to facilitate the comparison of the drought stress effect on the ecosystems throughout a of Vegetation Drought Stress through heterogeneous territory. The obtained drought coefficient (DC) shows patterns of temporal, spatial, Satellite Remote Sensing. Forests 2021, and interspecific differences on the response of vegetation to drought stress. The accuracy of the 12, 974. https://doi.org/10.3390/ methodology is examined by field measurements of spectral reflectance, statistical analysis and f12080974 validation methods using spectral reflectance profiles.

Academic Editor: John J. Couture Keywords: vegetation response; spectral indices; Sentinel-2; spectral reﬂectance proﬁles; Bulgaria

Received: 19 May 2021 Accepted: 15 July 2021 Published: 22 July 2021 1. Introduction

Publisher’s Note: MDPI stays neutral Drought is a part of the climate and can occur in any climate regime around the with regard to jurisdictional claims in world [1]. A constant increase in extreme climate-related events has been observed, with a published maps and institutional afﬁl- particularly sharp rise in the number of hydrological events [2,3]. This can have a direct iations. effect on the frequency and intensity of droughts, leading to possible negative consequences for land-production systems [4]. Drought can arise from a range of hydro-meteorological processes that suppress precipitation and/or limit surface water or ground water availability, creating conditions that are significantly drier than normal [5]. The precipitation deficits generally appear initially as Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. a deficiency in soil water [6]. It usually takes several weeks before precipitation deficiencies This article is an open access article begin to produce soil moisture deficiencies that lead to stress on ecosystems. distributed under the terms and Drought impacts different regions and climate zones around the world [7]. The anal- conditions of the Creative Commons yses of drought variability during the last few decades show some regions with drying Attribution (CC BY) license (https:// patterns in Europe. Situated in Southeastern Europe, Bulgaria is also subjected to the fre- creativecommons.org/licenses/by/ quent short- and long-term droughts [8,9]. Increases in air temperatures and a reduction 4.0/). in precipitation in Bulgaria are expected within the next few decades [10], which will lead

Forests 2021, 12, 974. https://doi.org/10.3390/f12080974 https://www.mdpi.com/journal/forests Forests 2021, 12, 974 2 of 32

to higher transpiration and evapotranspiration values, especially in the summer seasons. This will significantly increase the risk of drought, loss of biodiversity and ES [11], and requires an accurate assessment of ecosystem responses to this phenomenon. The traditional approach for drought assessment evolved primarily from the meteorological and hydrological sciences. The introduction of interdisciplinarity and the application of the system approach divides drought into three main types—meteorological, blue-water, and green-water drought [12]. The green-water drought is expressed by reduced water storage in soil and vegetation. Green-water drought causes variable effects across landscape components, on their functioning and the ES they provide [12]. Blue-water, or irrigation water, is only required when the green-water store in the soil runs short [13]. Understanding drought-impact pathways across blue- and green-water fluxes and geospheres is crucial for ensuring food and water security and for developing early-warning and adaptation systems in support of ecosystems and society [14]. The distinct landscapes and the forest ecosystems [15] that developed on them react in different ways to the changes in the environmental conditions [16]. Application of indices and indicators based mainly on hydro-meteorological data cannot sufficiently achieve effective and accurate drought assessments. Similar disadvantages are also shown by remote sensing methodological approaches, based entirely on spectral vegetation indices [17]. Semi-natural ecosystems in lower territories wither earlier than those in higher areas. The application of a methodology for drought assessment that uses only spectral indices could lead to misinterpretation of this natural process, defining it as drought. In addition, the differences in the withering process and drought sensitivity of the individual land cover classes (LCCs) further superimpose the differences between the individual ecosystems and complicate the assessment of drought. The drought response of plant communities is also influenced by the within-ecosystem variability, predetermined by the differences in landscape-forming components such as the lithological basis, soil type, topography, and landscape hydrology. There is evidence that ecosystems respond more strongly to climate variability with decreasing climatic wetness and biomass [18,19], which suggests that the drought sensitivity would be greater in grasslands than in shrubs and, respectively, greater in shrubs than in forest ecosystems [17]. All these prerequisites require a methodology that takes into account the differences between the ecosystems and unifies the assessment of ecosystems’ drought response throughout a heterogeneous territory. Therefore, the present study introduces coefficients for the normalization of drought assessments by two key landscape-forming factors—elevation and land cover. The elevation is a dominant driver of drought sensitivity. In the high mountain areas, soil is shallow, with lower water retention capacity and consequently with reduced capacity to supply ecosystems with moisture under severe drought conditions. In the foothills and valleys, the soils are deeper, and the water retention capacity is greater. These areas are distinguished by shallow groundwater availability and provide better moisture supply to vegetation. Different LCCs are also characterized by various degrees of drought sensitivity. Studies on the gross primary productivity (GPP) changes of different land cover types under drought stress show, for example, a considerable reduction in the drought-induced GPP in the mid-latitude region of the northern hemisphere (48%), followed by the low-latitude region of the southern hemisphere (13%) [20]. In contrast, a slightly enhanced GPP (10%) was evident in the tropical region under drought impact [20]. Shrubs and grasslands have a lower biomass than forests and, under drought conditions, show stronger drought sensitivity and greater differential response to severe drought than to moderate drought [17,19]. Water-limited ecosystems, distinguished by low GPP, demonstrate a higher correlation between hydro- climate variation and vegetation greenness [18]. They respond with greater productivity decreases under drought stress [21], and show slower recovery after that [20]. The present study introduces an innovative, experimental methodology, which intends to assess the drought impact on semi-natural lands, including forest, shrub and grassland ecosystems. The application of the system approach, which takes into consideration key differences between the ecosystems and their functioning, is included as a necessary basis Forests 2021, 12, x FOR PEER REVIEW 3 of 33

Forests 2021, 12, 974 grassland ecosystems. The application of the system approach, which takes 3into of 32 consideration key differences between the ecosystems and their functioning, is included as a necessary basis for accurate evaluation of the drought impact. The study outlines patterns of temporal, spatial, and interspecific differences in the response of vegetation to drought for accurate evaluation of the drought impact. The study outlines patterns of temporal, spatial,stress andin a interspeciﬁcpart of western differences Bulgaria, in thewhich response is distinguishe of vegetationd by to its drought heterogeneity stress in aand diver- partsity. of These western prerequisites Bulgaria, which make is distinguished the study valuable by its heterogeneity for further and drought diversity.-related These research prerequisitesconcerning makevarious the environments. study valuable for further drought-related research concerning various environments. 2. Materials and Methods 2. Materials and Methods 2.1.2.1. Study Study Area Area TheThe study study area area is located is located in western in western Bulgaria. Bulgaria. It is representative It is representative of the areas of the with areas with differentdifferent vegetation vegetation (forests, (forests, shrubs, shrubs, grass grass and and pasture pasture lands) lands) which which are considerably are considerably in- inﬂuencedfluenced byby drought. drought. The The area area covers covers two two adjacent adjacent Sentinel-2 Sentinel tile scenes,-2 tile encompassingscenes, encompassing 17.2%17.2% of of Bulgarian Bulgarian territory territory (Figure (Figure1). 1).

FigureFigure 1. 1.Study Study area area and and meteorological meteorological stations. stations.

TheThe local local relief relief is diverse, is diverse, including including valleys, valley hillys, areas hilly and areas mountainous and mountainous areas. The areas. The studystudy area area occupies occupies parts parts of the of Forethe Fore Balkan Balkan and Balkan and Balkan Mountain Mounta Range,in VitoshaRange, Moun-Vitosha Moun- tain,tain, Sredna Sredna Gora Gora and smallerand smaller adjacent adjacent mountains. mountains. The mean The elevation mean is elevation 715.5 m a.s.l. is 715.5 The m a.s.l. highest point is 2277.6 m and the lowest is 83.4 m. The highest point is 2277.6 m and the lowest is 83.4 m. The climate is temperate-continental, characterized by relatively warm summers and cold winters.The climate The mean is temperate annual temperatures-continental, vary characterized between 10.3 by◦C relatively in the lower warm areas summers and and 0.3cold◦C win at higherters. The altitudes. mean Theannual annual temperatures precipitation vary sum between is between 10.3 610 °C andin the 1187 lower mm, areas and depending0.3 °C at onhigher the elevation altitudes. and The slope annual exposure precipitation [22]. sum is between 610 and 1187 mm, dependingThe main on soil the types elevation include and Luvisols, slope Cambisols,exposure [22] Leptosols,. Planosols, Colluvisols, and Rendzinas.The main Thesoil valleystypes include are characterized Luvisols, mainlyCambisols, by Fluvisols, Leptosols, whereas Planosols, the high Colluvisols, mountainousand Rendzinas. parts areThe characterized valleys are by characterized Umbrisols. Within mainly the area by Fluvisols of the coniferous, whereas and the high beechmountainous forest spread, parts the are Cambisols characterized are predominant. by Umbrisols In the. Within low-latitude the area mountains of the coniferous and and hilly areas, the soil types are more diverse, due to differences in the lithological mate- beech forest spread, the Cambisols are predominant. In the low-latitude mountains and hilly areas, the soil types are more diverse, due to differences in the lithological material, Forests 2021, 12, 974 4 of 32

rial, the complex interaction between the main geocomponents, and the paleogeographic development [22].

2.1.1. Vegetation Variability The territory is dominated by semi-natural areas (56%). Broadleaved forests cover 48% of the semi-natural areas, pastures and grasslands cover 16.8%, transitional woodlands and shrubs cover 16.6%, mixed forests cover 13.8%, and ﬁnally coniferous forests cover 4.8%. The distribution and species diversity of vegetation is inﬂuenced by the altitude zonation, slope exposure, soil type, and lithological basis. In the Fore Balkan and lower mountains, oak, oak-hornbeam and beech forests (Querqus sp., Quercus dalechampii Ten., Carpinus orientalis Mill., Fagus sylvatica L.) are present. The higher parts of the mountains are occupied by European beech forests (Fagus sylvatica L.), and in the karst areas Mizian beech forests (Fagus sylvatica L. ssp. moesiaca (K. Manly) Hyelmq.) are present. The coniferous belt is fragmented and the main forest formations are dominated by Norway spruce (Picea abies (L.) H. Karst.) and mixed communities (Picea abies (L.) H. Karst. and Fagus sylvatica L. or Abies alba Mill.). The highest parts of the mountains are occupied by mountain-meadow vegetation [23]. The natural vegetation in the valleys and hilly areas is highly fragmented by agrophytocenoses and urban areas.

2.1.2. Climatic Conditions A trend of decreasing precipitation is observed in the southwestern regions of Bulgaria, with the greatest decrease being more than 25% in some mountain parts [24,25]. Long- term observations of climatic parameters, performed in mountain stations situated in the studied area, show a positive trend for the average monthly air temperatures. They are most pronounced in summer, reaching 1.3 ◦C above the norm, set for the reference period 1961–1990 [26,27]. When comparing the periods 1961–1990 and 1989–2018, the most significant fluctuations of climatic elements in the non-mountainous areas are observed in the annual mean maximum and minimum air temperature, followed by the number of ice days (Tmax < 0 ◦C), the number of tropical nights (T > 20 ◦C), mean number of days per year with snow cover, and mean number of days per year with precipitation above 1 mm [28]. The data in Table1 show the change in selected climatic elements and, respectively, the coefficient of climatic change (CCC), calculated on the basis of data acquired in two meteorological stations—Lovech, located on the border between the Danube plain and Central Fore Balkan (Northern Bulgaria) and Sofia, located in the Sofia valley (Southern Bulgaria) [28] (Figure1). The CCC is determined depending on the percentage increase or decrease in the values of the analyzed climatic element in the period 1989–2018, compared to the norm of the reference period (1961–1990). The change in a given climatic element is insignificant when the CCC is 0–3, slight when it is 4–5, moderate when it is 6–7), and significant when CCC ≥ 8 [28].

Table 1. CCC of selected climatic elements between the periods 1961–1990 and 1989–2018.

Climatic Element Change in Lovech (%) CCC Lovech Change in Soﬁa (%) CCC Soﬁa Annual mean maximum air temperature 8.4 10 7.9 9 Annual mean minimum air temperature 16.4 5 17.6 5 Number of ice days −15.9 5 −13.6 5 Number of tropical nights 200 4 220 5 Mean number of days per year with −6 1 11 2 snow cover Mean number of days per year with 3 1 −3.3 1 precipitation above 1 mm Average change 4.3 4.5 Note: According to Matev (2020) [28]. Forests 2021, 12, 974 5 of 32

The long-year chronological course of the mean annual maximum air temperatures for 1961–2018 shows large amplitudes, especially in the years after 1987, as well as a well- defined positive linear trend. The increase in the mean annual maximum temperatures is about 0.4 ◦C per decade, and the peak is already commonly observed at the beginning of August, not at the end of July. There is also a time shift of monthly minimum precipitation that is already commonly observed in August, not in October (meteorological station Sofia) [28]. The described changes in climatic elements create conditions for severe drought stress in ecosystems in August. The change in the number of days per year with precipitation above 1.0 mm in the non-mountainous part of the country is 40% in winter, 35% in summer, and 19% in spring. However, geographically, there is an increase in the rate of decrease in precipitation from north to south and from north-east to south-west. This is an indication primarily of larger- scale changes in the atmospheric circulation [28]. The influence of local orographic features is of secondary importance. Climate change increases as the altitude increases and is most significant in the temperate-continental climate zone [28]. These findings indicate considerable changes in the climatic conditions in the study area (Figure1).

2.2. Data 2.2.1. Satellite Data For the purposes of the present study, all available cloud-free Sentinel-2 satellite images (level 1-C product) or images with a small percentage of cloud cover for the summer periods of 2019 and 2020 were used. Removal of cloud pixels was performed on the latter. The level 1-C product is composed of 100 × 100 km2 tiles, on which per-pixel radiometric measurements in top of atmosphere (TOA) reﬂectance by the European Space Agency (ESA) are performed [29]. The study area is covered by two adjacent tile images (T34TFN and T34TGN). The satellite images are representative for August and September (Table2). There are two cloud-free satellite images, covering the entire study area for the end of August/beginning of September, acquired in 2019 and 2020. Different spectral band combinations were used for the calculation of spectral indices. The study also involves orthogonalization of multispectral satellite data that includes all 13 spectral bands of the Sentinel-2 satellite images. The Sentinel-2 satellite images are freely accessible through the Copernicus Open Access Hub of the European Space Agency (ESA) [30].

Table 2. Acquisition dates of Sentinel-2 satellite images and characteristics of spectral bands used in the study [31].

Acquisition Dates Band Spectral Region Central Wavelength (µm) Ground Sampling Distance (m) 7 August 2019 B1 0.443 60 12 August 2019 B2 Blue 0.49 10 31 August 2020 B3 Green peak 0.56 10 B4 Red 0.665 10 1 September 2019 B5 Red edge 0.705 20 16 September 2019 B6 Red edge 0.74 20 B7 Red edge 0.783 20 B8 NIR 0.842 10 B8A NIR narrow 0.865 20 B9 0.945 60 B10 1.375 60 B11 SWIR 1.61 20 B12 SWIR 2.19 20

The acquisition dates of the satellite images were chosen in August and Septem- ber, when the vegetation in the studied area is in a vegetative maturity and the climatic conditions are traditionally distinguished by drought with different intensity. The air temperatures in August are the highest in the year. The precipitation sums are the lowest and most unevenly distributed in time. In the last few years, the depicted climatic conditions have lasted longer, encompassing a large part of September. The satellite images are Forests 2021, 12, 974 6 of 32

representative for early August (on 7 August 2019), the middle of August (on 12 August 2019), the end of August/beginning of September (on 31 August 2020 and on 01 September 2019), and for the middle of September (on 16 September 2019) (Table2). There are two cloud-free satellite images, covering the entire study area for the end of August/beginning of September, acquired in 2019 and 2020. For that reason, a comparison of the climatic conditions and their impact on the semi-natural vegetation in August 2019 and 2020 was performed. The images from the end of August/ beginning of September were used for an assessment of the effect of the different weather pattern on the DC dynamics in both years. Generally, this assessment shows the relation of the DC to the actual drought effect.

2.2.2. Reference Data Corine Land Cover (CLC) data, released by the Copernicus Land Monitoring Service, referring to land cover/land use status of 2018, was used in the present study. CLC is based on classiﬁcation of satellite images and is characterized by minimum mapping unit of 25 ha and minimum width of linear elements of 100 m [32]. A digital elevation model (DEM) raster layer with a geographic accuracy of 25 m was also used. The DEM layer is available through Copernicus Land Monitoring Service [33].

2.2.3. Data, Used in the Validation Process For the purposes of the validation, data derived from Landsat-8 (OLI) and PROBA-V sensors were used. The PROBA-V-based datasets include fraction of vegetation cover (FCover), Leaf Area Index (LAI), and gross dry matter productivity (GDMP). The datasets are freely available through Copernicus Land Monitoring Service [34]. The Landsat-8 (OLI) data were available through USGS Earth Explorer [35].

2.2.4. Data Processing Data processing included several main steps, such as resampling; creation of com- posite layers; raster mosaicking; cropping to the range of each LCC and according to the elevation; assignment of weight coefficients and calculation of weighted sums; reclassifi- cation; satellite and in situ spectral profile generation; and creation of maps, showing the final results. Data processing steps were performed by using Erdas Imagine 2014, Version 14, Madison, AL, USA; Quantum GIS, Version 3.16, Beaverton, OR, USA software, and Ocean- View 2.0, Version 2.0.8., Largo, FL, USA firmware. Statistical analyses were performed on SigmaPlot, Version 14.5, San Jose, CA, USA software.

2.3. Integrated Steps for Assessment of Green-Water Drought Impact 2.3.1. Spectral Vegetation Indices Used in the Assessment of Green-Water Drought Impact The proposed methodology integrates four main steps, presented in detail in Schemes 1–4. The first step includes calculation of indices, used for assessing variations in moisture content, disturbance of ecosystems and drought severity. After calculation, each of the indices is assigned a weight coefficient, pre-defined according to the significance of the process and/or phenomenon it represents. The Normalized Multi-band Drought Index (NMDI) [36] is an index especially developed for monitoring soil and vegetation moisture with satellite remote sensing data. NMDI uses a combination between the NIR (0.86 µm) spectral band and the sensitive to vegetation and soil moisture content short-wave infrared part of the electromagnetic spectrum (0.164 nm and 0.213 µm) [36]. NMDI increases the accuracy of drought severity assessments and is particularly suitable for drought monitoring of an entire ecosystem, affected by both soil and vegetation drought. The lower NMDI values correlate with higher drought severity when monitoring areas with dense vegetation (Leaf Area Index > 2) [36]. In these cases, the reciprocal value of the index in every individual pixel is used. NMDI Forests 2021, 12, x FOR PEER REVIEW 7 of 33

Forests 2021, 12, 974 7 of 32

In these cases, the reciprocal value of the index in every individual pixel is used. NMDI is considered as the most significant indicator for the presence of green-water drought in isecosystems considered, and as theit was most assigned significant the highest indicator weight for the coefficient presence (45%). of green-water drought in ecosystems,The Disturbance and it was Index assigned (DI) the[37] highest is based weight on a linear coefficient orthogonal (45%). transformation (tas- seledThe cap Disturbancetransformatio Indexn—TCT) (DI) in [37 multidimensional] is based on a linear space, orthogonal which aims transformation to reduce the (tas-cor- seledrelation cap between transformation—TCT) its individual elements. in multidimensional TCT converts the space, original which strongly aims correlated to reduce data the correlationinto three uncorrelated between its individualindices (brightness elements.—BR, TCT greenness converts— theGR, original and wetness strongly— correlatedW), repre- datasenting into the three soil, uncorrelatedvegetation, and indices water (brightness—BR, components of ecosystems greenness—GR,, and increas and wetness—W),es the degree representingof their identification the soil, in vegetation, land cover and classifications water components [38]. The ofunitary ecosystems, matrix of and TCT increases is fixedthe for degreeeach sensor of their [39] identification. In the present in study, land cover a unitary classifications matrix, determined [38]. The unitaryfor the orthogonaliza- matrix of TCT istion fixed of images for each particularly sensor [39 from]. In the the Sentinel present-2 study, sensor a [40] unitary, was used. matrix, DI determinedis a linear combina- for the orthogonalizationtion of BR, GR and of W images components particularly of TCT from [37] the. Considered Sentinel-2 sequentially, sensor [40], was the used.index DIvalues is a linearprovide combination a direct method of BR, for GR quantification and W components of the ofdeviations TCT [37]. in Considered the ecosystems, sequentially, caused theby indexdestructive values impacts provide and a direct processes method [38] for. Such quantification a destructive of the process deviations could in be the a ecosystems, prolonged causeddrought. by DI destructive was assigned impacts a weight and coefficient processes of [38 35%.]. Such a destructive process could be a prolongedSimilar drought. to NMDI, DI the was Moisture assigned Stress a weight Index coefficient (MSI) [41] of incorporates 35%. the NIR and one of theSimilar short-wave to NMDI, infrared the Moisturespectral bands Stress and Index is (MSI)widely [41 applicable] incorporates in analysis the NIR of and vegeta- one oftion the canopy short-wave moisture infrared stress. spectral MSI is bands expected and to is be widely highly applicable effective in in analysis assessments of vegetation of mois- canopyture stress moisture in plants stress. with MSI tolerance is expected of low to leaf be highly water effectivecontent by in assessmentscellular adjustment of moisture that stresscannot in be plants detected with using tolerance NIR/R of lowvegetation leaf water indices content [42] by. I cellularn addition, adjustment MSI also that correlates cannot bewith detected soil moisture using NIR/R [43] that vegetation influences indices the overall [42]. In vegetation addition, MSIcondition also correlates in terms withof mois- soil moistureture content [43 ]and that stress. influences These the features overall of vegetation the index are condition advantages in terms in green of moisture-water drought content and stress. These features of the index are advantages in green-water drought assessments. assessments. MSI was assigned a weight coefficient of 20%. MSI was assigned a weight coefficient of 20%. Step 1 of the methodology ends with an intermediate assessment of the green-water Step 1 of the methodology ends with an intermediate assessment of the green-water drought severity (output raster 1) and separation of the studied area into two categories, drought severity (output raster 1) and separation of the studied area into two categories, based on the elevation difference (Scheme 1). based on the elevation difference (Scheme1).

Scheme 1. Step 1 of applied methodology: calculation of spectral vegetation indices - Normal- izedScheme Multi-band 1. Step 1 Droughtof applied Index methodology (NMDI) and: calculation Moisture of Stress spectral Index vegetation (MSI); performanceindices - Normalized of a lin- Multi-band Drought Index (NMDI) and Moisture Stress Index (MSI); performance of a linear or- ear orthogonal transformation (tasseled cap transformation—TCT), generation of brightness—BR, thogonal transformation (tasseled cap transformation—TCT), generation of brightness—BR, green- greenness—GR, and wetness—W indices, and calculation of Disturbance Index (DI); assignment of ness—GR, and wetness—W indices, and calculation of Disturbance Index (DI); assignment of weight weight coefﬁcients; intermediate assessment of drought severity; and separation of the studied area into two categories, based on the elevation difference. Forests 2021, 12, x FOR PEER REVIEW 8 of 33

coefficients; intermediate assessment of drought severity; and separation of the studied area into two categories, based on the elevation difference.

2.3.2. Defining Coefficients for Drought Resilience Forests 2021, 12, 974 The proposed methodology takes into account differences in semi8 of-natural 32 ecosystems, pre-determined by elevation and land cover type. Aiming to achieve a higher degree of comparability in assessing green-water drought throughout the studied area, these two factors2.3.2. Defining were assigned Coefficients coefficients for Drought for Resilience normalization. This process is depicted in the meth- odologyThe proposedflow chart methodology as Step 2 takesand intoStep account 3. differences in semi-natural ecosystems, pre-determined by elevation and land cover type. Aiming to achieve a higher degree The output raster, generated after the intermediate assessment, was separated into of comparability in assessing green-water drought throughout the studied area, these twotwo rasters, factors were represen assignedting coefficients the weighted for normalization. semi-natural This areas process located is depicted above in the900 m a.s.l. and themethodology lower located flow chart semi as-natural Step 2 and areas. Step 3.The 900 m isoline was selected as a conditional di- vidingThe line output between raster, generatedthe temperate after the and intermediate cold humid assessment, landscapes was separatedof the higher into mountains ontwo the rasters, one representinghand, and thethe weighted warmer semi-natural and drier areaslandscapes located above in the 900 lower m a.s.l. mountains and the and hilly lower located semi-natural areas. The 900 m isoline was selected as a conditional dividing areasline between on the theother temperate. Depending and cold on humid the landscapesexposure ofof thethe higher slope, mountains this isoline on the can one be considered ashand, the andlower the limit warmer of anddistribution drier landscapes of the in beech the lower forests mountains in the andmountains hilly areas of on western the Bulgaria [44]other.. Depending on the exposure of the slope, this isoline can be considered as the lower limitThe of distribution area separated of the beech in this forests way in was the mountains further divided of western depending Bulgaria [44 on]. the differences in the LCCs,The area pre separated-determined in this wayby their was further drought divided resilience depending. This on forms the differences the intermediate in the assess- LCCs, pre-determined by their drought resilience. This forms the intermediate assessment mentfor each for LCC each in LCC the territories in the territories below 900 mbelow and above 900 m 900 and m (Scheme above2 900). m (Scheme 2).

SchemeScheme 2.2.Step Step 2 of2 of applied applied methodology: methodology division: division of the separated of the separated by elevation by study elevation area into study area into semisemi-natural-natural Corine Corine Land Land Cover Cover (CLC) (CLC) classes; classes; differentiation differentiation of average of intermediate average intermediate assessment assessment valuesvalues (average(average drought drought severity severity values) values) of each of CLC each class CLC below class and below above 900and m. above 900 m. After the intermediate assessment of the each individual LCC, the average value of droughtAfter severity the intermediate was used as a basis assessment for the differentiation of the each of itsindividual drought resilience. LCC, the Satellite average value of droughtimages from severity early Junewas and used early as Julya basis were for used the for differentiation the territories below of its and,drought respectively, resilience. Satellite imagesabove 900 from m a.s.l. early in June this process. and early The July rationale were behind used thisfor the approach territories are the bel differencesow and, respectively, in seasonal timing and duration of species life-cycles in the lower lands and higher in above 900 m a.s.l. in this process. The rationale behind this approach are the differences in the mountains. In applying the methodology, the referent basic LCCs were selected for seasonalareas below timing and above and 900 duration m. The referent of species basis life LCCs-cycles are those in the with lower the highest lands average and higher in the mountains.value of drought In applying severity. the According methodology to these, referentthe referen classes,t basic the coefficientsLCCs were for selected the for areas belowdifferentiation and above of drought 900 m. resilience The referent of the other basis LCCs LCCs were are set. those The coefficientwith the ofhighest drought average value ofresilience drought of severity. each individual According LCC represents to these thereferent difference classes between, the thecoefficients average value for the of differentia- drought severity of the basic LCC and the average value of drought severity of each LCC tion of drought resilience of the other LCCs were set. The coefficient of drought resilience of for the areas below and above 900 m. The coefficients of drought resilience are used for eachthe normalization individual LCC of drought represents stress the assessment difference by landbetween cover the type. average The coefficients value of for drought sever- itynormalization of the basic by LCC elevation and werethe average calculated value as differences of drought between severity the average of each values LCC of for the areas belowdrought and severity above of 900 each m. CLC The class coefficients below and of above drought 900 m resilience (Scheme3). are used for the normalization of drought stress assessment by land cover type. The coefficients for normalization by Forests 2021, 12, x FOR PEER REVIEW 9 of 33

Forests 2021, 12, 974 elevation were calculated as differences between the average values of drought severity of 9 of 32 each CLC class below and above 900 m (Scheme 3).

Scheme 3. Step 3 of applied methodology: selection of referent basis Corine Land Cover (CLC) Scheme 3. Step 3 of applied methodology: selection of referent basis Corine Land Cover (CLC) class class for the areas below and above 900 m; defining coefficients for drought resilience for the areas for the areas below and above 900 m; defining coefficients for drought resilience for the areas below andbelow above and900 m above used 900for normalization m used for normalization of the drought of stress the drought assessment stress by assessment land cover type; by land defin- cover type; ing definingcoefficients coefficients for normalization for normalization by elevation. by elevation.

2.3.3.2.3.3. Evaluation Evaluation of Vegetation of Vegetation Drought Drought Stress Stress TheThe final final assessment assessment unites unites the intermediate the intermediate assessment assessment of drought of severity, drought the severity, co- the efficientscoefficients for drought for drought resilience resilience of the LCCs, of the LCCs,and the and coefficients the coefficients for elevation for elevation (Scheme (Scheme 4). 4). TheThe obtained obtained drought drought coefficients coefficients (DC) (DC)represent represent a unified a unified expression expression of drought of drought stress, stress, Forests 2021, 12, x FOR PEER REVIEW 10 of 33 takingtaking into into consideration consideration physically physically predetermined predetermined environmental environmental conditions conditions arising arising from fromdifferences differences in in land land cover cover type typeand and elevation.elevation.

SchemeScheme 4. Step 4 4 of of applied applied methodology methodology:: calculation calculation of the of the final final assessment assessment (Drought (Drought coefficient coefficient—— DC)DC) integratingintegrating thethe intermediateintermediate assessment of drought severity, the coefficients coefficients for drought re resiliencesili- ence of the land cover classes (LCCs) for the areas below and above 900 m, and the coefficients for of the land cover classes (LCCs) for the areas below and above 900 m, and the coefficients for elevation; elevation; validation of the results. validation of the results. 2.3.4. Methods for Validation of the Results The validation process includes several main steps and involves an application of linear regression analyses. In the first step, we used a thematically oriented multichannel spectrometer (TOMS) [45] based on USB2000 and NEARQUEST512 of OceanOptics Inc. for field measurements of spectral reflectance. The measurements were performed in randomly located ecosystems, belonging to the individual LCCs differently affected by drought stress. Aiming for the validation of DC in each point location (point-based validation) (Scheme 4), the spectral channels of the spectrometer were adjusted to those of Sentinel-2. After the point-based validation, spectral profiles were generated and analyzed through the calculation of spectral indices representing the unique environmental conditions in the randomly identified ecosystems. The selected spectral indices are broadly applied in remote sensing for the assessment of vegetation condition and its dynamics, affected by moisture stress and drought [36,46–51]. The accuracy of the spectral indices was evaluated on the basis of the field measurements. The spectral indices with the highest accuracy were used for validation, based on randomly selected test areas (area-based validation). In addition, in the area-based validation, datasets from independent sources were used (Landsat-8 and PROBA-V), including environmental indicators related to drought process (Scheme 4). The selected indicators are fraction of vegetation cover (FCover), Leaf Area Index (LAI), and gross dry matter productivity (GDMP). The datasets are distinguished by different spatial resolutions. For the purposes of the statistical analyses, the datasets were resampled, according to the resolution of the variables, used in the analysis. Generally, the datasets with higher resolution were resampled to lower resolution (Table 3).

Forests 2021, 12, 974 10 of 32

2.3.4. Methods for Validation of the Results The validation process includes several main steps and involves an application of linear regression analyses. In the first step, we used a thematically oriented multichannel spectrometer (TOMS) [45] based on USB2000 and NEARQUEST512 of OceanOptics Inc. for field measurements of spectral reflectance. The measurements were performed in randomly located ecosystems, belonging to the individual LCCs differently affected by drought stress. Aiming for the validation of DC in each point location (point-based validation) (Scheme4), the spectral channels of the spectrometer were adjusted to those of Sentinel-2. After the point-based validation, spectral profiles were generated and analyzed through the calculation of spectral indices representing the unique environmental conditions in the randomly identified ecosystems. The selected spectral indices are broadly applied in remote sensing for the assessment of vegetation condition and its dynamics, affected by moisture stress and drought [36,46–51]. The accuracy of the spectral indices was evaluated on the basis of the field measurements. The spectral indices with the highest accuracy were used for validation, based on randomly selected test areas (area-based validation). In addition, in the area-based validation, datasets from independent sources were used (Landsat-8 and PROBA-V), including environmental indicators related to drought process (Scheme4). The selected indicators are fraction of vegetation cover (FCover), Leaf Area Index (LAI), and gross dry matter productivity (GDMP). The datasets are distinguished by different spatial resolutions. For the purposes of the statistical analyses, the datasets were resampled, according to the resolution of the variables, used in the analysis. Generally, the datasets with higher resolution were resampled to lower resolution (Table3).

Table 3. Characteristics of the variables used in the ﬁnal step of the validation process.

Variable Sensor Data of Acquisition Spatial Resolution DC Sentinel-2 1 September 2019 100 m PSRI2 Sentinel-2 1 September 2019 10 m NMDI Sentinel-2 1 September 2019 10 m MSI2 Sentinel-2 1 September 2019 10 m NMDI Landsat-8 (OLI) 28 August 2019 30 m MSI2 Landsat-8 (OLI) 28 August 2019 30 m GDMP PROBA-V 31 August 2019 300 m FCover PROBA-V 31 August 2019 1 km LAI PROBA-V 31 August 2019 1 km

Spectral Indices and Environmental Indicators, Used in the Validation Process In addition to the vegetation indices, proposed in the methodology (MSI and NMDI), the Red Edge Normalized Difference Vegetation Index (NDVI705), which is a narrowband greenness vegetation index, the Plant Senescence Reflectance Index (PSRI), which is senescence-related vegetation index, and modifications of some of the listed indices were also calculated in validation process. As a modification of MSI that traditionally uses SWIR1 (1.61 µm) and NIR (0.842 µm) bands of Sentinel-2 sensors in its calculation, the ratio between SWIR2 (2.19 µm) and 8A (0.865 µm) is suggested (MSI2). These bands, similar to those used in calculation of MSI, are sensitive to changes in ecosystem moisture content and related dynamics of plant greenness. Using the ratio of bulk carotenoids to chlorophyll, the PSRI is sensitive to plant senescence. For the calculation of PSRI, the red, the blue, and one of the red edge (centered at 0.740 µm) Sentinel-2 bands are used. The original formula for calculation of the index is as follows [48]: ρ0.680 − ρ0.500 PSRI = (1) ρ0.750 In the present study, for the validation of the obtained results, in addition to PSRI, we introduce a modification, which calculates using two red edge bands and the green peak Forests 2021, 12, 974 11 of 32

band of Sentinel-2. The PSRI2 was shown to be effective in the differentiation of LCCs and in monitoring moisture stress. PSRI2 values increase in dryer conditions, with the reduction in moisture content. The formula of the proposed index is as follows:

ρ0.705 − ρ0.560 PSRI2 = (2) ρ0.783

The modified index proposed has the advantage of the effectiveness of red edge and green peak bands in monitoring the alternations of cellular structures and anthocyanins content induced by water stress. There is a high correlation between reflectance at 0.550 and 0.700 µm during the browning of plant tissues [52]. Spectral changes of browning plant tissues could be associated with heating [53], senescence [54,55], and wounding [56]. As browning progresses, the spectral reflectance in the whole range of the spectrum decreases. The decrease in reflectance is most pronounced in the green peak (0.550 µm) and in the range 0.750–0.800 µm, and is much smaller in the red and the blue regions [52], which are employed in the calculation of the original PSRI. Browning process is induced by a loss of cell compartmentalization and involves oxidation [57,58]. Oxidative stress in tree vegetation caused by environmental stresses is associated with accelerated cellular leaf senescence [59] in both coniferous and winter deciduous plants [60]. Drought stress can be adjusted by an increase in the anthocyanins (water-soluble flavonoids). Anthocyanins are plant pigments that, with their potential to regulate photosynthesis [61], have a pho- toprotective function [62,63], acting as osmotic adjusters and increase plants’ resistance to drought stress or freezing [64]. The narrowband green (0.550 µm) and early red edge (0.700 µm) reflectance have been shown to also be effective in monitoring the dynamics of anthocyanins [65]. In addition to the spectral vegetation indices, we used three environmental indicators, based on the PROBA-V sensor, in the validation process. The fraction of vegetation cover (FCover) refers to the spatial extent of the ground covered by green vegetation. It can be used as an indicator for monitoring vegetation life- cycles and for assessments of vegetation status, which can be strongly affected by drought. FCover is applicable in land surface models for evaluation of water-energy balance, in hydrology, and climate studies [66]. The Leaf Area Index (LAI) is recognized as an essential climate variable (ECV) by the Global Climate Observing System (GCOS) [67]. It represents the thickness of the vegetation cover and corresponds to the total green LAI of all the canopy layers [67]. In the forests, the satellite-based LAI also estimates the canopy of the understory, which significantly contributes to the water retention capacity of the ecosystems and, respectively, to their drought resilience. The gross dry matter productivity (GDMP) represents the increase in the dry biomass of the vegetation. GDMP is directly related to ecosystem net primary productivity (NPP) [68], also dependent on the environmental conditions and particularly on the drought.

3. Results 3.1. Drought Stress In the analysis of the results, the DC is separated conditionally by breakpoints of 2, 4, 6, 8, and 10 (Figures2 and3). This separation was performed for a better visualization of the spatial and temporal dynamics of the drought stress and facilitates the interpretation. The DC is in a testing phase and it is still not validated through ﬁeld measurements of the actual drought impact. For that reason, it should be considered as an index outlining the drought trends through time and space for different landcover classes in western Bulgaria, also considering the elevation factor. The analysis of the results includes a comparison of the climatic conditions and of their impact on the semi-natural vegetation at the end of August/beginning of September. Tentatively accepted, this assessment demonstrates the relation of the DC to the actual drought effect. Forests 2021, 12, x FOR PEER REVIEW 12 of 33

red edge (0.700 μm) reflectance have been shown to also be effective in monitoring the dynamics of anthocyanins [65]. In addition to the spectral vegetation indices, we used three environmental indicators, based on the PROBA-V sensor, in the validation process. The fraction of vegetation cover (FCover) refers to the spatial extent of the ground covered by green vegetation. It can be used as an indicator for monitoring vegetation life- cycles and for assessments of vegetation status, which can be strongly affected by drought. FCover is applicable in land surface models for evaluation of water-energy balance, in hydrology, and climate studies [66]. The Leaf Area Index (LAI) is recognized as an essential climate variable (ECV) by the Global Climate Observing System (GCOS) [67]. It represents the thickness of the vegetation cover and corresponds to the total green LAI of all the canopy layers [67]. In the forests, the satellite-based LAI also estimates the canopy of the understory, which significantly contributes to the water retention capacity of the ecosystems and, respectively, to their drought resilience. The gross dry matter productivity (GDMP) represents the increase in the dry biomass of the vegetation. GDMP is directly related to ecosystem net primary productivity (NPP) [68], also dependent on the environmental conditions and particularly on the drought.

3. Results 3.1. Drought Stress In the analysis of the results, the DC is separated conditionally by breakpoints of 2, 4, 6, 8, and 10 (Figures 2 and 3). This separation was performed for a better visualization of the spatial and temporal dynamics of the drought stress and facilitates the interpretation. The DC is in a testing phase and it is still not validated through field measurements of the actual drought impact. For that reason, it should be considered as an index outlining the drought trends through time and space for different landcover classes in western Bul- garia, also considering the elevation factor. The analysis of the results includes a compar- Forests 2021, 12, 974 ison of the climatic conditions and of their impact on the semi-natural vegetation 12at of the 32 end of August/beginning of September. Tentatively accepted, this assessment demonstrates the relation of the DC to the actual drought effect.

Forests 2021, 12, x FOR PEER REVIEW 13 of 33

Figure 2. Distribution of DC in the beginning of August.August.

FigureFigure 3. Distribution 3. Distribution of DC of DC in the in the middle middle of September. of September.

3.1.1.3.1.1. Drought Drought Stress Stress for for the the Whole Whole Studied Studied Area Area DCsDCs were were determined determined for for the the studied studied area area and and presented presented as averages as averages for thefor periodthe period of observationof observation (from (from the the beginning beginning of Augustof August 2019 2019 to the to the middle middle of September of September 2019) 2019) in in fourfour different different time time moments moments (early (early August, August, mid-August, mid-August, end end ofof August/beginning of of Sep- September,tember, mid mid-September).-September). TheThe resultsresults show show that that in in the the entire entire period, period, lands lands characterized characterized withwith a relatively a relatively low low to moderate to moderate degree degree of drought of drought stress stress (DC (DC < 6), < prevail6), prevail and and represent represent 57.2% of the area (Table4). The area of land identiﬁed as unaffected and weakly affected 57.2% of the area (Table 4). The area of land identified as unaffected and weakly affected by drought was 32.8% of the total area. The areas with a higher degree of drought stress by drought was 32.8% of the total area. The areas with a higher degree of drought stress increased smoothly. The lands with DC ranging between 4 and 6 represented 24.3% of the increased smoothly. The lands with DC ranging between 4 and 6 represented 24.3% of the area, while those with DC 6–8 represented 19.9%, those with DC 8–10 represented 7.6%, area, while those with DC 6–8 represented 19.9%, those with DC 8–10 represented 7.6%, and those with the highest degree of drought stress (DC > 10) had the lowest distribution and those with the highest degree of drought stress (DC > 10) had the lowest distribution (7.6%). The areas categorized as having the highest degree of drought stress, for the studied (7.6%). The areas categorized as having the highest degree of drought stress, for the stud- period in 2019, had a minimum distribution of −7.6%. The area with broadleaved forests in ied period in 2019, had a minimum distribution of −7.6%. The area with broadleaved for- this drought class (DC > 10) was greater, at −3.6%, due to the highest share of the territory (48%ests of in the this semi-natural drought class territories) (DC > 10) being was coveredgreater, at with −3.6%, them. due It to should the highest be mentioned share of the thatterritory in 2019, (48% the peak of the in semi the air-natural temperature territories) was being recorded covered in the with period them. 8–10 It Augustshould be and men- rangedtioned between that in 36.6 2019,◦C the in thepeak lower in the territories air temperature and 24.2 ◦wasC in re thecorded high mountainsin the period [69 ].8– The10 Au- smallestgust and area range withd DC between > 10 is 36.6 that °C which in the is lower covered territories with coniferous and 24.2 °C forests. in the Thehigh share mountains of coniferous[69]. The forests smallest is only area 4.8% with of DC the > studied10 is that area which and is they covered are spread with mainlyconiferous throughout forests. The theshare high altitudes,of coniferous with forests less inﬂuence is only from4.8% the of droughtthe studied events. area and they are spread mainly throughout the high altitudes, with less influence from the drought events.

Table 4. Distribution of the drought coefficient (DC) classes within the land cover classes (LCCs) in the studied area.

LCCs in % of Studied Area Average Area for the Representative DC Classes 231/321 * 324 * 311 * 312 * 313 * DC Class for the Studied Period in 2019, in % LCCs in % of studied area 16.8 16.6 48.0 4.8 13.8 <2 1.4 1.4 4.1 0.4 1.2 8.5 2–4 4.1 4.0 11.7 1.2 3.4 24.3 4–6 4.1 4.0 11.7 1.2 3.4 24.3 6–8 3.3 3.3 9.5 1.0 2.7 19.9 8–10 2.6 2.6 7.4 0.7 2.1 15.5 >10 1.3 1.3 3.6 0.4 1.0 7.6 * 231/321—pastures and grasslands; 324—transitional woodlands and shrubs; 311—broadleaved forests; 312—coniferous forests; 313—mixed forests.

Even in early August, there are differences in the response of vegetation to green- water drought between the individual LCCs. The share of the areas with DC < 4 prevails in forest ecosystems. In the broadleaved forests it was the largest (80.2%), in the coniferous Forests 2021, 12, 974 13 of 32

Table 4. Distribution of the drought coefﬁcient (DC) classes within the land cover classes (LCCs) in the studied area.

LCCs in % of Studied Area Average Area for the Representative DC Class for DC Classes 231/321 * 324 * 311 * 312 * 313 * the Studied Period in 2019, in % LCCs in % of studied area 16.8 16.6 48.0 4.8 13.8 <2 1.4 1.4 4.1 0.4 1.2 8.5 2–4 4.1 4.0 11.7 1.2 3.4 24.3 4–6 4.1 4.0 11.7 1.2 3.4 24.3 6–8 3.3 3.3 9.5 1.0 2.7 19.9 8–10 2.6 2.6 7.4 0.7 2.1 15.5 >10 1.3 1.3 3.6 0.4 1.0 7.6 * 231/321—pastures and grasslands; 324—transitional woodlands and shrubs; 311—broadleaved forests; 312—coniferous forests; 313— mixed forests.

Even in early August, there are differences in the response of vegetation to green-water drought between the individual LCCs. The share of the areas with DC < 4 prevails in forest Forests 2021, 12, x FOR PEER REVIEW 14 of 33 ecosystems. In the broadleaved forests it was the largest (80.2%), in the coniferous forests it was 75.3%, and in the mixed forest it was 74.9%. In the woodland/shrub ecosystems, the share of the areas with DC < 4 was 57.6%, whereas in the grasslands, even at the beginning of August,forests it the was areas 75.3%, with and in a comparativelythe mixed forest it low was degree74.9%. In of the green-water woodland/shrub drought ecosys- stress were tems, the share of the areas with DC < 4 was 57.6%, whereas in the grasslands, even at the 40.1%.beginning Within of the August, area coveredthe areas withwith a broadleaved comparatively forest low degree ecosystems, of green- forestswater drought with the highest degreestress of were drought 40.1%. stress Within (DC the area > 10) covered cannot with be broad observed.leaved forest In the ecosystems other forest, forests ecosystems, the territorieswith the highest with degree DC > of 10 drought covered stress barely (DC > 0.1%10) cannot of land be observed in the. mixed In the other forests forest and 0.4% in the coniferousecosystems, the forests. territories The with share DC of > 10 the covered most affectedbarely 0.1% woodland/shrubs of land in the mixed ecosystemsforests was similarand to 0.4% that in ofthe the coniferous coniferous forests. forests, The share whereas of the most in the affected grasslands, woodland/shrubs the share eco- of these areas systems was similar to that of the coniferous forests, whereas in the grasslands, the share was the largest—2.3% (Figure4a–e). of these areas was the largest—2.3% (Figure 4a–e).

(a) (b)

Figure 4. Cont. Forests 2021, 12, 974 14 of 32 Forests 2021, 12, x FOR PEER REVIEW 15 of 33

(e) Figure 4. Distributions of Drought coefficient (DC) for the individual land cover classes (LCC) at the studied dates; (a) Figure 4. Distributions of Drought coefficient (DC) for the individual land cover classes (LCC) at the studied dates; pastures and natural grasslands; (b) transitional woodlands/shrubs; (c) broadleaved forests; (d) coniferous forests; (e) (a) pasturesmixed and forests natural. grasslands; (b) transitional woodlands/shrubs; (c) broadleaved forests; (d) coniferous forests; (e) mixed forests. 3.1.2. Drought Stress Influence on Pastures and Natural Grasslands 3.1.2. DroughtOur results Stress show Influence clear differences on Pastures in vegetation and Natural response Grasslands to green-water drought stress between the individual LCCs. The areas of the pastures and grasslands identified Our results show clear differences in vegetation response to green-water drought as not being affected by drought (DC < 2) were similar for all studied dates, in the period stressfrom between the beginning the individual of August LCCs.to the middle The areas of September, of the pastures and vary and from grasslands 7.7% to 9.6% identified as not(Figure being 4a). affected The percentage by drought of areas (DC identified < 2) were as similar weakly for and all moderately studied dates, affected in by the period fromdrought the beginning stress (DC of from August 2 to 6) to exceeded the middle 20% for of August September, 2019 and and 2020. vary In fromSeptembe 7.7%r to 9.6% (Figure(from4a). 1stThe to 16th percentage September 2019), of areas the tendency identified smoothly as weakly changed and towards moderately an increase affected in by droughtthe areas stress with (DC a higher from degree 2 to 6)of exceededdrought stress 20% (DC for > 6). August The highest 2019 drought and 2020. stress In (DC September (from> 10) 1st for to 14.3% 16th Septemberof the area of 2019),the pastures the tendency and grasslands smoothly was observed changed in the towards middle an of increase September 2019, while at the beginning of August, the area of this LCC was less affected in the(Figure areas 4a). with The a smooth higher transition degree ofin droughtthe response stress of the (DC pastures > 6). and The grasslands highest droughtto the stress (DCdrought > 10) for could 14.3% be hypothetically of the area of accepted the pastures as a result and of grasslands the functional was group observed of the ingrass the middle of Septemberand/or interactions 2019, while among at thefunctional beginning groups of change August, with the their area biomass of this LCCshare. wasNo uni- less affected (Figureform4a). drought The smoothstress response transition of permanent in the responsegrassland was of thefound pastures [70]. In accordance and grasslands with to the droughtother could studies be, water/drought hypothetically stress accepted in grass asland a result ecosystems of the correlates functional with group biomass ofthe grass and/or[71,72] interactions. among functional groups change with their biomass share. No uniform drought3.1.3. stressDrought response Stress Influence of permanent on Transitional grassland Woodlands was found/Shrubs [70 ]. In accordance with other studies, water/drought stress in grassland ecosystems correlates with biomass [71,72]. The response of the transitional woodlands/shrubs to the drought stress differs in comparison to those of the pastures and natural grassland. The prevailing part of the area 3.1.3. Drought Stress Influence on Transitional Woodlands/Shrubs with this LCC (from 38.0% to 50%) belongs to DC 2–4, defined as being weakly affected byThe drought, response with ofa slight the transitional decrease during woodlands/shrubs the studied time (Figure to the 4b). drought Contrary stress to this, differs in comparisonthe results to show those that of the territories pastures unaffected and natural by grassland.drought (DC The < 2) prevailing increase with part time of the area withcloser this LCCto the (fromair temperature 38.0% to maximum 50%) belongs and continue to DC even 2–4, after defined that time as (Figure being weakly4b). The affected by drought,increase in with these a areas slight was decrease evenly distributed during the throughout studied the time territory, (Figure with4b). a sli Contraryght pre- to this, dominance in the high parts of the Kraishte. Similar recovery was also observed in the the resultsgrasslands show ecosystems, that the but territories is influenced unaffected by the elevation by drought factor and (DC is most < 2) distinguisha- increase with time closerble toin the highest air temperature parts of the Balkan maximum mountain and range. continue The share even of the after area that of transitional time (Figure 4b). The increasewoodlands/shrubs in these identified areas was as moderately evenly distributed affected by throughout drought (DC the4–6) territory,varies between with a slight predominance24% and 32% in (Figure the high 4b). parts The higher of the degree Kraishte. of drought Similar stress recovery (DC 6– was8) influences also observed less in the grasslandsthan 13% ecosystems, of the area, butwhile is influencedthe strongest by drought the elevation stress (DC factor > 10) and affects is mostless than distinguishable 3% in the(Figure highest 4b). Despite parts of the the fact Balkan that the mountainshare of the ecosystems range. The with share moderate of the drought area of stress transitional (DC 4–6) in woodlands/shrubs was slightly larger than those in the grasslands, the woodlands/shrubs identified as moderately affected by drought (DC 4–6) varies between 24% and 32% (Figure4b). The higher degree of drought stress (DC 6–8) influences less than 13% of the area, while the strongest drought stress (DC > 10) affects less than 3% (Figure4b). Despite the fact that the share of the ecosystems with moderate drought stress (DC 4–6) in woodlands/shrubs was slightly larger than those in the grasslands, the opposite trend was observed, concerning the areas affected by a higher degree of drought stress (DC > 6). In the last two LCCs (pastures and natural grasslands; transitional woodlands/shrubs), a significant increase in the ecosystems more severely affected by Forests 2021, 12, 974 15 of 32

drought (DC > 6) was noticed. In the middle of September, the share of the grassland area with DC > 6 represented up to 56.4%, while that of the woodlands/shrubs was 22.1% (Figure4a,b). The maximum established areas with DC 8–10 (6.4%) were more pronounced in the mountainous territories and those with DC > 10 (2.7%) were found in the lower parts of the Fore Balkan and the valleys in southern Bulgaria, adjacent to the agricultural lands. Currently, there is a consensus that a strong positive correlation exists between precipitation and above-ground net primary productivity (ANPP). This is due to photosynthesis, which is the main mechanism for plant growth and productivity [73,74]. It was found that the ANPP of xeric grasslands would exhibit greater sensitivity to drought, compared with mesic or hydric ecosystems [75]. Due to the sparse and low vegetation cover, the transitional woodlands/shrubs reﬂect a larger amount of incoming radiation than the grasslands with a lower albedo [76]. Additionally, the differences in the biology between grasses and shrubs, such as their periods of growth, root distributions (shallow-rooted grasses vs. deep-rooted shrubs), and canopy structure (dense grasses, and open shrub canopies), cause their response to drought to differ [77].

3.1.4. Drought Stress Influence on Broadleaved Forests The results for drought stress influence on broadleaved forests are presented in Figure4c . The results show that the largest (82.0%) area covered by this LCC was identified as unaffected or slightly affected by drought (DC < 4). These areas were evenly distributed throughout the northern part and in the high mountains, predominantly in the southern part of the study area. Most noticeable were the changes in the Sashtinska sredna gora, dominated by the European beech forests [23], developed on Cambisols [22]. The area slightly affected by drought (with DC 2–4) decreased with the time of observation and sharply passed to the next category of drought (DC 4–6), which represented 15.3%. These mixed broadleaved ecosystems are mainly spread on the karst areas in the western Balkan mountain range (form. Querceto-Carpineta betuli, form. Fageta moesiacae, mixed Quercus dalechampii Ten. and Carpinus orientalis Mill. forests), Vitosha (form. Fageta moesiacae, form. Betuleta pendulae), Plana Mountain (form. Querceta dalechampii, form. Carpineta orientalis, form. Acereta monospessulanii), and in the karst areas of the Kraiste (form. Querceta dalechampii, form. Querceto-Carpineta betuli and forests dominated by Quercus cerris L., Carpinus orientalis Mill., and Quercus frainetto Ten.) [23]. The canopy architecture of different deciduous species was considered as a main factor of the canopy foliage temperature acceptance. Based on canopy foliage temperature and sap flow data, it was established that Acer pseudoplatanus L. is the most drought-sensitive species followed by Fagus sylvatica L., Tillia platyphyllos Scop. and Prunus avium L.. It was suggested that two ring-porous species, Fraxinus excelsior L. and Querqus petraea (Matt.) Liebl., are clearly the least sensitive ones in regard to increasing summer temperatures and drought due to their ability to maintain transpirational fluxes and cooler canopies [78]. Similarly, based on remote sensing observations over Europe, it was found that broadleaved tree species locally reduce land surface temperatures in summer compared to needle-leaved species [79]. Moreover, the seasonal changes in plant leaf area, the presence of leaves and the time when they appear affect albedo and the gas exchange occurring between leaves and the atmosphere and thus affect carbon dioxide concentrations and the water availability/scarcity [80]. It can be concluded that the higher drought stress resistance of the broadleaved forests in the studied area is due to the different vegetation species within this group and their specific physiological peculiarities.

3.1.5. Drought Stress Influence on Coniferous Forests The results for the distribution of the coniferous forests between drought classes are specific and differ from the other LCCs (Figure5). The area identified as being unaffected and slightly affected by drought (DC < 4) prevailed and represented 79% of the total area. The areas of the territories with DC < 2 and DC 2–4 were similar—approximately 40% (Figure4d). Because of the morphological and physiological traits of some coniferous species, they are relatively tolerant to drought, although cumulative water deficit constrains Forests 2021, 12, x FOR PEER REVIEW 17 of 33

3.1.5. Drought Stress Influence on Coniferous Forests The results for the distribution of the coniferous forests between drought classes are specific and differ from the other LCCs (Figure 5). The area identified as being unaffected and slightly affected by drought (DC < 4) prevailed and represented 79% of the total area. The areas of the territories with DC < 2 and DC 2–4 were similar—approximately 40% (Figure 4d). Because of the morphological and physiological traits of some coniferous species, they are relatively tolerant to drought, although cumulative water deficit constrains Forests 2021, 12, 974 their growth in the long term [81]. The results relating to the beginning16 ofand 32 middle of August confirm this statement (Figure 4d and Figure 5). The area of coniferous forests with moderate drought stress (DC 4–6) decreased considerably and represented 14.6%, whichtheir is similar growth to in that the long for termdeciduous [81]. The forests results relating(Figure to 4 thed). beginningStudies on and the middle mechanisms of con- August conﬁrm this statement (Figures4d and5). The area of coniferous forests with trollingmoderate water droughtdelivery stress and (DC water 4–6) decreasedloss in the considerably leaves of and conifers represented considered 14.6%, which two is main mech- anismssimilar for conserving to that for deciduous water during forests (Figure water4d). stress Studies. One on the group mechanisms withstands controlling the drought by keepingwater the delivery stomata and closed water loss, and in thethe leaves other of does conifers so consideredby producing two main a water mechanisms transport system capablefor of conserving functioning water duringunder water extreme stress. str Oneess group [82]. withstands The territories the drought with by DC keeping > 6 were negli- the stomata closed, and the other does so by producing a water transport system capable of gible—functioningabout 6% under (Figure extreme 4d) stress, due [82 to]. Thetheir territories distribution with DC at > 6high were elevations negligible—about in mountain regions.6% (Figure4d), due to their distribution at high elevations in mountain regions.

(a) (b)

Figure 5. DistributionFigure 5. Distribution of coniferous of coniferous forests forests (green (green coloured coloured area) area) unaffected unaffected by drought by drought (DC < 2) at (DC the beginning< 2) at the of August beginning of Au- gust (a) and( ain) andthe in middle the middle of August of August ( (bb), ScaleScale 1:1,100,000. 1:1,100,000.

3.1.6. Drought3.1.6. Drought Stress Stress Influence Influence onon Mixed Mixed Forests Forests The results obtained for the average trend of DC values for the mixed forest show Thean analogy results to obtained the broad leavedfor the and average coniferous trend forests of DC (Figure values4e). However, for the concerningmixed forest show an analogyterritories to the notbroad affected leaved by drought, and coniferous as well as those forests slightly (Figure affected 4e (DC). However, 2–4), the mixed concerning ter- ritoriesforests not affected keep position by betweendrought, the as previous well twoas those LCCs. slightly The respective affected areas were(DC 28.3%2–4), and the mixed for- 52.0% (Figure4e). The results obtained are in accordance with the widespread agreement ests keepthat treeposition species between biodiversity the plays previous an important two role LCCs. in a functioning The respective forest ecosystem areas were [83], 28.3% and 52.0% ecosystem(Figure 4 servicese). The [84 results] and resistance/resilience obtained are in toaccordance different disturbances, with the both widespread biotic and agreement that treeabiotic species [84]. Allbiodiversity three types ofplays forest—broad an important leaved, coniferousrole in a andfunctioni mixed—hadng forest very ecosystem [83], ecosystemsimilar areas services with DC 4–6—15.3%,[84] and resistance/ 14.6% and 16.4%,resilience respectively to different (Figure4 disturbances,c–e). However, both biotic in the categories with a higher degree of drought impact, both broad leaved and mixed and abioticforests [84] were. shownAll three to be types more tolerant of forest to drought,—broad as theirleaved, average coniferous areas with and DC > mixed 6 were —had very similarabout area 3%,s with while DC for the4–6 coniferous—15.3%, forests, 14.6% this and area 16.4% was 6.3%, respectively (Figure4c–e). The(Figure adaptation 4c–e). However, in the tocategories drought in mixedwith standsa higher compared degre toe monospecificof drought stands impact, is due both to the broad coexistence leaved of and mixed species with complementary traits, which subsequently improve water availability, water forests were shown to be more tolerant to drought, as their average areas with DC > 6 uptake or water use efficiency and compatibility with the climatic and edaphic conditions were aboutof the site 3%, [85 while]. for the coniferous forests, this area was 6.3% (Figure 4c–e). The adaptation to drought in mixed stands compared to monospecific stands is due to the coex- 3.1.7. Recovery Processes istence of species with complementary traits, which subsequently improve water availa- The recovery process is presented as a difference in the area for all LCCs and DCs bility, variability water uptake between theor beginning water use of August efficienc 2019y and and the compatibility middle of September with (Figure the6 ). climatic and edaphic conditions of the site [85].

Forests 2021, 12, x FOR PEER REVIEW 18 of 33

3.1.7. Recovery Processes Forests 2021, 12, 974 17 of 32 The recovery process is presented as a difference in the area for all LCCs and DCs variability between the beginning of August 2019 and the middle of September (Figure 6).

FigureFigure 6. 6.Recovery Recovery process process of of the the individual individual land land cover cover classes classes (LCCs) (LCCs) in the in periodthe period between between early earlyAugust August and and mid mid-September-September shown shown as variationvariation in in Drought Drought coefﬁcient coefficient (DC) (DC) classes; classes 231/321—; 231/321—pas- pasturestures and and grasslands; grasslands; 324—transitional324—transitional woodlands woodlands and and shrubs; shrubs; 311—broadleaved 311—broadleaved forests; forests; 312— 312—co- coniferousniferous forests;forests; 313—mixed 313—mixed forests. forests.

TheThe results results show show that that with with the the decrease decrease in temperatures, in temperatures, the increase the increase in lands in un- lands unaf- affectedfected by droughtcontinues, continues, as as the the rate rate of of recovery recovery is the is the highest highest in the in broadleavedthe broadleaved for- forests,ests, followed followed by by coniferous coniferous andand mixed mixed forests. forests. These These LCCs LCCs most most successfully successfully coped coped with with the maximum summer air temperature, and the territories identiﬁed as unaffected by the maximum summer air temperature, and the territories identified as unaffected by drought (DC < 2) exceeded 25% in mid-September. However, the coniferous forests were characterizeddrought (DC by < a2) faster exceed recoveryed 25% after in mid drought-September. stress than However, the broadleaved the coniferous forests. The forests were areacharacterized of the recovered by a coniferousfaster recovery forests after was 15.5% drought in the stress middle than of August,the broad whereasleaved that forests. The ofarea the broadleavedof the recovered forests coniferous was only 2.2% forests (Table was5). 15.5% in the middle of August, whereas that of the broadleaved forests was only 2.2% (Table 5). Table 5. Change in the area of the respective Drought coefﬁcient (DC) by periods—in %. Table 5. Change in the area of the respective Drought coefficient (DC) by periods—in %. From the Early August to the From the Mid-August to the From the Early September to the DC From the Early AugustMid-August to the From the Mid-AugustEarly September to the Early From theMid-September Early September to the Mid- DC Mid-August Pastures and NaturalSeptember Grasslands September <2 0.5 1.9 −0.1 2–4 −4.6 Pastures and Natural−8.8 Grasslands −2.9 <2 4–6 0.5 −1.8 1.9 −8.4 −2.9 −0.1 2–4 6–8−4.6 1.1−8.8 3.0 −1.6 −2.9 8–10 2.8 7.2 2.7 4–6 >10−1.8 1.9−8.4 5.2 4.9 −2.9 6–8 1.1 Transitional Woodlands/Shrubs3.0 −1.6 8–10 <22.8 2.47.2 5.1 2.5 2.7 2–4 −5.9 −4.2 −3.3 >10 4–61.9 0.4 5.2 −6.7 −1.7 4.9 6–8 1.6Transitional Woodlands/Shrubs 2.4 0.9 8–10 1.0 2.4 0.9 <2 >102.4 0.45.1 1.0 0.9 2.5 2–4 −5.9 Broadleaved− forests4.2 −3.3 4–6 <20.4 2.2−6.7 16.2 12.0 −1.7 2–4 −5.5 −8.2 −11.0 6–8 4–61.6 2.6 2.4 −9.2 −1.6 0.9 8–10 6–81.0 0.52.4 0.7 0.3 0.9 >10 8–100.4 0.11.0 0.4 0.2 0.9 >10 0.0 0.2 0.1 Broadleaved forests <2 2.2 16.2 12.0 Forests 2021, 12, 974 18 of 32

Table 5. Cont.

From the Early August to the From the Mid-August to the From the Early September to the DC Mid-August Early September Mid-September Coniferous Forests <2 15.5 10.6 2.2 2–4 −13.0 −7.0 −1.4 4–6 −3.1 −4.5 −1.0 6–8 0.0 0.0 −0.1 8–10 0.3 0.5 0.0 >10 0.2 0.5 0.2 Mixed Forests <2 5.4 13.5 6.9 2–4 −3.9 −5.7 −5.5 4–6 −2.1 −8.8 −1.8 6–8 0.3 0.4 0.3 8–10 0.2 0.3 0.1 >10 0.1 0.2 0.1

The broadleaved forests unaffected by drought were almost evenly distributed throughout the study area, with concentrations in Ihtimanska Sredna gora, Verila, Plana (Figures7 and8) , and on the lower slopes of the Balkan mountain range (form. Querceta dalechampii; form Ostryeta carpinifoliae and forests dominated by Carpinus orientalis Mill.; Quercus cerris L. and Querqus frainetto Ten.; [23]). The main part of the recovered mixed forests was on the northern slopes of the Central Balkan range, and that of the coniferous was on the southeastern slopes of Vitosha Mountain. For the pastures and grasslands and for the transitional woodlands and shrubs, these territories were rather less common (Figure6, Table5). The areas with DC 2–4 and 4–6 decreased considerably, especially for the broadleaved and coniferous forests and to a lesser degree for the mixed forests. In the grasslands, a tendency towards an increase in drought impact was observed. In all of the LCCs, the drought categories with DC < 8 recorded a decrease in the areas between the beginning of September and mid-September. Only the grasslands with DC > 8 showed an increase in the area. These results probably indicate that the grassland ecosystems, influenced by the local environmental factors, reached their limit of drought resilience at in the beginning of September and ceased their vegetative development. In Forests 2021, 12, x FOR PEER REVIEW 20 of 33 woodlands/shrubs, the increase in areas severely affected by drought (DC > 6) for the same period was 2.6% (Figure6, Table5).

(a) (b)

Figure 7. DistributionFigure 7. Distribution of Drought of coefficient Drought coefﬁcient(DC) in Ihtimanska (DC) in Ihtimanska Sredna gora, Sredna Verila, gora, and Verila, Plana and at Plana the beginning at the of Sep- tember 2019beginning (a) and in of the September middle of 2019 September (a) and 2019 in the (b middle); scale of1:300,000 September. 2019 (b); scale 1:300,000.

(a) (b)

(c) Figure 8. Distribution of broadleaved forests unaffected by drought (DC < 2) in the middle of August 2019 (a), at the beginning of September 2019 (b), and in the middle of September 2019 (c), scale 1:1,000,000.

3.1.8. LCCs Vegetation Response to Drought Stress in 2019 and 2020 The differences in the spatial distribution of the individual drought categories in the studied ecosystems in 2019 and 2020 directly reflect the variations in ecological conditions, caused by the manifestation of the climatic elements (Figures 9 and 10). The whole of 2019 was warmer and drier than 2020 [69]. The analysis of climatic data, acquired in four meteorological stations located in the study area, shows that the mean average air temperature in August 2019 was higher by 0.9 °C (22 °C) than that in 2020 (20.1 °C) [69]. The average precipitation sum in August 2019 was 21.55 mm, ranging between 22.4 mm in the lower territories and 9.1 mm in the high mountains, distributed over an average of 4.25 days in the month and concentrated in early August (1–5 August 2019) [69]. In August Forests 2021, 12, x FOR PEER REVIEW 20 of 33

(a) (b) Forests 2021, 12, 974 19 of 32 Figure 7. Distribution of Drought coefficient (DC) in Ihtimanska Sredna gora, Verila, and Plana at the beginning of Sep- tember 2019 (a) and in the middle of September 2019 (b); scale 1:300,000.

(a) (b)

(c)

Figure 8.Figure Distribution 8. Distribution of broad of broadleavedleaved forests forests unaffected unaffected by droughtdrought (DC (DC < 2)< in2) thein the middle middle of August of August 2019 (a 2019), at the (a), at the beginningbeginning of September of September 2019 2019 (b), (andb), and in inthe the middle middle of of September September 2019 2019 (c ),(c scale), scale 1:1,000,000. 1:1,000,000.

3.1.8.3.1.8. LCCs LCCs Vegetation Vegetation Response Response to to Drought Drought Stress Stress in 2019 in 2019 and 2020and 2020 TheThe differences differences in in the the spatial distribution distribution of theof the individual individual drought drought categories categories in the in the studied ecosystems in 2019 and 2020 directly reﬂect the variations in ecological conditions, studiedcaused ecosystems by the manifestation in 2019 and of 2020 the climatic directly elements reflect the (Figures variations9 and 10 in). ecological The whole conditions, of caused2019 by was the warmer manifestation and drier of than the 2020climati [69c]. elements The analysis (Figures of climatic 9 and data, 10). acquiredThe whole in of 2019 wasfour warmer meteorological and drier stations than 2020 located [69] in. theThe study analysis area, of shows climatic that thedata, mean acquired average in air four me- teorologicaltemperature stations in August located 2019 in was the higher study by area 0.9 ◦,C shows (22 ◦C) that than the that mean in 2020 average (20.1 ◦C) air [69 tempera-]. tureThe in August average precipitation2019 was higher sum in b Augusty 0.9 °C 2019 (22 was °C) 21.55 than mm, that ranging in 2020 between (20.1 °C) 22.4 [69] mm. The av- eragein theprecipitation lower territories sum and in August 9.1 mm in 2019 the highwas mountains, 21.55 mm, distributed ranging between over an average 22.4 mm of in the 4.25 days in the month and concentrated in early August (1–5 August 2019) [69]. In August lower2020, territories the amount and of precipitation9.1 mm in the was high much mountains, greater at 74.5 distributed mm, ranging over between an 43.5average mm of 4.25 daysin in the the lower month territories and andconce upntrated to 98 mm in in early the high August mountains, (1–5 distributedAugust 2019) over 7.25[69] days. In August during the month [69]. Forests 20212021,, 1212,, xx FORFOR PEERPEER REVIEWREVIEW 2121 of of 33 33

Forests 2021, 12, 974 2020,2020, the the amount amount of of precipitation precipitation was was much much greater greater at at 74.5 74.5 mm, mm, ranging ranging between between 43.5 43.520 ofmm mm 32 inin the the lower lower territories territories and and up up to to 98 98 mm mm in in the the high high mountains, mountains, distributed distributed over over 7.25 7.25 days days duringduring thethe month month [69] [69]. .

FigureFigureFigure 9.9. 9. Distribution Distribution ofof the the Drought Drought coefficient coefﬁcientcoefficient ( (DC)DC(DC) )at atat the thethe end endend of ofof August/beginning August/beginningAugust/beginning of of of Septem- Septem- Septem- berberber 2019.2019. 2019.

Figure 10. Distribution of the Drought coefficient (DC) at the end of August/beginning of Septem- FigureFigure 10. 10. Distribution Distribution of of the the Drought Drought coefficient coefficient ( DC(DC) )at at the the end end of of August/beginning August/beginning of of Septem- Septem- ber 2020. berber 2020. 2020. The analysis of drought impact on the ecosystems at the end of August/beginning of The analysis of drought impact on the ecosystems at the end of August/beginning of SeptemberThe analysis in 2019 of and drought 2020 clearly impact indicates on the ecosystems the differences at the in vegetationend of August/beginning response of the of September in 2019 and 2020 clearly indicates the differences in vegetation response of the individualSeptember LCCs in 2019 and and ecosystems. 2020 clearly indicates the differences in vegetation response of the individual LCCs and ecosystems. individualThe vegetation LCCs and response ecosystems. in grassland ecosystems was most significant. The difference The vegetation response in grassland ecosystems was most significant. The difference in theThe areas vegetation of grassland response severely in grassland affected by ecosystems drought in was the most drier significant. 2019, compared The difference to 2020, in the areas of grassland severely affected by drought in the drier 2019, compared to 2020, wasin the 5.9% areas (Figure of grassland4a). In a severely study performed affected by by drought Cartwright in the et al.,drier 2020 2019, [ 17 compared], a clear depen- to 2020, dencewaswas 5.9%5.9% between (Figure(Figure GPP 4 4a).a). and In In a droughta study study performed performed sensitivity by by was Cartwright Cartwright observed. et et Amongstal. al., ,2020 2020 [17] the[17], studieda, aclear clear depend- ecosys-depend- tems,enceence between between the grasslands GPP GPP and and are drought drought characterized sensitivity sensitivity as having was was observed. observed. the lowest Amongst Amongst GPP. The the the woodlands/shrubsstudied studied ecosystems, ecosystems, ecosystemsthethe grasslandsgrasslands are are secondare characterized characterized in terms of as as biomass having having andthe the GPP.lowest lowest In GPP. theGPP. latter The The woodlands/shrubs ecosystems, woodlands/shrubs the difference ecosys- ecosys- intemstems the areare spatial second second distribution in in terms terms of of of biomass biomass the lands an an identifieddd GPP. GPP. In In the as the beinglatter latter severelyecosystems, ecosystems, affected the the difference difference by drought in in the in the 2019spatialspatial and distribution distribution 2020 was 4.2%.of of the the Inlands lands the identified coniferousidentified as as forests, being being severely theseverely percentage af affectedfected was by by drought 1.8%,drought in in the in 2019 2019 mixed and and forests20202020 was was it was4.2%. 4.2%. 0.9%, In In the the and coniferous coniferous in the broadleaved forests, forests, the the forestspercentage percentage it was was was 0.7% 1.8%, 1.8%, (Figure in in the the4 c–e).mixed mixed The forests forests obtained it it was was results0.9%,0.9%, and and are in similarin the the broad broad to thoseleavedleaved of forests Cartwright forests it it was was et 0.7% al.,0.7% 2020 (Figure (Figure [17 ],4c 4c and––e).e). indicateThe The obtained obtained that theresults results ecosystems are are sim- sim- withilarilar toto relatively thosethose ofof lowCartwright Cartwright GPP shows et et al. al. stronger, ,202 20200 [17] [17] drought, ,and and indicate stressindicate and that that a greaterthe the ecosystems ecosystems differential with with response relatively relatively to severelowlow GPPGPP compared showsshows stronger tostronger moderate drought drought droughts stress stress than and and foresteda a greater greater areas. differential differential However, response response the results to to severe severe showing com- com- theparedpared differences toto moderatemoderate in the droughtsdroughts spatial distributionthan than forested forested of areas. areas. the drought However, However, unaffected the the results results ecosystems showing showing the reveal the differ- differ- an interestingencesences inin thethe trend spatial spatial for dist dist theributionribution forest ecosystems.of of the the droug droug Thehtht unaffected unaffected difference ecosystems ecosystems in their spatial reveal reveal distribution an an interesting interesting in thetrendtrend drier forfor 2019,the the forest forest compared ecosystems. ecosystems. to the The wetterThe difference difference 2020 was in in their largesttheir spatial spatial in the distribution distribution coniferous in forestsin the the drier drier (18.9%), 2019, 2019, followed by the mixed (14.9%) and broadleaved forests (11.4%) (Figure4c–e). These results indicate that the local factors, such as ecosystems fragmentation, lithological basis, azonal soil types (e.g., Rendzinas), slope exposure, etc., have a significant impact on the vegetation response, further exacerbating the drought effect and additionally contributing to the Forests 2021, 12, x FOR PEER REVIEW 22 of 33

compared to the wetter 2020 was largest in the coniferous forests (18.9%), followed by the mixed (14.9%) and broadleaved forests (11.4%) (Figure 4c–e). These results indicate that the Forests 2021, 12, 974 local factors, such as ecosystems fragmentation, lithological basis, azonal soil types 21(e.g. of 32, Rendzinas), slope exposure, etc., have a significant impact on the vegetation response, further exacerbating the drought effect and additionally contributing to the stronger vegetation responsestronger of the vegetation forests with response lower of GPP the forestsin the drought with lower categories GPP in the with drought DC > categories6. The broad- with leavedDC forests > 6. The show broadleaveded greater forestsadaptability, showed weaker greater differential adaptability, response weaker differentialto severe drought response and convergencto severe droughte in the andcategories convergence with slight in the drought categories stress with (DC slight 2–4) drought (Figure stress4c–e).(DC 2–4) (Figure4c–e). 3.2. Validation of the Results 3.2. Validation of the Results The results obtained after the application of validation methods are presented in the figures andThe tables results below. obtained after the application of validation methods are presented in the Thefigures spectral and tables profiles below., generated after the field measurements and point-based vali- The spectral profiles, generated after the field measurements and point-based val- dation, are shown in Figure 11a–e. On their basis, vegetation indices were calculated. The idation, are shown in Figure 11a–e. On their basis, vegetation indices were calculated. values of the vegetation indices are shown in Table 6. They are representative of the envi- The values of the vegetation indices are shown in Table6. They are representative of the ronmentalenvironmental conditions conditions in each point in each and point were and used were in used the evaluation in the evaluation of the ofaccuracy the accuracy of the of vegetationthe vegetation indices presented indices presented in Table in 7. Table The 7indices. The indices with the with highest the highest accuracy accuracy (PSRI2 (PSRI2 and MSI2)and were MSI2) involved were involvedin the area in-based the area-based validation. validation. The validation The validation includes includeslinear regres- linear sion analysesregression (Table analyses 7, Figure (Table 712a,b)., Figure 12a,b).

(a)

(b)

Figure 11. Cont. Forests 2021, 12, x FOR PEER REVIEW 23 of 33 Forests 2021, 12, 974 22 of 32

(c)

(d)

(e)

FigureFigure 11. Spectral 11. Spectral profile proﬁless of of the the individual land land cover cover classes classes (LCCs) (LCCs differently) differently affected affected by drought: by drought:(a) pastures (a) pastures and natural and natural grasslands; grasslands (b) broadleaved; (b) broad forests;leaved (c ) forests; coniferous (c) forests;coniferous (d) mixed forests; forests; (d) mixed(e forests;) transitional (e) transitional woodlands/shrubs. woodlands/shrubs. The Drought The coefﬁcientDrought coefficient (DC) classes (DC) presents classes the presents differently the differentlyaffected affected by drought by drought point locations point locations (point-based (point validation).-based validation).

Forests 2021, 12, 974 23 of 32

Table 6. Spectral indices values of the individual land cover classes (LCCs) differently affected by drought: pastures and natural grasslands; broadleaved forests; coniferous forests; mixed forests; transitional woodlands/shrubs. The Drought coefﬁcient (DC) classes presents the differently affected by drought point locations (point-based validation).

Spectral Index Drought Coefﬁcient (DC) Class Pastures and Natural Grasslands <2 2–4 4–6 6–8 8–10 >10 MSI 0.54 0.72 0.73 1.41 1.51 1.68 MSI2 0.21 0.28 0.31 0.81 0.89 0.98 NDVI705 0.44 0.38 0.39 0.13 0.13 0.00 NMDI 0.57 0.48 0.47 0.44 0.40 0.35 PSRI −0.13 −0.10 −0.14 −0.02 −0.01 0.07 PSRI2 0.07 0.09 0.06 0.16 0.15 0.20 Broadleaved Forests <2 2–4 4–6 >6 n/a n/a MSI 0.40 0.54 0.50 0.51 n/a n/a MSI2 0.15 0.17 0.19 0.22 n/a n/a NDVI705 0.56 0.53 0.55 0.50 n/a n/a NMDI 0.62 0.55 0.57 0.54 n/a n/a PSRI −0.15 −0.16 −0.13 −0.10 n/a n/a PSRI2 0.03 0.02 0.04 0.06 n/a n/a Coniferous Forests <2 2–4 >4 n/a n/a n/a MSI 0.44 0.55 0.58 n/a n/a n/a MSI2 0.16 0.18 0.20 n/a n/a n/a NDVI705 0.47 0.56 0.51 n/a n/a n/a NMDI 0.63 0.59 0.55 n/a n/a n/a PSRI −0.25 −0.15 −0.13 n/a n/a n/a PSRI2 −0.02 0.03 0.05 n/a n/a n/a Mixed Forests <2 2–4 >4 n/a n/a n/a MSI 0.54 0.51 0.50 n/a n/a n/a MSI2 0.15 0.15 0.16 n/a n/a n/a NDVI705 0.51 0.56 0.59 n/a n/a n/a NMDI 0.60 0.58 0.58 n/a n/a n/a PSRI −0.23 −0.21 −0.12 n/a n/a n/a PSRI2 0.00 −0.01 0.03 n/a n/a n/a Transitional Woodlands/Shrubs <2 2–4 4–6 6–8 >8 n/a MSI 0.45 0.57 0.67 1.07 0.76 n/a MSI2 0.16 0.21 0.28 0.49 0.34 n/a NDVI705 0.46 0.44 0.43 0.18 0.38 n/a NMDI 0.63 0.53 0.52 0.40 0.46 n/a PSRI −0.26 −0.08 −0.13 −0.06 −0.13 n/a PSRI2 −0.03 0.06 0.06 0.19 0.09 n/a Forests 2021, 12, x FOR PEER REVIEW 25 of 33 Forests 2021, 12, 974 24 of 32

Table 7. Correlation coefficients (in %) between the individual Drought coefficient (DC) classes in theTable studied 7. Correlation semi-natural coefﬁcients territories (in and %) between the spectral the individual indices used Drought in the coefﬁcient validation (DC) process. classes in the studied semi-natural territories and the spectral indices used in the validation process. Spectral Index Forest Ecosystems All Semi-Natural Territories MSISpectral Index53.3 Forest Ecosystems All Semi-Natural66.7 Territories MSI2 MSI86.7 53.390.5 66.7 MSI2 86.7 90.5 NMDINMDI 66.7 66.776.2 76.2 NDVI705NDVI705 46.7 46.761.9 61.9 PSRI PSRI60.0 60.061.9 61.9 PSRI2PSRI2 86.7 86.771.4 71.4

(a) (b)

FigureFigure 12. 12.LinearLinear regression regression analyses analyses for for the the independent independent D Droughtrought coefficient coefficient (DC)(DC) andand dependentdependent ( a()a) modified modified Moisture Moisture StressStress Index Index (MSI2 (MSI2)) and and (b) ( bmodified) modified Plant Plant Senescence Senescence Reflectance Reflectance Index (PSRI2).(PSRI2).

TheThe area area-based-based validation validation also also includes includes independent independent datasets (FCover,(FCover, LAI, LAI, GDMP, GDMP, andand Landsat Landsat-8-8 based based indices). indices). In In this this validation validation step, inin additionaddition to to the the indices indices with with the the highesthighest accuracy accuracy (MSI2 (MSI2 and and PRSI2) PRSI2),, NMDI NMDI was also involved,involved, because because this this index index has has the the highesthighest weight weight coefficient coefficient in in the the DC DC algorithm. algorithm. PSRI2 PSRI2 uses uses two two red red edge edge ba bandsnds in inits its calculation,calculation, which which are not are supported not supported by the by Landsat the Landsat-8.-8. For that For thatreason, reason, the regression the regression anal- analysis included only one PSRI2, based on Sentinel-2 image. ysis included only one PSRI2, based on Sentinel-2 image. The results, obtained after the statistical analyses, show a close relationship between The results, obtained after the statistical analyses, show a close relationship between the spatial resolution and the values of the correlation coefficients. Generally, the input thedata spatial with resolution lower spatial and resolution the values decreased of the correlation the correlation coefficients. coefficients. Generally, The correlation the input datacoefficient with lower between spatial GDMP resolution and MSI2, decrease derivedd the from correlation the Landsat-8 coefficients. dataset, T washe correlation the only coefficientexception between (Table8). GDMP Nevertheless, and MSI2, the results derived could from be the used Landsat for a comparison-8 dataset, between was the the only exceptionindividual (Table indices 8). andNevertheless, the environmental the results indicators. could be used for a comparison between the individual indices and the environmental indicators. Table 8. Correlation coefficients (in %) between the environmental indicators and the spectral indices Tableused 8. in Correlation the area-based coefficients validation (in process. %) between the environmental indicators and the spectral indices used in the area-based validation process. DC NMDI-S2 NMDI-L8 PSRI2-S2 MSI2-S2 MSI2-L8 FCover DC 27.2NMDI-S2 41.9NMDI-L8 37.6 PSRI2 42.5-S2 MSI2 45.1-S2 MSI2 42.8 -L8 FCoverLAI 27.2 28.841.9 45.337.6 40.942.5 46.3 45.1 44.9 39.942.8 LAIGDMP 28.8 34.445.3 50.540.9 44.146.3 51.8 44.9 55.9 59.239.9 GDMP 34.4 50.5 44.1 51.8 55.9 59.2 The GDMP and the FCover showed the highest correlation with the MSI2, whereas theThe LAI GDMP was most and dependent the FCover on theshow PSRI2ed the (Table highest8). correlation with the MSI2, whereas the LAI was most dependent on the PSRI2 (Table 8). The DC was distinguished as having the lowest dependency on FCover, LAI and GDMP (Table 8), which was expected to some extent. The DC algorithm involves coefficients of dissimilarities, used for normalization by elevation and land cover type. These Forests 2021, 12, 974 25 of 32

The DC was distinguished as having the lowest dependency on FCover, LAI and GDMP (Table8), which was expected to some extent. The DC algorithm involves coefficients of dissimilarities, used for normalization by elevation and land cover type. These coefficients are permanent and do not change dynamically over time. Nevertheless, they affect the values of the spectral indices, used in the calculation of the DC. On the other hand, the traditional spectral indices and the environmental indicators are affected only by the dynamically changing spectral reflectance of the land surface and eventually by the meteorological data, used in their calculation. When comparing the indices in relation to the environmental indicators, it can be found that the DC and the PSRI2 have a similar pattern. Both indices showed the lowest correlation with the FCover, whereas the MSI2 was less correlated with the LAI (Table8).

A Comparison between NMDI and DC in the Drought Stress Assessment The drought coefficient (DC) suggested in a recent study integrates traditional spectral indices (NMDI and MSI), widely used for assessments of ecosystems’ moisture content and drought. However, these indices are relative indices, showing the differences between the moisture content throughout a given territory without taking into consideration dissimilarities between the ecosystems, physically predetermined by the landscape-forming factors. Applying traditional indices, without considering any of the climate-related, landscape-forming factors, such as elevation and land cover type, in drought assessments, leads to misinterpretation of the results. In this way, typically drier ecosystems (e.g., oak ecosystems) could be identified as subjected to drought stress. Aiming to diminish such misinterpretations, the DC was designed with the intention to assess the real drought effect. Figure 13 represents the share of the area of distribution of the NMDI and DC for the individual LCCs below and above 900 m. The values between 0 and 100 represent the percentages of areas for the DC and those between 100 and 200—the areas of the NMDI. The results indicate the differences in the manifestation between the DC normalized by elevation and land cover type and the traditional NMDI in the assessment of drought stress in the individual LCCs, divided by elevation. The values of both indices were divided proportionally into six categories, according to the breakpoints used in the drought stress assessment (2, 4, 6, 8, and 10). The areas with the lowest category (1) were identified as the least affected by drought, and those with the highest category (6) were the most affected. Although, NMDI was assigned with the highest weight coefficient (45%) in the DC algorithm and forms a significant part of it, the differences in manifestation of both indices are clearly visible. The NMDI overestimated the lower situated ecosystems as being more affected by the drought stress and vice versa; the higher situated ecosystems were assessed as being weakly affected. This manifestation of NMDI is expected as it is a relative index, showing that one place is drier than another. In this manner, the greater part of the forests ecosystems, situated below 900 m were identified as drier, which is completely valid for assessments of moisture content but not for assessments of drought stress. In contrast, the DC more sufficiently assessed the distribution of areas between the different categories of drought stress, regardless of the altitude. Consequently, the ecosystems with the highest drought stress, assessed by the DC, increase smoothly in the areas situated below 900 m and vice versa when compared to higher elevation areas. The mentioned tendency can be more clearly observed in the ecosystems with lower GPP. In the forest ecosystems, due to their higher GPP and lower sensitivity to drought stress [17], the depicted trend is less noticeable. This indicates that the DC reduces the relativity in the evaluation of drought. However, the DC is still in an experimental phase of development, and despite its advantages in drought stress assessment, it should be interpreted cautiously. Forests 2021Forests, 122021, x FOR, 12, 974 PEER REVIEW 26 of 3227 of 33

Pastures and Natural Grasslands

Transitional Woodlands/Shrubs

Broadleaved forests

Coniferous forests

Mixed forests

FigureFigure 13. Share 13. Share of the of area the areaof distribution of distribution (in (in %) %) of of the the Normalized Normalized Multi-bandMulti-band Drought Drought Index Index (NMDI) (NMDI and) theand Drought the Drought coefficientcoefﬁcient (DC) (DC)for the for different the different land land cover cover classes classes (LCCs (LCCs)) above andand below below 900 900 m. m. a.s.l. a.s.l in. the in studiedthe studied area. area.

4. Discussion The areas identified as being most affected by drought stress had their minimum distribution at the beginning of August. Due to the maximum air temperature [28], which is observed in early August, the drought unaffected area showed its minimum distribution (Figure 4a–e). In the broadleaved forests in the beginning of August, ecosystems severely affected by drought (DC > 10) were not observed, whereas in the coniferous forests, Forests 2021, 12, 974 27 of 32

4. Discussion Forests 2021, 12, x FOR PEER REVIEW 28 of 33 The areas identified as being most affected by drought stress had their minimum distribution at the beginning of August. Due to the maximum air temperature [28], which is observed in early August, the drought unaffected area showed its minimum distribution their(Figure percentage4a–e). In thewas broadleaved 0.4% (Figure forests 4c,d). inUnder the beginning conditions of of August, drought, ecosystems plants commonly severely allocateaffected available by drought resources (DC > 10)to root were growth not observed, rather than whereas shoot in growth the coniferous [86,87].forests, As conifers their dopercentage not have was the 0.4%mechan (Figureisms4 c,d).to roll Under their conditionsleaves or drop of drought, foliage, plants they cope commonly with drought allocate stressavailable through resources an efficient to root growthroot system rather that than can shoot mine growth the soil [86 ,profile87]. As for conifers available do not water, have downregulatingthe mechanisms growth to roll their of aboveground leaves or drop mass, foliage, and theymaintaining cope with an droughtefficient stressphotosystem. through Deciduousan efficient tre rootes, systemon the other that can hand, mine can the reduce soil profile water loss for availablethrough transpiration water, downregulating by reduc- inggrowth leaf ofsize, aboveground changing stomata mass, and density, maintaining altering an cuticle efficient properties, photosystem. and Deciduousleaf senescence trees, [88]on. the other hand, can reduce water loss through transpiration by reducing leaf size, changingWith stomataincreasing density, time alteringdistance cuticlefrom the properties, temperature and leafmaximum, senescence an [increase88]. in the shareWith of lands increasing identified time as distance being fromunaffected the temperature by drought maximum, (DC < 2) anwas increase observed in the (Figure share 4aof– landse). Coniferous identified forests as being are unaffectedcharacterized by droughtby a faster (DC recovery < 2) was after observed drought (Figure stress4 a–ethan). theConiferous broadleaved forests forests. are characterizedThe area of the by recovered a faster coniferous recovery after forests drought is 15.5% stress in the than middle the ofbroadleaved August, and forests. that of The the areabroad ofleaved the recovered forests was coniferous only 2.2% forests (Table is 15.5% 5). The in recovery the middle pro- of cessesAugust, in andthe thatbroad ofleaved the broadleaved ecosystems forests were wasconcentrated only 2.2% in (Table the 5northern). The recovery part of processesthe stud- iedin the area broadleaved and in the high ecosystems mountains were in concentrated the southern inpart, the where northern the part soils of are the characterized studied area byand a inhigher the highsoil moisture mountains level in, the due southern to less solar part, radiation. where the This soils recovery are characterized pattern clearly by a indicateshigher soil a moistureclose connection level, due betwe to lessen solar the restoration radiation. This processes recovery in patterndeciduous clearly forests indicates and climatica close connectionfactors. The between Balkan mountain the restoration range processesrepresentsin a barrier deciduous for the forests air masses and climatic pene- tratingfactors. from The the Balkan northwest mountain, and rangeconsequently represents affects a barrier the spatial for the distribution air masses of penetrating precipita- tionfrom and the heatnorthwest, flow. As and a consequently result, the precipitation affects the spatial on the distribution northern slopes of precipitation of the Balkan and mountainheat flow. range As a and result, in the the highest precipitation mountainous on the northernparts reach slopeses up ofto 1200 the Balkan–1400 mm, mountain while onrange the southern and in the slopes highest and mountainous adjacent territories, parts reaches which upare tounder 1200–1400 a rain shadow mm, while effect, on the amountsouthern of slopes precipitation and adjacent is about territories, 550–600 which mm [89] are. under a rain shadow effect, the amount of precipitationIn the middle is aboutof August, 550–600 the mm effects [89 ].of drought continued to intensify (Figure 4a–e). In theIn broad the middleleaved offorests, August, areas the with effects an ofincreasing drought drought continued impact to intensify can be (Figureobserved4a–e). on theIn thesouthern broadleaved slope of forests, the western areas withBalkan an mountain increasing range drought (Figure impact 14a can–c). beThese observed are karst on areasthe southern with southern slope of exposure, the western under Balkan the mountainrain shadow range effect (Figure of the 14 a–c).Balkan These range. are Karst karst landscaareas withpes southernare characterized exposure, by under rapid theinfiltration rain shadow of the effect precipitation of the Balkan into the range. carbonate Karst rocks.landscapes Following are characterized this, the leaked by precipitation rapid infiltration recharges of the the precipitation ground water into [90] the. This carbonate leads torocks. long Following-term surface this, “ thekarst leaked drought precipitation” [91]. Moreover, recharges carbonate the ground rocks water accumulate [90]. This leadsa lot of to heatlong-term and then surface slowly “karst release drought” it, leading [91]. Moreover, additionally carbonate to moisture rocks accumulateloss in the akarst lot of land- heat scapesand then [92] slowly. release it, leading additionally to moisture loss in the karst landscapes [92].

(a) (b) (c)

FigureFigure 14. Distribution of Drought coefﬁcientcoefficient (DC) (DC) in in the the western western Balkan Balkan range range in in the the middle middle of Augustof August (a), ( ata), theat the beginning begin- ningof September of September (b), and (b), inand the in middle the middle of September of September (c); scale (c); scale 1:400,000. 1:400,000.

At the end of August/beginning of September, an increase in both areas unaffected by drought and those severely affected by drought was observed (Figure4a–e). The most noticeable reason for this was the recovery of the broadleaved forests, which continued until the end of the study period (Table5). This indicates that the deciduous forests need more time to recover after drought stress than the coniferous forests. The deciduous forests are also distinguished by weaker dynamics in vegetation response to drought impact. The prevailing part of the broadleaved forests showed slight drought impact (DC 2–4) during the entire period of study (from early August until mid-September) (Figure4c). Similarly, a study of deciduous forest trees’ responses to severe drought in Central Europe did not show severe water stress [93]. In comparison to the coniferous forests, the recovery processes after drought stress in the broadleaved ecosystems were slower. They reached the maximum in the degree of increase in the areas unaffected by drought by the end of August, while in the coniferous forests, this maximum was observed in mid-August. Moreover, in the coniferous forests, the areas with slight drought impact (DC 2–4) prevailed only at the beginning of the studied period (Figure4d). After mid-August, territories identiﬁed as unaffected by drought (DC < 2) dominated. The faster recovery of coniferous forests and the prevalence of the areas unaffected by drought after the drought stress, during the temperature maximum, indicates their short-term drought resistance in comparison to broadleaved forests. However, in spite of that, the coniferous forest also showed a greater increase in areas more severely affected by drought (6.9%) in comparison to broadleaved forests (3.3%) (Figure4d,c). In the spatial distribution of the more severely affected areas, there is a clear pattern. In the mountainous, higher altitude territories, the areas with DC ranging between 8 and 10 increased, whereas in the lower territories, such as the valleys and plains, the areas of the ecosystems most affected by drought (DC > 10) increased. This pattern is a result of differences in the horizontal structure of the landscapes in the mountains and plains. The valleys and plains are characterized by a higher degree of anthropogenization and fragmentation of the semi-natural areas. Moreover, the coniferous forests in the lower territories are predominantly artiﬁcially created and the planted species are not developed in their natural habitats. Such forests are exposed to constant stress and their resilience is severely limited. The regulating services, provided by forests with fragmented areas, are also severely deteriorated [94]. In the mountainous areas, however, the forests are not as fragmented, which increases their resilience to stress and maintains their regulating functions. The lower growth of deciduous forests severely affected by drought is also connected with the fact that they are predominantly developed in their natural habitat regardless of whether they have a secondary origin. Although less resistant to drought, the broadleaved forests were predominantly characterized by a slight drought impact (DC 2–4). The broadleaved forests with moderate drought impact (DC 4–6) covered 11.8% of the area in early September. The percentage of the moderate affected by drought coniferous forests was the same. However, in the categories of severely affected by drought ecosystems, the coniferous forests were more widely represented than deciduous forests (Figure4c,d). The vegetation response of the individual LCCs to different manifestation of climate elements is strongest in the ecosystems with low GPP. The ecosystems with the highest GPP (broadleaved forests) are distinguished by the weakest impact of climate variables. In spite of the drought tolerance of coniferous forests, under differences in air temperature by 0.9 ◦C and by 53 mm in the precipitation sum between August 2019 and 2020, the broadleaved forests showed a change in the severely affected by drought areas by only 0.7%, whereas the change in the severely affected coniferous forest was 1.8%. Nevertheless, the coniferous forests were the least affected by drought. The percentage of the unaffected by drought areas in the coniferous forests was by 7.5% higher than in the broadleaved forests (Figure4c,d). This indicates that in the short term, the coniferous forests are more sensitive to climatic factors than broadleaved forests. In mid-September, the broadleaved forests were distinguished by greater dynamics in the spatial distribution of the areas differently affected by drought. These forests were Forests 2021, 12, 974 29 of 32

characterized by the highest growth of both unaffected and severely affected by drought ecosystems (Table5). This nature of distribution shows again that local, landscape-forming factors are crucial for the difference in vegetation response to drought stress throughout the study area. The obtained results indicate that the climatic factors, as well as the local landscape- forming factors, forest fragmentation, and anthropogenization, predetermine the spatial distribution of the differently affected by drought ecosystems.

5. Conclusions The assessment of drought impact on the ecosystems is a complex task that needs to consider the influence of various environmental factors on the functions and processes occurring in complex systems—landscapes. The application of indices and indicators based entirely on spectral vegetation indices cannot sufficiently achieve effective and accurate drought assessments [17]. This was also confirmed by the present study. The comparison between the potential of NMDI and DC to evaluate drought stress on a heterogeneous and diverse territory clearly indicates the advantages of the normalizing elevation and land cover type DC. The results show that the DC reduces the relativity in the evaluation of drought. The application of only one spectral index or a combination of spectral indices which do not consider basic differences between the ecosystems leads to relativity of drought stress evaluation and misinterpretation of the results for a heterogeneous territory. However, the DC is still in an experimental phase. In order to refine the DC algorithm for drought stress assessment, a validation field campaign is needed. A field measurement of the actual drought impact on the various ecosystems would bring valuable information for more accurate specification of the coefficients for normalization and weight factor- ing. For that reason, despite the advantages of the DC, the obtained results should be interpreted cautiously.

Author Contributions: Conceptualization, D.A.; Methodology, D.A.; Data processing, D.A.; Analysis of the results, D.A. and E.V.; Validation, D.B.; Spectral reflectance profiles, D.B.; PSRI2 index, D.A.; writing—original draft, D.A.; writing—review and editing, E.V. and D.A. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by Bulgarian National Science Fund under contract No KP-06- M27/2 (KΠ-06-M27/2). Acknowledgments: The authors appreciate the anonymous reviewers for their constructive com- ments and suggestions that significantly improved the quality of this manuscript. Conflicts of Interest: The authors declare no conflict of interest.

References 1. Handbook of Drought Indicators and Indices. Integrated Drought Management Programme (IDMP); Integrated Drought Management Tools and Guidelines Series 2; Svoboda, M., Fuchs, B.A. (Eds.) World Meteorological Organization (WMO) and Global Water Partnership (GWP): Geneva, Switzerland, 2016. 2. Ebi, K.; Bowen, K. Extreme events as sources of health vulnerability: Drought as an example. Weather Clim. Extrem. 2016, 11, 95–102. [CrossRef] 3. Seneviratne, S.I.; Nicholls, N.; Easterling, D.; Goodess, C.M.; Kanae, S.; Kossin, J.; Luo, Y.; Marengo, J.; McInnes, K.; Rahimi, M.; et al. Changes in climate extremes and their impacts on the natural physical environment. managing the risks of extreme events and disasters to advance climate change adaptation. In A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC); Field, C.B., Barros, V., Stocker, T.F., Qin, D., Dokken, D.J., Ebi, K.L., Mastrandrea, M.D., Mach, K.J., Plattner, G.-K., Allen, S.K., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2012; pp. 109–230. 4. IPCC (Intergovernmental Panel on Climate Change). Summary for policy-makers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013; p. 28. 5. European Environment Agency. European Waters—Assessment of Status and Pressures; Publications Ofﬁce of the European Union: Luxembourg, 2018; p. 85. [CrossRef] Forests 2021, 12, 974 30 of 32

6. Zhao, T.; Dai, A. The magnitude and causes of global drought changes in the 21st century under a low moderate emissions scenario. J. Clim. 2015, 28, 4490–4512. [CrossRef] 7. Um, M.J.; Kim, Y.; Park, D.; Jung, K.; Wang, Z.; Kim, M.M.; Shin, H. Impacts of potential evapotranspiration on drought phenomena in different regions and climate zones. Sci. Total Environ. 2020, 703, 135590. [CrossRef] 8. Sharov, V.; Ivanov, P.; Alexandrov, V. Meteorological and Agrometeorological Aspects of Drought in Bulgaria. Rom. J. Hydrol. Water Resour. 1994, 1, 163–172. 9. Koleva, E.; Alexandrov, V. Drought in the Bulgarian low regions during the 20th century. Theor. Appl. Climatol. 2008, 92, 113–120. [CrossRef] 10. National Climate Change Adaptation Strategy and Action Plan for the Republic of Bulgaria; Ministry of Environment and Water: Sofia, Bulgaria, 2019. 11. The Study on Integrated Water Management in Bulgaria; Japan International Cooperation Agency (JICA) Project; Ministry of Environment and Water: Sofia, Bulgaria, 2008. 12. Sayers, P.B.; Li, Y.; Moncrieff, C.; Li, J.; Tickner, D.; Xu, X.; Speed, R.; Li, A.; Lei, G.; Qiu, B.; et al. Drought Risk Management: A Strategic Approach; UNESCO: Paris, France, 2015. 13. Clothier, B.; Green, S.; Deurer, M. Green, blue and grey waters: Minimising the footprint using soil physics. In Proceedings of the 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane, Australia, 1–6 August 2010. 14. Orth, R.; Destouni, G. Drought reduces blue-water fluxes more strongly than green-water fluxes in Europe. Nat. Commun. 2018, 9, 1–8. [CrossRef][PubMed] 15. Hristova, V.; Petrov, A. Study on the influence of the forests as an opportunity for organic capture of greenhouse gases. In Proceedings of the 10th Scientific Conference with International Participation Space, Ecology, Safety (SES 2014), Space Research and Technology Institute—Bulgarian Academy of Sciences, Sofia, Bulgaria, 21–23 May 2014; pp. 389–393. 16. Avetisyan, D.; Nedkov, R. Application of remote sensing and GIS for determination of predicted status of the ecosystem/landscape services in changing environmental conditions. In Proceedings of the SPIE 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), Paphos, Cyprus, 18–21 March 2019. [CrossRef] 17. Cartwright, J.M.; Littlefield, C.E.; Michalak, J.L.; Lawler, J.J.; Dobrowski, S.Z. Topographic, soil, and climate drivers of drought sensitivity in forests and shrublands of the Pacific Northwest, USA. Sci. Rep. 2020, 10, 1–13. [CrossRef][PubMed] 18. Vicente-Serrano, S.M.; Gouveia, C.; Camarero, J.J.; Beguería, S.; Trigo, R.; Lopez-Moreno, I.; Azorin-Molina, C.; Pasho, E.; Lorenzo-Lacruz, J.; Revuelto, J.; et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. USA 2013, 110, 52–57. [CrossRef][PubMed] 19. Sims, D.A.; Brzostek, E.; Rahman, A.F.; Dragoni, D.; Phillips, R.P. An improved approach for remotely sensing water stress impacts on forest C uptake. Glob. Chang. Biol. 2014, 20, 2856–2866. [CrossRef] 20. Yu, Z.; Wang, J.; Liu, S.; Rentch, J.S.; Sun, P.; Lu, C. Global gross primary productivity and water use efficiency changes under drought stress. Environ. Res. Lett. 2017, 12, 014016. [CrossRef] 21. Zhang, Y.; Voigt, M.; Liu, H. Contrasting responses of terrestrial ecosystem production to hot temperature extreme regimes between grassland and forest. Biogeosciences 2015, 12, 549–556. [CrossRef] 22. Nam, K. Natural Geography of Bulgaria; Geosfera: Sofia, Bulgaria, 2003. (In Bulgarian) 23. Assenov, A. Biogeography and Natural Capital of Bulgaria; University Press St. Kliment Ohridski: Sofia, Bulgaria, 2021. (In Bulgarian) 24. Velev, S. Climate in Bulgaria; Heron Press: Sofia, Bulgaria, 2010. (In Bulgarian) 25. Filipov, D.; Rachev, G. Chronological variability of precipitation at the meteorological stations Mussala, Botev and Chernivrah. In Annual of Sofia University “St. Kliment Ohridski”, Faculty of Geology and Geography, Book 2—Geography; University Press “St. Kliment Ohridski”: Sofia, Bulgaria, 2016; Volume 109. (In Bulgarian) 26. Mitkov, S.; Koleva-Lizama, I. Study of the multi-annual fluctuations of the monthly air temperatures for the region of Cherni Vrah. Manag. Sustain. Dev. 2017, 5, 1–6. (In Bulgarian) 27. Mitkov, S.; Koleva-Lizama, I. Investigation of perennial fluctuations in monthly air temperatures for the Murgash peak area. Bulg. J. Soil Sci. Agrochem. Ecol. 2019, 53, 3–4. (In Bulgarian) 28. Matev, S. Contemporary Climate Fluctuations in Bulgaria. Ph.D. Thesis, Sofia University St. Kliment Ohridski, Sofia, Bulgaria, 2020. (In Bulgarian). 29. User Guides. Sentinel-2 MSI—Level-1C Product—Sentinel Online—Sentinel. Available online: https://sentinel.esa.int/web/ sentinel/user-guides/sentinel-2-msi/product-types/level-1c (accessed on 15 May 2021). 30. Open Access Hub. Available online: https://scihub.copernicus.eu/ (accessed on 15 May 2021). 31. Spatial Resolutions. Sentinel-2 MSI. User Guides. Available online: https://sentinels.copernicus.eu/web/sentinel/user-guides/ sentinel-2-msi/resolutions/spatial (accessed on 15 May 2021). 32. CLC 2018—Copernicus Land Monitoring Service. Available online: https://land.copernicus.eu/pan-european/corine-land-cover (accessed on 27 June 2021). 33. EU-DEM v1.1—Copernicus Land Monitoring Service. Available online: https://land.copernicus.eu/imagery-in-situ/eu-dem/ eu-dem-v1.1 (accessed on 15 May 2021). 34. Copernicus Land Monitoring Service. Available online: https://land.copernicus.eu/ (accessed on 27 June 2021). 35. USGS_EarthExplorer—Home. Available online: https://earthexplorer.usgs.gov/ (accessed on 21 June 2021). Forests 2021, 12, 974 31 of 32

36. Wang, L.; Qu, J.J. NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophys. Res. Lett. 2007, 34, L20405. [CrossRef] 37. Healey, S.; Cohen, W.; Zhiqiang, Y.; Krankina, O. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote. Sens. Environ. 2005, 97, 301–310. [CrossRef] 38. Stankova, N. Study of the condition and effects of forest ecosystems after fire using remote aerospace methods and data. Ph.D. Thesis, Space Research and Technology Institute, Bulgarian Academy of Sciences, Sofia, Bulgaria, 2017. 39. Schowengerdt, R. Remote Sensing, Models and Methods for Image Processing; Elsevier: Burlington, MA, USA, 2007; pp. 237–239. ISBN 978-0-12-369407-2. 40. Nedkov, R. Orthogonal Transformation of Segmented Images from the Satellite Sentinel-2. Comptes Rendus Acad. Bulg. Sci. 2017, 70, 687–692. 41. Hunt, E.R., Jr.; Rock, B.N. Detection of changes in leaf water content using Near- and Middle-Infrared reflectances. Remote Sens. Environ. 1989, 30, 43–54. 42. Rock, B.N.; Vogelmann, J.E.; Williams, D.L.; Vogehnann, A.F.; Hoshizaki, T. Remote detection of forest damage. Bioscience 1986, 36, 439–445. [CrossRef] 43. Welikhe, P.; Quansah, J.E.; Fall, S.; McElhenney, W. Estimation of Soil Moisture Percentage Using LANDSAT based Moisture Stress Index. J. Remote. Sens. GIS 2017, 6, 1–5. [CrossRef] 44. Assenov, A. Biogeography of Bulgaria; An-Di Andriyan Tasev: Sofia, Bulgaria, 2006. (In Bulgarian) 45. Petkov, D.; Georgiev, G.; Nikolov, H. Thematically oriented multichannel spectrometer (TOMS). Aerosp. Res. Bulg. 2005, 20, 51–54. 46. Serrano, L. Deriving Water Content of Chaparral Vegetation from AVIRIS Data. Remote. Sens. Environ. 2000, 74, 570–581. [CrossRef] 47. Gitelson, A.; Merzlyak, M.N. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B Biol. 1994, 22, 247–252. [CrossRef] 48. Merzlyak, M.N.; Gitelson, A.A.; Chivkunova, O.B.; Rakitin, V.Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 1999, 106, 135–141. [CrossRef] 49. Zang, Z.; Wang, G.; Lin, H.; Luo, P. Developing a spectral angle based vegetation index for detecting the early dying process of Chinese fir trees. ISPRS J. Photogramm. Remote. Sens. 2021, 171, 253–265. [CrossRef] 50. Galieni, A.; D’Ascenzo, N.; Stagnari, F.; Pagnani, G.; Xie, Q.; Pisante, M. Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography. Front. Plant Sci. 2021, 11, 11. [CrossRef][PubMed] 51. Hwang, T.; Gholizadeh, H.; Sims, D.A.; Novick, K.A.; Brzostek, E.R.; Phillips, R.P.; Roman, D.T.; Robeson, S.; Rahman, A.F. Capturing species-level drought responses in a temperate deciduous forest using ratios of photochemical reflectance indices between sunlit and shaded canopies. Remote. Sens. Environ. 2017, 199, 350–359. [CrossRef] 52. Chivkunova, O.B.; Solovchenko, A.E.; Sokolova, S.G.; Merzlyak, M.N.; Reshetnikova, I.V.; Gitelson, A.A. Reflectance Spectral Features and Detection of Superficial Scald–induced Browning in Storing Apple Fruit. J. Russ. Phytopathol. Soc. 2001, 2, 73–77. 53. McClure, W.F. A Spectrophotometric Technique for Studying the Browning Reaction in Tobacco. Trans. ASAE 1975, 18, 380–383. [CrossRef] 54. Merzlyak, M.N.; Gitelson, A.A.; Pogosyan, S.I.; Chivkunova, O.B.; Lekhimena, L.; Garson, M.; Buzulukova, N.P.; Shevyryova, V.V.; Rumyantseva, V.B. Reflectance spectra of plant leaves and fruits during their development, senescence and under stress. Rus. J. Plant Physiol. 1997, 44, 614–622. 55. Kancheva, R.; Borisova, D. Plant senescence and soil background impact on vegetation reflectance and color features. Compt. Rend. Acad. Bulg. Sci. 2004, 57, 53–58. 56. Merzlyak, M.N.; Chivkunova, O.B.; Rybakova, N.I. Reflectance spectra of potato tuber discs as related to wounding, infection with Phytophthora infestans and treatment with arachidonic acid. Mosc. Univ. Sci. Bull. 1990, 1, 26–32. 57. Vaughn, K.C.; Duke, S.O. Function of polyphenol oxidase in higher plants. Physiol. Plant. 1984, 60, 106–112. [CrossRef] 58. Butt, V.S. Oxygenation and oxidation in the metabolism of aromatic compounds. Annu. Proc. Phytochem. Soc. Eur. 1985, 25, 349–365. 59. Gunthardt-Goerg, M.S.; Vollenweider, P. Linking stress with macroscopic and € microscopic leaf response in trees: New diagnostic perspectives. Environ. Pollut. 2007, 147, 467–488. [CrossRef] 60. Escudero, A.; Delarco, J.M. Ecological significance of the phenology of leaf abscission. Oikos 1987, 49, 11–14. [CrossRef] 61. Barker, D.H.; Seaton, G.G.R.; Robinson, S.A. Internal and external photoprotection in developing leaves of the CAM plant Cotyledon orbiculata. Plant Cell Environ. 1997, 20, 617–624. [CrossRef] 62. Steyn, W.J.; Wand, S.J.E.; Holcroft, D.M.; Jacobs, G. Anthocyanins in vegetative tissues: A proposed unified function in photoprotection. New Phytol. 2002, 155, 349–361. [CrossRef][PubMed] 63. Close, D.C.; Beadle, C.L. The ecophysiology of foliar anthocyanin. Bot. Rev. 2003, 69, 149–161. [CrossRef] 64. Chalker-Scott, L. Environmental significance of anthocyanins in plant stress responses. Photochem. Photobiol. 1999, 70, 1–9. [CrossRef] 65. Gitelson, A.A.; Merzlyak, M.N.; Chivkunova, O.B. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem. Photobiol. 2001, 74, 38–45. [CrossRef] 66. Fraction of Green Vegetation Cover. Copernicus Global Land Service. Available online: https://land.copernicus.eu/global/ products/fcover (accessed on 27 June 2021). 67. Leaf Area Index. Copernicus Global Land Service. Available online: https://land.copernicus.eu/global/products/lai (accessed on 27 June 2021). Forests 2021, 12, 974 32 of 32

68. Dry Matter Productivity. Copernicus Global Land Service. Available online: https://land.copernicus.eu/global/products/dmp (accessed on 27 June 2021). 69. StringMeteo.com. Нaчaлo. Available online: https://www.stringmeteo.com (accessed on 15 May 2021). (In Bulgarian). 70. Carlsson, M.; Merten, M.; Kayser, M.; Isselstein, J.; Wrage-Mönnig, N. Drought stress resistance and resilience of permanent grasslands are shaped by functional group composition and N fertilization. Agric. Ecosyst. Environ. 2017, 236, 52–60. [CrossRef] 71. Lenhart, P.A.; Eubanks, M.D.; Behmer, S.T. Water stress in grasslands: Dynamic responses of plants and insect herbivores. Oikos 2014, 124, 381–390. [CrossRef] 72. Zhang, Y.; Feng, X.; Fu, B.; Chen, Y.; Wang, X. Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability. Remote Sens. 2021, 13, 1289. [CrossRef] 73. Koerner, S.E.; Collins, S.L. Interactive effects of grazing, drought, and fire on grassland plant communities in North America and South Africa. Ecology 2014, 95, 98–109. [CrossRef][PubMed] 74. Knapp, A.K.; Smith, M.D. Variation Among Biomes in Temporal Dynamics of Aboveground Primary Production. Science 2001, 291, 481–484. [CrossRef] 75. Knapp, A.K.; Carroll, C.J.; Denton, E.M.; La Pierre, K.J.; Collins, S.L.; Smith, M.D. Dierential sensitivity toregional-scale drought in six central US grasslands. Oecologia 2015, 177, 949–957. [CrossRef] 76. Kurc, S.; Small, E. Dynamics of evapotranspiration in semiarid grassland and shrubland ecosystems during the summer monsoon season, central New Mexico. Water Resour. Res. 2004, 40, W09305. [CrossRef] 77. Eldridge, D.J.; Bowker, M.A.; Maestre, F.T.; Roger, E.; Reynolds, J.F.; Whitford, W.G. Impacts of shrub encroachment on ecosystem structure and functioning: Towards a global synthesis. Ecol. Lett. 2011, 14, 709–722. [CrossRef] 78. Scherrer, D.; Bader, M.K.; Körner, C. Drought-sensitivity ranking of deciduous tree species based on thermal imaging of forest canopies. Agric. For. Meteorol. 2011, 151, 1632–1640. [CrossRef] 79. Schwaab, J.; Davin, E.L.; Bebi, P.; Duguay-Tetzlaff, A.; Waser, L.T.; Haeni, M.; Meier, R. Increasing the broad-leaved tree fraction in European forests mitigates hot temperature extremes. Sci. Rep. 2020, 10, 14153. [CrossRef] 80. Dahlin, K.M.; Fisher, R.A.; Lawrence, P.J. Environmental drivers of drought deciduous phenology in the Community Land Model. Biogeosciences 2015, 12, 5061–5074. [CrossRef] 81. Herguido, E.; Granda, E.; Benavides, R.; García-Cervigón, A.I.; Camarero, J.J.; Valladares, F. Contrasting growth and mortality responses to climate warming of two pine species in a continental Mediterranean ecosystem. For. Ecol. Manag. 2016, 363, 149–158. [CrossRef] 82. Brodribb, T.; McAdam, S.; Jordan, G.; Martins, S. Divergent adaptation to drought in conifers. Proc. Natl. Acad. Sci. USA 2014, 111, 14489–14493. [CrossRef] 83. Grossiord, C. Having the right neighbors: How tree species diversity modulates drought impacts on forests. New Phytol. 2019, 228, 42–49. [CrossRef] 84. Pretzsch, H.; Steckel, M.; Heym, M.; Biber, P.; Ammer, C.; Ehbrecht, M.; Bielak, K.; Bravo, F.; Ordóñez, C.; Collet, C.; et al. Stand growth and structure of mixed-species and monospecific stands of Scots pine (Pinus sylvestris L.) and oak (Q. robur L., Quercus petraea (Matt.) Liebl.) analysed along a productivity gradient through Europe. Eur. J. For. Res. 2019, 162, 141. [CrossRef] 85. Forrester, D.I.; Bauhus, J. A review of processes behind diversity—Productivity relationships in forests. Curr. For. Rep. 2016, 2, 45–61. [CrossRef] 86. Blake, T.; Li, J. Hydraulic adjustment in jack pine and black spruce seedlings under controlled cycles of dehydration and rehydration. Physiol. Plant. 2003, 117, 532–539. [CrossRef][PubMed] 87. Ledig, F.T.; Bormann, F.H.; Wenger, K.F. Distribution of dry matter growth between shoot and roots in loblolly pine. Bot. Gaz. 1970, 131, 349–359. [CrossRef] 88. Taiz, L.; Zeiger, E. Plant Physiology, 4th ed.; Sinauer Associates: Sunderland, MA, USA, 2006; pp. 671–680. 89. Penin, R. Physical Geography of Bulgaria; Bulvest 2000: Sofia, Bulgaria, 2012. (In Bulgarian) 90. Nerantzaki, S.D.; Nikolaidis, N.P. The response of three Mediterranean karst springs to drought and the impact of climate change. J. Hydrol. 2020, 591, 125296. [CrossRef] 91. Qing, X. Characteristics and Comprehensive Treatment Strategy of Karst Drought in Central Guangxi. J. Guilin Univ. Technol. 2005, 7, 278–283. 92. Todorov, N.; Velchev, A. Contemporary Landscape Structure of Karst Areas in Sofia Basin. In Problems of Geography; Bulgarian Academy of Sciences: Sofia, Bulgaria, 2016; Volume 1–2, pp. 65–77. (In Bulgarian) 93. Leuzinger, S.; Zotz, G.; Asshoff, R.; Körner, C. Responses of deciduous forest trees to severe drought in Central Europe. Tree Physiol. 2005, 25, 641–650. [CrossRef][PubMed] 94. Avetisyan, D.; Borisova, B.; Nedkov, R. Determination of the landscapes regulation capacity and their role in the prevention of catastrophic events: A case study from the Lom River upper Valley, Bulgaria. In Sustainable Mountain Regions: Challenges and Perspectives in Southeastern Europe; Koulov, B., Zhelezov, G., Eds.; Springer: Cham, Switzerland, 2016; pp. 173–190. [CrossRef]