sustainability
Article The Vegetation Dynamics and Climate Change Responses by Leaf Area Index in the Mu Us Desert
Defeng Zheng 1 , Yanhui Wang 1, Yanying Shao 2,* and Lixin Wang 3
1 School of Urban and Environmental Science, Liaoning Normal University, Dalian 116029, China; [email protected] (D.Z.); [email protected] (Y.W.) 2 Department of Geography, Changchun School Affiliated to Beijing Normal University, Changchun 130000, China 3 Jilin Yinhe Water Conservancy Hydropower New Technology Design Co., Ltd., Changchun 130000, China; [email protected] * Correspondence: [email protected]
Received: 11 May 2019; Accepted: 28 May 2019; Published: 4 June 2019
Abstract: Knowing the long-dated dynamic changes of vegetation in the Mu Us Desert is critical for strengthening sustainable management of vegetation restoration projects in the next planned cycle until 2050. To predict leaf area indexes (LAIs) under long-dated climate scenarios (2013–2050) in the Mu Us Desert, the relationship between earlier meteorological data and LAI was tracked with regression analysis on the basis of LAI data from the Global Land Surface Satellite (GLASS) and the grid meteorological data during 1982–2012, and the LAIs were estimated based on five-Global Circulation Model (GCM) ensemble means under three representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5). We found an increasing trend in precipitation and a significant increase in potential evapotranspiration (PET) during the earlier period in the Mu Us Desert, and that could continue into the long-dated under three RCPs in the Mu Us Desert. Warming trends occur in the earlier and long-dated periods for annual average air temperature. Compared with the observations, the temperature rises respectively by 0.6 °C, 0.7 °C, and 1 °C under the three RCPs mentioned above. The annual maximum LAI largely increased with a rate-of-change of 0.029 m2 m 2 yr-1. · − · Precipitation has been a major influencing factor to vegetation dynamics and growth in the Mu Us Desert. The permissible LAIs by 2050 are 0.42–0.88 m2 m 2, 0.42–0.87 m2 m 2, and 0.41–0.87 m2 m 2 · − · − · − under the three RCPs, respectively. Contrasted with the baseline period (1982–2012), the LAI is found to be already close to the current value in the northwestern and southern Mu Us Desert.
Keywords: leaf area index; vegetation restoration; climatic variables; general circulation models
1. Introduction For the terrestrial ecosystem, vegetation is an important component. Desert vegetation provides important water and soil conservation services [1] and plays a dominant role in the prevention of desertification, mitigation of wind-sand damage, and restoration of the local environment in arid and semi-arid ecosystems [2–4]. Revegetation is an important approach for restoring degraded and disturbed ecosystems resulting from inappropriate anthropogenic activities [5,6], and has been internationally recognized and accepted as one of the most reasonable and effective ways [7–10]. Some international organizations, such as UNDP, UNEP, and FAO, had implemented a series of plans in sand-fixing plants to prevent and control desertification, especially in developing countries for rebuilding desert ecosystems. Since 1978, China has launched some of the most ambitious ecological restoration programs [11,12] called vegetation rehabilitation programs, including the Grain to Green Project [13], Natural Forest Conservation Program, and the Three North Shelter Forest System Project [14,15]. The Mu Us Desert is
Sustainability 2019, 11, 3151; doi:10.3390/su11113151 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 3151 2 of 13 one of the main areas of vegetation restoration [9]. Since its implementation in 1978, artificial vegetation construction has remarkably increased in desert regions and promoted local habitat restoration [16]. However, Guo et al. showed that the soil water status gradually deteriorated with the increase of vegetation coverage [17]. Mu et al. found that large-scale artificial vegetation restoration caused excessive consumption of deep soil water and affected the sustainability of vegetation restoration [18]. Feng et al. thought vegetation expansion in water-limited desert regions would exacerbate the shortage of water resources [19]. It is a challenge as how best to recover vegetation and safeguard regional water resources safely at the same time in Mu Us Desert. Limited efforts have focused on soil water carrying capacity for vegetation [20–22], which is associated with plot scales [22–24]. Due to the strong influences from multiple environmental controls like hydrological and climate elements, terrain, vegetation types, and soil characteristics with spatial and temporal heterogeneity at a regional scale [25], the research results from plot scales could not be directly applied to guide the ecological construction and allocate water resources optimally on a larger scale. The planned period for the next cycle ending is in 2050 [14,26,27]. However, how much vegetation is reasonable and effective for re-vegetation projects in the Mu Us Desert remains uncertain. For this problem, based on regression analysis between LAI and climatic variables, we predicted the dynamic changes of vegetation in the later period (2013–2050). The adopted data included meteorological data, LAIs data (1982–2012), and five GCMs under three RCPs (2.6, 4.5, and 8.5) from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) from 2013 to 2050. Our specific objectives are as follows: the first is to analyze variation of water and heat conditions including precipitation (P), temperature (T), and potential evapotranspiration (PET) from 1982 to 2012 and estimate their further variation (2013–2050) under various RCPs relative to 1982–2012; the second is to further explore spatiotemporal variation of LAI during 1982–2012 in the Mu Us Desert; the third objective is to predict the dynamic changes of LAIs (2013–2050) under the long-dated scenarios (2013–2050).
2. Research Data and Methods
2.1. Situation of the Research Area
We chose the Mu Us Desert as the research area which lies on 106◦500–111◦300 E longitude and 37 61 –40 22 N latitude and covers approximately 4 104 km2 (Figure1). It is located in the ◦ 0 ◦ 0 × transitional zone of arid and semi-arid, most of which belong to the continental monsoon climate [28], with an average annual temperature of 6.0–8.5 °C. The average annual precipitation is 250–440 mm, of which more than 60% occurs in summer. The present vegetation of research area is comprised of steppe or grassland, and the western margin belongs to the desertification steppe zone, the central and eastern parts belong to the typical grassland zone, and the southeast margin develops forest grassland. The major vegetation groups in this area are grassland, shrub, meadow, and marsh vegetation, chiefly dominated by Stipabungeana, Artemisia frigida, Caragana korshinskii, and Carexstenophylla, and so on [29]. There are various landscape types dominated by fixed, semi-fixed, or mobile dunes. In addition, grasslands and interconnected lakes and swamps are sporadically distributed [30]. The zonal soils consist of dark loessial soils, light chestnut soil, and brown soil, and the azonal soils are mainly sandy soils, meadow soils, and saline-alkali soils from east to west in this region [31]. It is noteworthy that there is a bedrock-dominated area in the northwest of the study area [32,33]. Sustainability 2019, 11, 3151 3 of 13 Sustainability 2019, 11, x FOR PEER REVIEW 3 of 13
Figure 1.1. Mu Us Desert and meteorological stations location.location. 2.2. Data Source and Processing 2.2. Data Source and Processing All daily meteorological data including temperature, air pressure, relative humidity, wind speed, All daily meteorological data including temperature, air pressure, relative humidity, wind sunshine hours, and precipitation (mean, minimum, maximum) were obtained from 16 meteorological speed, sunshine hours, and precipitation (mean, minimum, maximum) were obtained from 16 stations within and near the Mu Us Desert from China’s National Meteorological Administration meteorological stations within and near the Mu Us Desert from China’s National Meteorological during the earlier period of 1982–2012. Then we interpolated the data onto a 0.083 grid covering the Administration during the earlier period of 1982–2012. Then we interpolated the data onto a 0.083 whole desert region using a thin plate smoothing spline method with the ANUSPLIN 4.3 software grid covering the whole desert region using a thin plate smoothing spline method with the (Australian National university, Canberra, Australia) [34]. Moreover, 8-km re-sampling of the existing ANUSPLIN 4.3 software (Australian National university, Canberra, Australia) [34]. Moreover, 8-km 90-m resolution digital elevation model (DEM) was developed by the Data Center for Resources and re-sampling of the existing 90-m resolution digital elevation model (DEM) was developed by the Data Environmental Sciences, Chinese Academy of Sciences (RESDC). The data of land use at a 1:100,000 scale Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC). The data in 2010 was also provided by RESDC. of land use at a 1:100,000 scale in 2010 was also provided by RESDC. The simulated climate projections from 1982–2050 were acquired from five GCMs (GFDL-ESM2M, The simulated climate projections from 1982–2050 were acquired from five GCMs (GFDL- HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) under RCP 2.6, 4.5, and 8.5. ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) under RCP 2.6, 4.5, The daily variables (the mean, maximum, and minimum temperatures (K); precipitation (kg m 2 s 1); and 8.5. The daily variables (the mean, maximum, and minimum temperatures (K); precipitation· − · − shortwave downwelling radiation (W m 1); near-surface wind speed (m s 1); and relative humidity (kg·m−2·s−1); shortwave downwelling radiation· − (W·m−1); near-surface wind· −speed (m·s−1); and relative (%)) of the GCMs were downscaled to 0.5, and the bias was corrected by the Inter-Sectoral Impact humidity (%)) of the GCMs were downscaled to 0.5, and the bias was corrected by the Inter-Sectoral Model Intercomparison Project (ISI-MIP) [35,36]. Impact Model Intercomparison Project (ISI-MIP) [35,36]. The leaf area index (LAI) 15-daily temporal resolution data from the Mu Us Desert were obtained The leaf area index (LAI) 15-daily temporal resolution data from the Mu Us Desert were from the Global Land Surface Satellite (GLASS) LAI product because it provides temporally continuous obtained from the Global Land Surface Satellite (GLASS) LAI product because it provides temporally profiles from 1982 to 2012 and is more accurate and of a higher quality than the CYCLOPES and MODIS continuous profiles from 1982 to 2012 and is more accurate and of a higher quality than the LAI product with a 5-km spatial resolution in 1982–2000 and 1-km in 2001–2012 [37]. LAI has been chosen CYCLOPES and MODIS LAI product with a 5-km spatial resolution in 1982–2000 and 1-km in 2001– as the indicator of vegetation growth due to the understanding of broader biophysical and physiological 2012 [37]. LAI has been chosen as the indicator of vegetation growth due to the understanding of processes, including photosynthesis, respiration, transpiration, carbon cycling, NPP, etc. [38,39]. broader biophysical and physiological processes, including photosynthesis, respiration, 2.3.transpiration, Local Thin carbon Plate Smoothing cycling, NPP, Spline etc. Method [38,39].
2.3. LocalANUSPLIN Thin Plate (Australian Smoothing NationalSpline Method university, Canberra, Australia) is based on the interpolation theory of ordinary thin plate and local thin plate spline function. Local thin plate smoothing spline ANUSPLIN (Australian National university, Canberra, Australia) is based on the interpolation method is an extension of thin plate smoothing spline prototype, which allows the introduction of a theory of ordinary thin plate and local thin plate spline function. Local thin plate smoothing spline linear covariant quantum model in addition to the ordinary spline independent variables [40]. In this method is an extension of thin plate smoothing spline prototype, which allows the introduction of a study, two modules, SPLINA and LAPGRD in the software, were used to interpolate meteorological linear covariant quantum model in addition to the ordinary spline independent variables [40]. In this study, two modules, SPLINA and LAPGRD in the software, were used to interpolate meteorological factors, such as precipitation and temperature, with longitude and latitude as independent variables and altitude as covariables. The theoretical statistical model of local thin plate smoothing spline is: Sustainability 2019, 11, 3151 4 of 13 factors, such as precipitation and temperature, with longitude and latitude as independent variables and altitude as covariables. The theoretical statistical model of local thin plate smoothing spline is:
T zi = f (xi) + b yi + ei (i = 1, ... , N), (1) where, zi is the dependent variable at point i in space, xi is the independent variable of d dimensional spline, f is the unknown smoothing function of xi, yi is the independent covariant of p dimension, b is the p dimensional coefficient of yi, ei is the random error of independent variables with expected value 2 2 0 and variance wiσ , where wi is the known local relative variation coefficient as the weight, σ is the error variance and is constant at all data points but usually unknown. As can be seen from Equation (1), when the second term is missing in the equation, namely, when the covariant dimension p is 0, the model can be simplified as ordinary thin plate smoothing spline. When the first independent variable is missing, the model becomes a multiple linear regression model. The functions f and b can be determined by least squares estimation:
N T X zi f (xi) b yi − − + ρJm f (x) (2) wi i=1 where, Jm (f ) is the roughness measure function of function f (xi), it is called the spline number, but also called roughness number in ANUSPLIN (Australian National university, Canberra, Australia). ρ is a positive smoothing parameter; it acts as a balance between data fidelity and surface roughness. ρ is usually determined by the minimization of generalized cross-validation (GCV) but also by minimization of the generalized maximum likelihood (GML) or expected true square error (MSE).
2.4. Potential Evapotranspiration (PET)
The Penman–Monteith model can be used to calculate daily reference evapotranspiration (PET0 in mm d 1) then annual PET is obtained by summing all PET . The calculation formula is as follows · − 0 and the calculation process of each component can be found in the literature [41].
900 0.408∆(Rn G) + γ T+273 u2(es ea) PET0 = − − (3) ∆ + γ(1 + 0.34u2)
1 where, ∆ represents curve slope of saturation vapor pressure vs. air temperature (kPa °C− ), Rn and 2 1 G represent respectively net radiation and soil heat flux (MJ m− d− ), γ represents psychometric constant, T represents average daily air temperature (°C), u2 represents average daily wind speed 1 at the 2-m height (m s− ), es and ea are, respectively, the saturation vapor pressure and actual vapor pressure (kPa).
2.5. Theil-Sen Trend Estimator The linear regression analysis was used to detect the temporal and spatial variation of annual maximum LAI, average annual precipitation (P), air temperature (T) and PET in the Mu Us Desert. The overall trends of LAI and climatic variables were reflected by fitting a least squares regression through the time series of each pixel, and the trend slope coefficients were calculated [42], which is computed from: Pn Pn Pn n = i xi = i = xi Slope = i 1 · − i 1 i 1 (4) P P 2 n n i2 n i i=1 − i=1 where, slope represents the average change rate (i.e., trend) of the time-series data. n is the cumulative years during the study period, and i is the year serial number. xi represents a dependent variable (i.e., LAI, precipitation (P), air temperature (T), or PET) for year i. Sustainability 2019, 11, 3151 5 of 13
These correlation coefficients between LAI and other climate variables (i.e., P, T, or PET) can be calculated by using the Pearson correlation method. We have checked the data before using linear regression and Pearson correlation to be sure that all data have a normal distribution.
Sustainability3. Result Analysis 2019, 11, x FOR PEER REVIEW 5 of 13
3.1.3.1. Climatic Climatic V Variablesariables ToTo check the performance performance of simulated simulated values values of of climatic climatic variables variables from from 2013 2013–2050–2050 in in the the Mu Mu Us Us Desert,Desert, the the linear linear regression regression analysis analysis was was performed performed for for the the fitting fitting of of the the data data between between the the simulated simulated annualannual valuesvalues andand corresponding corresponding observational observational values values during during the earlierthe earlier period period (1982–2012). (1982–2012). The fitting The fittingresults (Figureresults 2 (Figure) showed 2) that: show theed coe that:fficients the of coefficients determination of determination (R2) demonstrated (R2) the demonstrated simulations and the simulationsobservations and generally observation agreeds generally with R2 agreedof 0.6463 with and R2 0.5786 of 0.6463 for andP and 0.5786T, respectively. for and This, respectively indicated. Thisthat the indicate later climated that theprediction later climate data using prediction the above-obtained data using linear the above regression-obtained equation linear could regression achieve equationgood results could and achieve have the good rationality results and and ha credibilityve the rationality of application. and credibility of application.
FigureFigure 2. 2. ComparisonComparison of of the the observed observed and and simulated simulated annual annual values values during during 1982 1982–2012.–2012.
FigureFigure 3 showedshowed the spatial distribution distributionss of the long series series P,, T , and PET during the observ observeded periodperiod and and RCP RCP 4.5 4.5 scenario scenario (RCP (RCP 2.6 2.6 and and RCP RCP 8.5 8.5scenario scenarioss were were omitted omitted)) in the in Mu the Us Mu Desert Us Desert.. The distributionThe distributionss of of exhibitedP exhibited similar similar spatial spatial patterns patterns during during the the observed observed period period and and all all RCP RCP scenariosscenarios,, and and had had a gradually a gradually decreasing decreasing from from east east to west towest with withthe range the rangeof 198.4 of–383.6 198.4–383.6 mm, 218. mm,6– 42218.6–4277 mm, 218. mm,1–42 218.1–4222 mm, and mm, 215. and4–422. 215.4–422.33 mm for mmthe earlier for the period earlier and period three and RCPs three (2.6, RCPs 4.5, and (2.6, 8.5 4.5,) inand the 8.5) Mu in Us the Desert Mu Us(Figure Desert 3a,b (Figure). The spatial3a,b). Thepattern spatials of patterns were not of uniformT were between not uniform observations between andobservations scenarios, and but scenarios,were similar but for were three similar RCPs scenario for threes, RCPs with the scenarios, range of with 5.7– the9.2 ℃ range, 7.7– of10. 5.7–9.22 ℃, 7.6°C–, 10.17.7–10.2 ℃, and°C, 7.9 7.6–10.1–10.4 ℃°C for, and the 7.9–10.4 observed°C periodfor the and observed all RCP period scenarios, and allrespectively RCP scenarios, (Figure respectively 3c,d). The s(Figurepatial distribution3c,d). Thes spatial of annual distributions PET were ofsimilar annual during PET werethe observed similar period during and the all observed scenarios period, and hadand aall gradual scenarios, increase and had from a gradual east to increasewest with from the east range to westof 942. with6–1110.4 the range mm, of 1095. 942.6–1110.47–1226.7 mm mm,, 1084.51095.7–1226.7–1224.2 mm, mm, and 1084.5–1224.2 1093.4–1230.8 mm, mm and in 1093.4–1230.8 the Mu Us Desert mm in for the the Mu observed Us Desert period for the and observed all the RCPperiod scenarios, and all therespectively RCP scenarios, (Figure respectively 3e,f). (Figure3e,f). Sustainability 2019, 11, 3151 6 of 13 Sustainability 2019, 11, x FOR PEER REVIEW 6 of 13
Figure 3. TThehe spatial spatial distribution distributionss of ofthe the long long series series annual annual precipitation precipitation ( ), (airP), temperature air temperature ( ), and (T), andpotential potential evapotranspiration evapotranspiration (PET) (PET) during during the the observ observeded period period and and representative representative concentration pathways (RCP)(RCP) 4.54.5 scenario in thethe MuMu UsUs desert.desert.
Figure4 4 showed showed thethe changechange processprocess ofof annualannual precipitation,precipitation, airair temperature,temperature, andand thethe PETPET timetime series ofof the the observed observed spatial spatial mean mean values value fors the for earlier the earlier observed observed period (1982–2012) period (1982 and–2012) the simulated and the spatialsimulated mean spatial values mean for value the laters for period the later (2013–2050) period (2013 using–2050) the using 10-year the running10-year running mean in mean the Mu in the Us Desert.Mu Us Desert Figure.4 Figurea provided 4a provided the time the series time of series observed of observed and simulated and simulated regional regional mean values mean of value annuals of precipitation.annual precipitation. The results The indicatedresults indicated annual annual fluctuation fluctuation and an increasingand an increasing trend in trend precipitation in precipitation during 1 the observed period, and the change rate of 0.6 mm yr (p 0.05).−1 The increasing trend in precipitation during the observed period, and the change rate· of− 0.6 mm·yr≥ (p ≥ 0.05). The increasing trend in wasprecipitation captured was in all captured scenarios in inall the scenarios Mu Us in Desert the Mu (Table Us 1Desert). Comparing (Table 1). the Comparing observed precipitationthe observed duringprecipitation the earlier during period the earlier with the period result with of the the five-GCM result of ensemblethe five-GCM mean ensemble during 2013–2050 mean during under 2013 the– three2050 under RCPs, the precipitation three RCPs changes, precipitation were uniform changes between were uniform observations between and observations scenarios, yetand the scenarios, change magnitudeyet the change of precipitation magnitude wasof precipitation different under was the different three RCPs. under The the annual three precipitation RCPs. The slightlyannual roseprecipitation by 9.8%, slightly 11.0%, androse 10.6%by 9.8 by%, 205011.0%, under and 10.6% the three by 2050 RCPs under contrasting the three the RCP precipitations contrasting during the theprecipitation observed during period the (Table observed1). The period time series(Table of1). regional The time mean series values of regional of annual mean airvalue temperatures of annual wasair temperature presented in was Figure presented4b. A warmingin Figure trend4b. A warming occurred duringtrend occurred the earlier during period the (earlierp < 0.01) period and ( itp is< 0.01) expected and thatit is theexpected average that annual the average temperature annual will temperature continue to will warm continue in the laterto warm period in (thep < later0.01, Tableperiod1) ( inp the< 0.01, Mu UsTable Desert. 1) in Compared the Mu Us with Desert. the observations, Compared with the simulated the observations temperature, the increasedsimulated withtemperature most strong increased changes with for most RCP 8.5strong and smallestchanges magnitudesfor RCP 8.5 forand RCP smallest 2.6. Relative magnitudes to the for observation RCP 2.6. period,Relative the to the annual observation temperature period, rose the respectively annual temperature by 0.60 °C rose, 0.69 respectively°C, and 0.97 by°C 0.60by ℃ 2050, 0.69 under ℃, and the three0.97 ℃ RCPs by 2050(Table under1). Figure the 4 threec showed RCP as time(Table series 1). F ofigure observed 4c showed and simulated a time series regional of observed mean values and ofsimulated annual PET.regional The mean results value revealeds of annual that an PET. increasing The results trend revealed in PET hasthat appeared an increasing during trend the in earlier PET periodhas appeared (p < 0.05) during and werethe earlier projected period to persist (p < 0.05) into and the laterwere periodprojected under to persist each RCP into (p the< 0.01, later Table period1). Comparingunder each RCP the observed (p < 0.01, valuesTable 1). during Comparing the earlier the observed period with values the resultsduring ofthe the earlier five-GCM period ensemble with the mean,results PETof the rose five respectively-GCM ensemble by 7.3%, mean, 6.5%, PET and rose 7.0% respectively under three by RCPs 7.3%, (Table 6.5%,1 ).and 7.0% under three RCPs (Table 1). Sustainability 2019, 11, 3151 7 of 13 Sustainability 2019, 11, x FOR PEER REVIEW 7 of 13
Figure 4. ChangesChanges of of observed observed and and simulated simulated regional regional mean mean values values of annual of annual precipitation precipitation (a), (aira), temperatureair temperature (b),( band), and PET PET (c) in (c) the in theMu Mu Us UsDesert. Desert.
Table 1. Table 1. The regionalregional meanmean valuesvaluesand and the the trends trends of of annual annual precipitation, precipitation, air air temperature, temperature, and and PET PET in Mu Us Desert. in Mu Us Desert.
1 1 1 Period P (mm) P-trend (mm yr ) −1T (◦C) T-trend ( C yr )−1 PET (mm) PET-trend (mm yr −1) Period P (mm) P-trend· (mm·yr− ) T (°C) T-trend◦ (·°C·yr− ) PET (mm) PET-trend (mm·yr· − ) 1982–20121982–2012 299.26299.26 0.600.60 7.967.96 0.0490.049 ** ** 1038.921038.92 1.671.67 * * RCPRCP 2.6 2.6 328.45 328.45 0.37 *0.37 * 8.558.55 0.0260.026 ** ** 1114.371114.37 1.901.90 ** ** RCPRCP 4.5 4.5 332.06 332.06 0.54 **0.54 ** 8.658.65 0.0320.032 ** ** 1106.101106.10 2.002.00 ** ** RCPRCP 8.5 8.5 331.00 331.00 0.66 **0.66 ** 8.928.92 0.0450.045 ** ** 1111.711111.71 2.022.02 ** ** Note:Note: *and *and ** denote ** denote respectively respectively statistically statisticallysignificant significant trend trend of pof< p0.05 < 0.05 and andp < 0.01. p < 0.01.
3.2. Vegetation Vegetation D Dynamicsynamics There are variousvarious types types of of land land use use (e.g., (e.g., farmland, farmland, urbanization, urbanization, and and water water bodies) bodies) in the in studythe study area areawhere where the desert the is desert not completely is not completely covered. To covered accurately. To explore accurately the characteristics explore the characteristicsof spatiotemporal of spatiotemporalvegetation variation vegetation and its variation inherent relationshipand its inherent with relationshiclimate change,p with the climate above-mentioned change, the types above of- mentionedland use were types excluded of land fromuse were the analysis. excluded from the analysis. Spatial distribution ofofthe the over over years years mean mean from from 1982–2012 1982–2012 in in annual annual maximum maximum of LAIof LAI reflected reflected the thegeneral general situation situation of vegetation of vegetation growth, growth, and the and value the value range range of annual of a maximumnnual maximum LAI was LAI 0.4–0.9 was m0.42 –m0.–29 · m(Figure2·m–2 5(Figurea). The 5a). value The between value between 0.6–0.8 and 0.6 0.4–0.6–0.8 and m 0.42 m––20.6was m2·m the–2 mainwas the body, main and body, occupied and 64.4%occupied and · 64.4%30.8% and of the 30.8% whole of the area, whole respectively, area, respectively, with the higherwith the values higher of values LAI occurring of LAI occurring in the south in the and south the andlower the values lower in values the northwest in the northwest of the study of the area. study area. IInn the whole study area area,, a annualnnual maximum LAI derived from GLASS was on the rise with th thee change rate rate of of 0.029 0.029 m m2 2mm–2 –2yr–yr1 (–1p <( p0.05).< 0.05). The area The with area withincreasing increasing trend trendoccupied occupied 84.9% 84.9%of the whole of the area,whole while area, the while vegetation the vegetation growth growth status was status mainly was mainlyslight, and slight, significant and significant improvements improvements were found were withfound 8 with3.3% 83.3% and 1.6% and 1.6% of the of total the total area, area, respectively respectively (Table (Table 2).2). Furthermore, Furthermore, vegetation exhibitedexhibited stability or a decreasing trend in some scatteredscattered areasareas ofof thethe MuMu UsUs DesertDesert (Figure(Figure5 5bb).). Sustainability 2019, 11, 3151 8 of 13 Sustainability 2019, 11, x FOR PEER REVIEW 8 of 13
Figure 5. The spatial distribution and and time trend of the over years mean (1982–2012)(1982–2012) in the annual maximum of leaf area index (LAI) in the Mu Us Desert. Table 2. Trend and variation classification of annual maximum LAI during 1982–2012. Table 2. Trend and variation classification of annual maximum LAI during 1982–2012. Classification of Variation Trend Area Percentage (%) Classification of Variation Trend Area Percentage (%) Serious degradation Slope ≤0.01−0.01 / Serious degradation ≤ − / Slight degradation 0.01 < Slope 0.0006 5.1 Slight degradation −−0.01 < ≤ −≤ −0.0006 5.1 Stability 0.0006 < Slope 0.0006 9.9 Stability −−0.0006 < ≤ ≤ 0.0006 9.9 Slight improvement 0.0006 < Slope 0.01 83.4 Slight improvement 0.0006 < ≤ ≤ 0.01 83.4 Significant improvement Slope > 0.01 1.6 Significant improvement > 0.01 1.6
3.3. Correlation Correlation A Analysisnalysis between LAI and C Climaticlimatic V Variablesariables By correlation correlation analysis, analysis, we we calculated calculated the the correlation correlation coefficients coefficients between between annual annual maximum maximum LAI andLAI andclimatic climatic variables variables (Table (Table 3).3 The). The results results revealed revealed the the significant significant correlation correlation between annualannual maximum LAI and precipitation (P) (P),, the correlation coecoefficientfficient was 0.567 ((pp << 0.01). However, LAI was negatively correlated with temperature (T) and PET PET,, and the correlationcorrelation between LAI and PET was higher than that between LAI andand temperature.temperature. F Furthermore,urthermore, LAI LAI was al alsoso related to terrain factors such as DEM, longitude, and latitude. LAI was negatively correlated with DEMDEM ((pp << 0.01) and positively correlated with longitude (p < 0.01),0.01), but but there there was was no no significan significantt relationship between LAI and latitude (p 0.05). and latitude (p ≥ 0.05).
TableTable 3. Correlation coefficients coefficients between annual maximum LAILAI and climatic variables.
VariablesVariables Correlation Coefficient Coefficient VariablesVariables CorrelationCorrelation Coefficient Coefficient P P 0.567 ** DEMDEM 0.237 **-0.237 ** − T T −0.1970.197 LongitudeLongitude 0.349 * 0.349 * − PET PET −0.373 ** LatitudeLatitude 0.065 -0.065 − − Note:Note: * and * and** denote ** denote respectively respectively significant significant correlationcorrelation at at 5% 5 and% and 1% 1 level% level (2-tailed). (2-tailed).
3.4. Possible Possible LAIs LAIs under under Three S Scenarioscenarios According to to the the correlation correlation coe coefficientfficients ins Table in Table3, we selected 3, we selected the main the factors main a ff ecting factors vegetation affecting vegetationgrowth, including growth, precipitation, including precipitation, PET, longitude, PET and, longitude, DEM. Based and onDEM. regression Based on analysis, regression we quantified analysis, wethe qrelationshipuantified the between relationship annual between maximum annual LAI maximum and other relevantLAI and variables.other relevant The correspondingvariables. The correspondingregression equation regression was as equation follows: was as follows: