remote sensing

Article Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2

Manjunatha Venkatappa 1,2,3,* , Sutee Anantsuksomsri 1,2 , Jose Alan Castillo 4, Benjamin Smith 5,6 and Nophea Sasaki 7

1 Regional Urban and Built Environmental Analytics, Faculty of Architecture, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand; [email protected] 2 Department of Urban and Regional Planning, Faculty of Architecture, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand 3 LEET Intelligence Co., Ltd., Perfect Park, Suan Prikthai, Muang Pathum Thani, Pathum Thani 12000, Thailand 4 Ecosystems Research and Development Bureau, Department of Environment and Natural Resources, Forestry Campus, Los Baños, Laguna 4031, Philippines; [email protected] 5 Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW 2751, Australia; [email protected] 6 Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12. S-223 62, Sweden 7 Natural Resources Management, SERD, Asian Institute of Technology. P.O. Box 4, Khlong Luang, Pathumthani 12120, Thailand; [email protected] * Correspondence: [email protected]



Received: 30 July 2020; Accepted: 15 September 2020; Published: 22 September 2020


Abstract: Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018. After determining the minimum and maximum threshold values for bamboo during the leaf-shedding phenology stage, the PBTC method was applied to produce a seasonal composite enhanced vegetation index (EVI) for Landsat collections and assess the bamboo distributions in 2015 and 2018. Bamboo distributions in 2019 were then mapped by applying the EVI phenological threshold values for 10 m resolution Sentinel-2 satellite imagery by accessing 442 tiles. The overall Landsat 8 OLI bamboo maps for 2015 and 2018 had user’s accuracies (UAs) of 86.6% and 87.9% and producer’s accuracies (PAs) of 95.7% and 97.8%, respectively, and a UA of 86.5% and PA of 91.7% were obtained from Sentinel-2 imagery for 2019. Accordingly, carbon stocks of natural bamboo by district in Siem Reap at the province level were estimated. Emission reductions from the protection of natural bamboo can be used to offset 6% of the carbon emissions from tourists who visit this tourism-destination province. It is concluded that a combination of GEE and PBTC and the increasing availability of remote sensing data make it possible to map the natural distribution of bamboo and carbon stocks.

Keywords: bamboo mapping; Google Earth Engine; Landsat 8 OLI; Sentinel-2; vegetation phenology; threshold values; threshold classification; carbon stocks; CDM; PBTC; REDD+

Remote Sens. 2020, 12, 3109; doi:10.3390/rs12183109 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 3109 2 of 23

1. Introduction Bamboos are evergreen perennial flowering plants that can be found in the tropical and subtropical regions of the world [1]. Bamboos provide important ecosystem goods and services to people: for example, they serve as sources of food and raw materials for construction [2] and play important roles in soil erosion control, water conservation, and land rehabilitation [3]. Bamboo forests also have a potential role in climate change mitigation, acting as sinks or sources of atmospheric carbon [2,4], depending on how they are managed. Despite such importance, studies on the natural distribution of bamboos remain limited, making it difficult to assess their abundance or measure and monitor their carbon stocks. Therefore, it is critically important to map bamboo distributions using fast and timely technologies for transparent, speedy, and monitoring at scale. Understanding the natural distribution of bamboos also allows us to assess the roles of bamboos in climate change mitigation and rural community development. Bamboos are plants in the subfamily Bambusoideae of Gramineae. More than 1500 species are found in the tropical and subtropical regions of Asia [1]. Bamboos are considered non-timber forest products (NTFPs) with the ability to provide food, raw materials [2], and other ecosystem services to industries and local people [2,5]. Natural bamboo and plantations can be managed to mitigate climate change [3,6]: as fast-growing, high-yield plants they are capable of sequestering between 5 and 1 1 12 tCO2 ha− year− [3,5,6]. Therefore, the management of bamboo forests can be eligible under the REDD+ scheme (reducing emissions from deforestation and forest degradation, forest conversation, sustainable management of forests, and enhancement of forest carbon stocks) of the United Nations Framework Convention on Climate Change (UNFCCC) [6]. It can also contribute to the achievement of Sustainable Development Goals (SDGs) 7, 13, and 15 [3]. Nevertheless, effective management of bamboo forests requires an understanding of their distribution through accurate mapping [7]. Remote sensing imagery has been used for various phenological applications in land use and land cover classification [8], mapping of rubber plantations [9], cropland [10,11], and bamboo mapping [12]. Researchers have used medium-resolution Landsat satellite data for the assessment of land cover changes over the past 40 years. Such data have become freely available in global archives that offer detail on land use and land cover dynamics [13–15]. These Landsat data have 30 m spatial resolution and a 16-day temporal interval. Launched in February 2013, Landsat 8 is the latest data acquired using the Operational Land Imager (OLI) and Thermal Infrared Sensor sensors in addition to the existing Landsat series of NASA. The recent launch of the Sentinel-2 satellite mission of the European Space Agency in 2015 and 2017 provides a new opportunity for land-based mapping and monitoring in the tropics using Sentinel-20s high-resolution multispectral data and 10-day temporal interval. While Landsat 8 OLI data have been used for phenology-based studies, very few studies have evaluated the potential use of Sentinel-2 imagery data for phenology-based land cover mapping. Previous studies attempted to examine surface phenology for land use and land cover classification [13] and rubber plantation [9,16] and cropland mapping [17] in tropical regions [11]. For example, Schwieder et al. [18] used remote sensing technologies to assess forest carbon stocks [18]. However, challenges remain in determining the phenology-based threshold values for bamboo distribution mapping because of the rapid changes in phenological behavior between seasons [19,20]. Such rapid changes in phenology make it difficult to avoid the misclassification of bamboo. A recent study indicates that the mapping of bamboo distribution needs to employ robust classification methods to avoid misclassification [13]. Freely available moderate-resolution remote sensing data in a cloud-computing platform offers significant potential [12] as a robust methodology, for example in developing countries in Southeast Asia such as Cambodia, where remotely sensed reconstructions of past land cover change have been limited. Previous studies that employed current mapping techniques for bamboo stands either used very high resolution images without regard to temporal spectral behavior [7,19,21] or time-series Landsat data [12,21] and the enhanced vegetation index (EVI) [22–27] with an additional focus on temporal information. The majority of these studies also implemented traditional imagery Remote Sens. 2020, 12, 3109 3 of 23 acquisition and processing methods, downloading large-sized images and processing them using image processing software. This traditional approach is not only time-consuming, especially when dealing with time-series imagery, but also requires a workstation with a high capacity for image processing and storage [13]. Available studies on bamboo mapping were mostly carried out in subtropical China, India, and East Africa. Several studies have used remote sensing applications to estimate carbon stocks [12,28,29]. Sasaki et al. [30] estimated carbons stocks in Cambodian forest types, including bamboo, using four carbon pools—above-ground biomass, below-ground biomass, deadwood, and litter—by applying the Intergovernmental Panel on Climate Change (IPCC) method [31]. Attempts have also been made to study the natural distribution of bamboo in Cambodia using commercial remotely sensed data such as Phased Array type L-band Synthetic Aperture Radar [32] and Light Detection and Ranging [33]. Although these studies yielded some information about bamboo distribution, their methods are labor-intensive and costly in terms of computation power. The recent launch of Google Earth Engine (GEE) offers an enormous opportunity for time-series image acquisition and processing in a fast cloud-computing environment [34,35], which can be used to study the natural distribution of bamboo free of cost. GEE’s fast cloud-computing technology and its available remote sensing data, specifically Landsat and Sentinel satellite products, have been used for a wide range of applications at the local and global scales [13,36–38]. Recently, GEE has been extensively used for surface phenology mapping [15,39]. However, the literature is limited in studies that used the GEE cloud-computing platform for bamboo mapping using the vegetation phenology threshold approach. Furthermore, finding low-cost technologies for assessing carbon sequestration is important to support mitigation efforts in relation to the ongoing climate crisis [18,40]. The present study was designed to determine threshold values for naturally distributed bamboo using a GEE harmonic and regression model for Landsat 8 OLI and to map the bamboo distribution and related carbon stocks in Siem Reap Province in Cambodia for the years 2015, 2018, and 2019 using a phenology-based threshold classification method (PBTC) [13] and freely available Landsat 8 OLI and Sentinel-2 collections in GEE.

2. Study Materials and Methods

2.1. Study Area Siem Reap Province, Cambodia, was chosen for this study because it has all forest types found in Cambodia, except Mangrove forest. Siem Reap (Figure1) is one of the fastest-developing regions in Cambodia because of its proximity to the tourist attractions of the Angkor Wat complex and Tonle Sap Lake. Although tourism is an important income source for some local people, the majority still depend on forest products and agriculture for their livelihoods [41]. Bamboo grows naturally in Cambodia’s tropical climate. Cambodia has about 10 species in four genera of bamboo, namely, Bambusa, Arundinaria, Dendrocalamus, and Oxytenanthera. Bambusa has a more extensive distribution in the country as compared with Oxytenanthera and Arundinaria. Bamboo species such as Arundinaria ciliata, Arundinaria pusilla, and Bambusa blumeana grow beneath evergreen, semi-evergreen, and deciduous trees [42]. Several bamboo species in the region have commercial value as a material for construction and for making agricultural tools, handicrafts, and paper. The above three species, as well as Dendrocalamus spp., are used as food and for making kitchen utensils, matting, slat traps, and floats [42]. The major forest types are deciduous, evergreen, flooded forest, and bamboo. Dense bamboo stands are found in the northern part of Cambodia in evergreen and deciduous forests, around Tonle Sap Lake, and along stream banks [42]. Some ethnic groups rely heavily on bamboo products for their livelihoods, especially in the north and northeast regions, which include some parts of Siem Reap Province [42]. Bamboo remains widely distributed in the remaining forested area and continues to support rural areas. A recent study by Sasaki et al. [30] showed that Cambodia’s forest carbon stocks, Remote Sens. 2020, 12, 3109 4 of 23 Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 24 including bamboo, are are helping the government’s efforts efforts to reach their national emission reduction targets [30,,43]43].. In In 2018, 2018, the government included bamboo species as a major forest type [[43]43]..

Figure 1. MapMap of of the the study study area. area. Note: Note: Light Light green green points points indicate indicate phenology phenology sampling sampling points points for bamboo,for bamboo, evergreen, evergreen, and and rubber rubber plantations. plantations. The The yellow yellow box box indicates indicates th thee National National Park Park of of Phnom Kulen mountain. Source: Source: The The background background map map is is the bamboo distribution derived from Sentinel-2Sentinel-2 imagery in 2019 in this this study. study. The The provincial provincial and and district boundaries were were obtained from Open Development Cambodia [[44].44]. 2.2. Remote Sensing Data 2.2. Remote Sensing Data Google Earth Engine provides ready-to-use remote sensing data in an up-to-date library Google Earth Engine provides ready-to-use remote sensing data in an up-to-date library with with moderate to high-resolution imagery. To characterize vegetation phenology, we used 297 moderate to high-resolution imagery. To characterize vegetation phenology, we used 297 moderate- moderate-resolution 30 m Landsat 8 OLI top-of-atmosphere (TOA) collections from 2015 to 2018 (Table1). resolution 30 m Landsat 8 OLI top-of-atmosphere (TOA) collections from 2015 to 2018 (Table 1). Forty Forty Landsat 8 OLI collections that cover the study area of Siem Reap Province were sourced between Landsat 8 OLI collections that cover the study area of Siem Reap Province were sourced between December and February of 2015 and 2018 for bamboo threshold-based classification [13] (Table2). December and February of 2015 and 2018 for bamboo threshold-based classification [13] (Table 2). To To further explore vegetation threshold values for Sentinel-2 products, we accessed 422 Sentinel-2 further explore vegetation threshold values for Sentinel-2 products, we accessed 422 Sentinel-2 images from January to December 2019 (Table2). images from January to December 2019 (Table 2).

Table 1. Landsat 8 Operational Land Imager (OLI) collections used for assessing the phenological Table 1. Landsat 8 Operational Land Imager (OLI) collections used for assessing the phenological behavior of selected land cover categories. behavior of selected land cover categories.

Landsat 8 OLI PATH ROW 2015 2016 2017 2018 PATH ROW 2015 2016 2017 2018 126 50 17 16 16 15 126 50 17 16 16 15 51 21 20 18 16 127 5051 2021 2020 1918 16 18 127 5150 1720 2220 2219 18 20 Total Collections51 7517 7822 7522 20 69 Total Collections 75 78 75 69

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Table 2. Landsat OLI and Sentinel-2 image collections used in this study for phenology-based threshold classification (PBTC). Remote Sens. 2020, 12, 3109 5 of 23 Cloud-Free Month PATH/ROW Sentinel-2 Landsat 8 Number of and Year TILE Sentinel-2 Images/Tiles Table 2. Landsat OLI and Sentinel-2 image collections used in this study for phenology-based threshold December–February 126/50 classification (PBTC). L-8-OLI-TOA 20 2014–2015 127/51 Landsat 8 Number of Cloud-Free Month and Year PATH/ROW Sentinel-2 TILE December–February 126/50 Sentinel-2 Images/Tiles L-8-OLI-TOA 20 December–February 126/50 L-8-OLI-TOA 20 20172014–2015–2018 127/51127 /51 JanDecember–Februaryuary–December 48PUA, 48PUV, 126 48PVA,/50 SentinelL-8-OLI-TOA-2 442 20 2017–20182019 48PVV 127 /51 January–DecemberTotal 2019 48PUA, 48PUV, 48PVA, 48PVV Sentinel-2482 442 Total 482 Note: OLI is Landsat Operational Land Imager 8, and TOA is top-of-atmosphere; 48PUA, 48PUV, Note: OLI is Landsat Operational Land Imager 8, and TOA is top-of-atmosphere; 48PUA, 48PUV, 48PVA, and 48PVV48PVA, are and Sentinel-2 48PVV Military are Sentinel Grid Reference-2 Military System Grid (MGRS) Reference tiles inSystem the study (MGRS) region. tiles in the study region.

2.3.2.3.Methodology Methodology FigureFigure2 illustrates 2 illustrates the methodological the methodological framework framework of selected of vegetation selected phenological vegetation phenological assessment andassessment phenology-based and phenology threshold-based classification threshold classification methods for methods bamboo for mapping bamboo ma andpping bamboo and bamboo carbon stockcarbon assessment. stock assessment.

FigureFigure 2. 2. MethodologicalMethodological framework framework of of phenology-based phenology-based threshold threshold classification classification toto identify identify the the naturalnatural distribution distribution ofof bamboo bamboo forest. forest. GEE,GEE, GoogleGoogle EarthEarth Engine;Engine;EVI, EVI, enhancedenhanced vegetationvegetation index;index; EOS,EOS, end end of of season; season; SOS,SOS, start start of of season; season; LSP, LSP, leaf-shedding leaf-shedding phenology phenology stage; stage; LFP, LFP, leaf-flushing leaf-flushing phenologyphenology stage. stage.

Here,Here,we we used used JavaScript JavaScript programmingprogramming ininGEE GEEto to collect collect time-series time-series ofof LandsatLandsat 88 OLI OLI image image collectionscollections from from 2015 2015 to to 2018 2018 and and applied applied cloud cloud mask mask functions functions (less than(less 45%)than to45%) exclude to exclude cloud pixels cloud priorpixels to prior the assessment to the assessment of land use of classificationland use clas (Figuresification2, Step(Figure 1) [ 102, ,Step34,37 ,1)45 [10,3,46].4 We,37 then,45,46 applied]. We then an imageapplied compositing an image compositing and reducer and function reducer to select function the mostto select recent the pixelmost inrec theent collections. pixel in the collections. WeWe appliedapplied a composite composite median median reducer reducer function function to tocalculate calculate the themedian median value value of each of image each imagecollection. collection. The median The median reducer reducer function function removes removes clouds clouds in the in image, the image, which which have have high high values values,, and andshadows shadows,, which which have have low low values values [35,4 [357],.47 The]. Theoutput output composite composite value value is the is median the median in each in each band band over overtime time [34,46 [34]., 46We]. applied We applied a clip a clipfunction function to subset to subset the theimage image collections collections with withinin the thestud studyy region region and andthen then calculated calculated the theEVI EVI using using Equation Equation (1) ( (1)Figure (Figure 2, Step2, Step 1) (Appendix 1) (Appendix A)A: ):

(NIR(퐍퐈퐑 −RED퐑퐄퐃) ) 퐄퐕퐈EVI==퐆G× − (1(1)) ×(퐍퐈퐑(NIR++퐂ퟏC1 ×RED퐑퐄퐃 −C2퐂ퟐBLUE× 퐁퐋퐔퐄+ L)+ 퐋) × − × where G = 2.5 (gain factor) [48–50]; C1 = 6 and C2 = 7.5 (aerosol correction factors); L = 1 (canopy background adjustment factor) [48–50]; and NIR (near infrared) is Landsat band 5 and

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where G = 2.5 (gain factor) [48–50]; C1 = 6 and C2 = 7.5 (aerosol correction factors); L = 1 (canopy background adjustment factor) [48–50]; and NIR (near infrared) is Landsat band 5 and Sentinel-2 band 8, RED is band 4 in both Landsat and Sentinel-2, and BLUE is band 2 in both Landsat and Sentinel-2 [49]. In assessing the vegetation phenological profiles of selected land cover classes, we applied a time-series analysis for EVI by adapting harmonic and regression model JavaScript algorithms from our recent study [13] (Figure 2, Step 2). We selected 65 geometry locations (Figures 1 and 3D), including 30 for bamboo forest, 30 for evergreen forest, and 5 for rubber plantations, where no large Remoteland Sens. cover2020 changes, 12, 3109 had occurred during the phenological analysis period (2015 to 2018). As6 of we 23 observed during the field survey in 2019, the larger portion of bamboo distribution was found in and Sentinel-2around the band Kulen 8, RED Mountain is band area, 4 in bothincluding Landsat Varin, and Banteay Sentinel-2, Seri, and and BLUE Svay is Leu band Districts 2 in both, and Landsat in the andnorthern Sentinel-2 part [of49 Chi]. Kreng District (Figure 1). Thus, we selected 10 sites with a large homogeneous extentIn assessing of bamboo the around vegetation the phenological Kulen Mountain profiles for of creating selected theland geometry cover classes, for analyzing we applied th ae time-seriesphenological analysis behavior for EVI of bamboo by adapting. harmonic and regression model JavaScript algorithms from our recentFor study the [13rubber] (Figure plantation2, Step 2).samples, We selected we created 65 geometry four geometry locations (Figureslocations1 andin Chi3D), Kraeng including District 30 , fortwo bamboo in the northern forest, 30 part for of evergreen Varin, two forest, in Angkor and 5 for Thom rubber, and plantations, two locations where in Banteay no large Srei land Districts. cover changesFor evergreen had occurred forest during geometries, the phenological we created analysis three in period the Angkor (2015 to 2018). Wat region, As we observed where there during are thepermanent field survey sampling in 2019, plots the largerfor evergreen portion offorest bamboo [51], distribution and seven geometries was found inwere and created around within the Kulen the Mountainstudy area area, (Figure including 3). Eventually, Varin, Banteay we generated Seri, and the Svay time Leu-series Districts, and andfitted in and the northernoriginal EVI part profiles of Chi Krengfor each District selected (Figure vegetation1). Thus, we category selected to 10 assess sites with phenolog a largeical homogeneous behaviors extent[52] and of bamboo determine around the thethreshold Kulen Mountain values [13] for from creating Landsat the geometry 8 OLI collections for analyzing during the phenologicalthe leaf-shedding behavior phenology of bamboo. stage (December to March) from 2015 to 2018 (Figure 2, Step 2).

A B

C D

Figure 3. Example of original and fitted OLI index time-series profile of evergreen forest and bamboo Figure 3. Example of original and fitted OLI index time-series profile of evergreen forest and bamboo between 2015 and 2018 generated by using harmonic regression and a time-series model. (A) is the between 2015 and 2018 generated by using harmonic regression and a time-series model. (A) is the GEEGEE JavaScript JavaScript code code window, window, ( B(B)) is is the the EVI EVI time-series time-series graph, graph, ( C(C)) is is the the geometry geometry inputs inputs for for selected selected forestforest land land cover cover categories categories and and ( D(D)) shows shows the the map map window window in in GEE. GEE.

ForIn Figure the rubber 3A, plantation is the code samples, window we for created the GEE four time geometry-series indices locations and in Chiharmonic Kraeng regression District, twoJavaScript in the northern code developed part of Varin, in GEE two to in Angkorassess the Thom, phenolog and twoical locationsbehaviors in of Banteay selected Srei vegetation Districts. Forcategories; evergreen Figure forest 3 geometries,B is the graphical we created window three showing in the Angkor the harmonic Wat region, and where time there-series are phenolog permanentical samplingbehaviors plots of evergreen, for evergreen bamb forestoo, and [51 ],rubber and seven vegetation geometries’s fitted were (blue created line) and within original the study (red dots) area (Figurevegetation3). Eventually, index values we generatedfrom 2015 the to time-series2018; Figure and 3C fitted shows and geometry original EVIinputs profiles of selected for each vegetation selected vegetationcategories; category Figure to 3D assess is the phenological map window behaviors showing [52 ] the and geometry determine of the evergreen threshold (green), values [ 13 bamb] fromoo Landsat(yellow) 8, OLIand collectionsrubber plantation during the(purple) leaf-shedding in Siem Reap phenology Province. stage The (December background to March) image from is Landsat 2015 to 8 2018OLI (Figurecomposite2, Step EVI 2). data in GEE. In Figure3A, is the code window for the GEE time-series indices and harmonic regression JavaScript code developed in GEE to assess the phenological behaviors of selected vegetation categories; Figure3B is the graphical window showing the harmonic and time-series phenological behaviors of evergreen, bamboo, and rubber vegetation’s fitted (blue line) and original (red dots) vegetation index values from 2015 to 2018; Figure3C shows geometry inputs of selected vegetation categories; Figure3D is the map window showing the geometry of evergreen (green), bamboo (yellow), and rubber plantation (purple) in Siem Reap Province. The background image is Landsat 8 OLI composite EVI data in GEE. Google Earth Engine generates EVI time-series profile datasets in a comma-separated values file format (CSV). Using the GEE export option, we exported 25 (10 evergreen, 10 bamboo, and five rubber Remote Sens. 2020, 12, 3109 7 of 23 plantations) CSV profiles and used the Pivot Table function of Microsoft Excel to obtain the average EVI values per year and month over four years, from 2015 to 2018 (Figure2, Step 3). The time-series analysis of fitted vegetation indices allowed us to quantify the phenological behaviors of bamboo, rubber, and evergreen forest categories. To do this, we first assessed the fitted vegetation index profiles derived from the time-series harmonic regression model using compressed (15-day intervals) Landsat 8 OLI collections. We then calculated the average of the mean fitted vegetation index values by creating 120 phenological profiles for selected land use categories (2015, 2016, 2017, and 2018) during the leaf-flushing phenology stage (LFP) at the start of the season (SOS) and the leaf-shedding phenology stage (LSP) at the end of the season (EOS) over four years. Previous phenological studies showed that fitted index values have higher accuracy in determining the vegetation threshold values for mapping during the LSP stage, locally called EOS [13]. We selected the fitted EVI profiles and assessed the phenology-based vegetation index values from November to April (EOS) or during the LSP season to determine the separation of bamboo index profiles from evergreen forests and rubber plantations from 2015 to 2018. We then created 12 average-fitted EVI phenological profiles for the three land use categories by year to estimate the average EVI profiles and determine the threshold values for bamboo forest, evergreen forest, and rubber plantation and to classify bamboo distribution in the region during the mid-dry season (Figure2, Step 3). Because the purpose of this study was to determine the natural distribution of bamboo forest, we did not consider the vegetation index values for other land cover categories in this study, which fell below the evergreen forest threshold values. Since the resulting Landsat 8 OLI phenology threshold values did not match the Sentinel-2 composite EVI for the selected land use categories, to classify the Sentinel-2 composite EVI data by using the PBTC method, we assigned Min = 0.6712 and Max = 0.7760 for bamboo, 0.6695–0.6623 for rubber, and 0.555–0.660 for the evergreen forest category by modifying Landsat Thematic Mapper (TM) threshold values from our previous phenological study [13]. We used JavaScript to build the PBTC algorithm [13] for classifying the median EVI data from composite Landsat OLI and Sentinel-2 images. As depicted in Figure2 (Step 4), the steps used for the composite image and pixel-based threshold mapping of individual land cover categories were as follows:

Imagery preprocessing and time-series collections were filtered for the dry mid-phenology season • (from December to February). Image filter functions were applied to select image collections in the particular phenological • period within the study region [13]. To develop the mapping function for EVI data, the GEE image reducer and median function was • used to form composite time-series images to obtain a median EVI [35,53]. The PBTC function was applied to the bamboo forest, evergreen forest, and rubber • plantation categories.

The final PBTC maps were used to map the bamboo distribution and calculate the bamboo carbon stocks in Siem Reap Province (Figure4). Remote Sens. 20202020, 1212, 3109x FOR PEER REVIEW 8 of 24 23

A

B

Figure 4. Example result of Sentinel-2 imagery phenology-based threshold classified map. Legend: Figure 4. Example result of Sentinel-2 imagery phenology-based threshold classified map. Legend: (A) Code window showing the PBTC functions developed in GEE. (B) Map window that represents (A) Code window showing the PBTC functions developed in GEE. (B) Map window that represents the 10 m resolution Sentinel-2 land use map in 2019. Bamboo forest appears in yellow (inset legend). the 10 m resolution Sentinel-2 land use map in 2019. Bamboo forest appears in yellow (inset legend). The background image is GEE Google Earth imagery. The background image is GEE Google Earth imagery.

2.2.4.4. Accuracy Accuracy Assessment Assessment Google Earth higher higher-resolution-resolution imagery (VHR) is free of cost and can be used directly as a base source for validat validatinging land use use,, land cover map mapss [ 54] because it provides the most accurate data for selecting the the most most appropriate appropriate reference reference points points that that are are very very important important for for validation validation.. The The images images are updatedare updated whenever whenever new new image imagess become become available. available. Depending Depending on on the the sensor, sensor, the the image image resolution ranges fromfrom 3030 mm to to 15 15 cm. cm Utilizing. Utilizing the the time-lapse time-lapse feature feature in Googlein Google Earth Earth Pro allowsPro allows the user the touser view to viewzoomable zoomable images images as far as back far back as 30 as years, 30 years, which which is ideal is ideal for validating for validat landing land use, use land, land cover cover maps maps [55] [5and5] and performing performing investigation investigation and and preliminary preliminary studies studies with with suitable suitable accuracy accuracy[ [5566,57,57]] byby applying recognized protocols protocols,, as recommended by [[5588].]. Here Here,, a stratified stratified random sampling technique was employed for for the the validation validation of phenology-basedof phenology-based threshold threshold classified classified maps. maps. From theFrom number the number of random of randompoints generated points generated in ArcGIS, in a total ArcGIS, of 1500 a total accuracy of 1500 points accuracy were selected points within were selectedthe evergreen within forest, the ebamboo,vergreen and forest rubber, bamboo plantation, and landrubber cover plantation categories. land We cover excluded categories. the random We excluded points that the fell random within pointsthe water that and fell otherwithin land the coverwater categories.and other land Because cover the categor spatialies distribution. Because the of spatial bamboo distribution and rubber of bambooplantation and areas rubber is smaller plantation than area thats ofis thesmaller evergreen than that forest of the and evergreen other land forest cover and classes, other we land selected cover classrandomes, reference we selected points random within thereference three forest points categories within to the acquire three a considerable forest categories number to ofacquire reference a considerablepoints for the number bamboo of and reference rubber plantation points forcategory the bamboo areas and to better rubber assess plantation mapping category accuracy areas [59 ].to betterPBTC assess maps mapping were accuracy exported [59 to]. Google Drive using the export function in GEE, and we used thesePBTC maps maps in ArcGIS were exported to transform to Google land cover Drive labels using intothe export 1500 accuracy function points in GEE, by and applying we used ArcGIS these mapsspatial-join in ArcGIS and to related transform tools. land We thencover used label theses into accuracy 1500 accuracy points points in Google by applying Earth Pro ArcGIS to verify spatial and- joinvalidate and therelated 1500 tools. points We for then Landsat used OLIthese (2015 accuracy and 2018)point ands in Google Sentinel-2 Earth (2019) Pro mapsto verify (Figure and5 validate). the 1500These points accuracy for Landsat points wereOLI (2015 compared and 2018) to the and matching Sentinel land-2 (2019) cover maps labels (Figure using the 5). VHR imagery in Google Earth Pro. The percentage of agreement was recorded in an accuracy table, and classification accuracy was assessed using a confusion matrix in Microsoft Excel. We then calculated overall accuracy, producer’s accuracy, user’s accuracy, and kappa coefficients from the confusion matrix [13,58] for PBTC maps.

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Figure 5. Imported reference points in Google Earth Pro for PBTC map accuracy assessment. Figure 5. Imported reference points in Google Earth Pro for PBTC map accuracy assessment.

2.5. BambooThese Carbon accuracy Stock points Assessment were compared to the matching land cover labels using the VHR imagery in Google Earth Pro. The percentage of agreement was recorded in an accuracy table, and classificationTo implement accuracy the REDDwas assessed+ scheme, using Cambodia a confusion employs matrix in a Microsoft national Excel. definition We then of forest calculated that is consistentoverall accuracy, with the Globalproducer’s Forest accuracy, Resources user’s Assessment accuracy, and [60] kappa and considers coefficients bamboo from the to be confusion one of the mainmatrix forest [13,5 categories8] for PBTC for maps. net carbon sequestration from land use, land use change, and forestry [60]. Conventionally, forest inventories are used for measuring, reporting, and verification (MRV) of deforestation2.5. Bamboo andCarbon forest Stock degradation Assessment in the tropics to provide the needed information for establishing baselineTo emissions implement against the REDD+ which mitigationscheme, Cambodia measures employs and performance a national can definition be assessed of forest [61 –that63]. is consistentEquation with (2) the was Global applied Forest for Resources calculating Assessment carbon stocks [60] and in consider the existings bamboo bamboo to be areasone of and the is presentedmain forest below categories [30,60]: for net carbon sequestration from land use, land use change, and forestry [60]. Conventionally, forest inventories are used forX12 measuring, reporting, and verification (MRV) of TCS = BF CS (2) deforestation and forest degradation in the tropics to providei × the needed information for establishing i=1 baseline emissions against which mitigation measures and performance can be assessed [61–63]. where TCSEquationis the total(2) was carbon applied stocks for of calculating bamboo forest carbon in 2015, stocks 2018, in the and existing 2019; BFi, bamboois the area areas of and bamboo is forestpresented in district belowi in [3 Siem0,60]: Reap Province at any given time; and CS is the carbon stocks in bamboo forest in the respective years (i.e., 2015, 2018,12 or 2019). Sasaki et al. [30] estimated the bamboo carbon stocks in Cambodia to be 57.4 Mg C ha 1 [64] for all pools except organic soil carbon. Here, we used 푇퐶푆− = ∑ 퐵퐹 × 퐶푆 1)( 2) the same value to calculate the bamboo carbon stocks푖 in 12 districts in Siem Reap Province in 2015, 푖=1

2018, and 2019 (Figure2, Step 5) [30,60]. whereIf natural TCS is bamboothe total forestscarbon stocks are completely of bamboo cleared, forest in total 2015, emissions 2018, and from2019; suchBFi, is clearing the area wereof estimatedbamboo usingforest in district i in Siem Reap Province at any given time; and CS is the carbon stocks in bamboo forest in the respective years (i.e., 2015, 2018, or 442019). Sasaki et al. [30] estimated the bamboo CE = TCS (3) carbon stocks in Cambodia to be 57.4 Mg C ha−1 [64] for× all12 pools except organic soil carbon. Here, we used the same value to calculate the bamboo carbon stocks in 12 districts in Siem Reap Province in where CE is carbon emission (Mg CO2), TCS is the total bamboo carbon stock at any given time of 2015, 2018, and 2019 (Figure 2, Step 5) [30,60]. clearing (in 2015, 2018, and 2019) in Equation (2), and 44/12 is the molecular weight ratio of carbon If natural bamboo forests are completely cleared, total emissions from such clearing were dioxide to carbon [30,65]. estimated using On the other hand, if all these natural bamboo forests are fully protected and the avoided carbon emissions (ACE) are used to offset the carbon44 emissions from tourists visiting the province, 퐶퐸 = 푇퐶푆 × (3) the contribution of bamboo forest to climate change mitigation12 was estimated using

where CE is carbon emission (Mg CO2), TCS is the total bamboo carbon stock at any given time of ACE clearing (in 2015, 2018, and 2019) in EquationCOR = (2), and 44/12100 is the molecular weight ratio of carbon (4) T EP × dioxide to carbon [30,65]. ×

Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 24

On the other hand, if all these natural bamboo forests are fully protected and the avoided carbon emissions (ACE) are used to offset the carbon emissions from tourists visiting the province, the contribution of bamboo forest to climate change mitigation was estimated using Remote Sens. 2020, 12, 3109 퐴퐶퐸 10 of 23 퐶푂푅 = 푋 100 (4) 푇 × 퐸푃 wherewhere CORCOR isis carboncarbon ooffsettingffsetting raterate (%),(%), TT isis thethe totaltotal numbernumber ofof touriststourists visitingvisiting Siem Reap in 20152015 (2,100,000)(2,100,000) andand 20182018 (2,200,000)(2,200,000) [[6666].]. Due to the lack of data in 2019, tourist-basedtourist-based emissions for 2019 werewere notnot includedincluded inin ourour carboncarbon ooffsettingffsetting calculation,calculation, EP isis thethe tourist’stourist’s perper capitacapita emissionsemissions forfor traveling to to and and staying staying in Siemin Siem Reap Reap (4.14 (4.14 Mg CO Mg2/person) CO2/person [67]; ACE) [67is]; theACE avoided is the carbon avoided emissions. carbon Ifemissions. 100% is avoided, If 100% is ACE avoided,= CE. ACE = CE.

3.3. Results 3.1. Phenological Profiles and Threshold Values 3.1. Phenological Profiles and Threshold Values The time-series analysis of fitted vegetation indices allowed us to quantify the differences in The time-series analysis of fitted vegetation indices allowed us to quantify the differences in phenological behavior between bamboo, rubber, and evergreen forest land cover categories, as shown phenological behavior between bamboo, rubber, and evergreen forest land cover categories, as in Figures6–8. shown in Figures 6–8.

0.90

Leaf-flushing phenology (LFP) Leaf-shedding phenology (LSP) 0.85

0.80 Amplitude Phase 0.75

0.70

2015 2016 SOS 0.65 EOS Phase of SOS and EOS 2017 2018 0.60 May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

Figure 6. Fitted phenological profile of Landsat 8 OLI EVI composite data for bamboo forest. Remote Sens.Figure 2020, 6.12,Fitted x FOR phenologicalPEER REVIEW profile of Landsat 8 OLI EVI composite data for bamboo forest. 11 of 24 In Figure 6, the yearly average index profiles were generated by analyzing 40 profiles from 10 0.90 geometry locations between 2015 and 2018. The highlighted blue color shows the LFP stage and indicates the local startLeaf--flushingof-season phenology (SOS) (LFP) and end-of-season (EOS) periodsLeaf-shedding from phenology May to (LSP)November. 0.85 The dashed blue lines indicate the SOS and EOS phases. The blue arrow and black dashed line show the leaf-shedding phenological stage and the amplitude of the minimum and maximum bamboo 0.80 index profiles from December to April.

0.75 Amplitude Phase

0.70

SOS EOS 0.65 2015 2016 Phase of SOS and EOS 2016 2018 0.60 May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

FigureFigure 7. FittedFitted mean mean vegetation vegetation index index profiles profiles of Landsat of Landsat 8 OLI 8 EVI OLI composite EVI composite data for data rubber for rubberplantation plantation.. Average Average index profiles index profileswere generated were generated by assessing by assessing 40 profiles 40 from profiles 10 geometry from 10 geometry locations locationsof the bamboo of the category bamboo categorybetween between2015 and 20152018. and 2018.

0.80

0.75 Leaf-shedding phenology (LSP) Leaf-flushing phenology (LFP)

0.70

Amplitude Phase 0.65

0.60

2015 2016 SOS

0.55 EOS 2017 2018 Phase of SOS and EOS 0.50 May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

Figure 8. Fitted EVI profile of Landsat 8 OLI EVI composite data for evergreen forest. The average vegetation index profiles were generated from 10 geometry locations by assessing 40 profiles from 2015 to 2018.

Figure 6 (bamboo), Figure 7 (rubber), and Figure 8 (evergreen) show the overlapping of average- fitted vegetation index profiles during SOS and EOS and the regular fluctuation of average EVI profiles of bamboo, rubber, and evergreen forest during the LSP stage (from December to April) in all 30 of the selected geometry locations from 2015 to 2018. The most distinguishable differences in the three vegetation cover indices were apparent in the LSP stage. The bamboo average-fitted vegetation index values were Min = 0.71 and Max = 0.83 from 2015 to 2017 and Min = 0.71 and Max = 0.84 in the LFP stage in 2018 (Figure 6). The fitted values for evergreen in the LSP stage reveal lower vegetation index values than those for bamboo in all selected phenology years (Figure 8). We found that evergreen had fitted average index values of Min = 0.63 and Max = 0.70 in the LFP stage from 2015 to 2018. The rubber plantation values (Min = 0.75 and Max = 0.80) were higher than the bamboo and evergreen forest values in the SOS to EOS season, and its vegetation index values were lower than bamboo and evergreen values during the LSP season (Figure 7). However, during the start of the LSP stage, bamboo vegetation index values (Max = 0.86 and Min = 0.83 from November to January) were greater than those of evergreen (Max = 0.71 and Min = 0.69) and rubber (Max = 0.80 and Min = 0.76) in the overall SOS and EOS or LFP period. The separation of fitted indices of bamboo, rubber, and evergreen phenological profiles in the mid-LSP stage is shown in Figure 9.

Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 24

0.90

Leaf-flushing phenology (LFP) Leaf-shedding phenology (LSP) 0.85

0.80

0.75 Amplitude Phase

0.70

SOS EOS 0.65 2015 2016 Phase of SOS and EOS 2016 2018 0.60 May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

Figure 7. Fitted mean vegetation index profiles of Landsat 8 OLI EVI composite data for rubber Remoteplantation Sens. 2020,.12 A,verage 3109 index profiles were generated by assessing 40 profiles from 10 geometry locations11 of 23 of the bamboo category between 2015 and 2018.

0.80

0.75 Leaf-shedding phenology (LSP) Leaf-flushing phenology (LFP)

0.70

Amplitude Phase 0.65

0.60

2015 2016 SOS

0.55 EOS 2017 2018 Phase of SOS and EOS 0.50 May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

Figure 8.8. FittedFitted EVI profileprofile ofof LandsatLandsat 88 OLIOLI EVIEVI compositecomposite datadata forfor evergreenevergreen forest.forest. TheThe averageaverage vegetationvegetation indexindex profiles profiles were were generated generated from from 10 10 geometry geometry locations locations by assessingby assessing 40 profiles 40 profil fromes from 2015 to2015 2018. to 2018.

InFigure Figure 6 (6bamboo, the yearly), Figure average 7 (rubber index), and profiles Figure were 8 (evergreen generated) byshow analyzing the overlapping 40 profiles of fromaverage 10- geometryfitted vegetation locations index between profiles 2015 during and 2018. SOS andThe EOShighlighted and the blue regular color fluctuation shows the of LFP average stage and EVI indicatesprofiles of the bamboo, local start-of-season rubber, and evergreen (SOS) and forest end-of-season during the (EOS)LSP stage periods (from from December May to to November. April) in Theall 30 dashed of the blueselected lines geometry indicate the locations SOS and from EOS 2015 phases. to 2018. The The blue most arrow distinguishable and black dashed differences line show in thethe leaf-sheddingthree vegetation phenological cover indices stage and were the apparent amplitude in of the the L minimumSP stage. and The maximum bamboo bambooaverage- indexfitted profilesvegetation from index December values towere April. Min = 0.71 and Max = 0.83 from 2015 to 2017 and Min = 0.71 and Max = 0.84 inFigure the LFP6 (bamboo), stage in 2018 Figure (Figure7 (rubber), 6). The andfitted Figure values8 for(evergreen) evergreen show in the theLSP overlapping stage reveal lower of average-fittedvegetation index vegetation values than index those profiles for bamboo during SOSin all and selected EOS andphenology the regular years fluctuation (Figure 8). ofWe average found EVIthat profilesevergreen ofbamboo, had fitted rubber, average and index evergreen values forest of Min during = 0.63 the and LSP Max stage = (from 0.70 in December the LFP tostage April) from in all2015 30 to of 2018. the selected The rubber geometry plantation locations values from (Min 2015 = to0.75 2018. and The Max most = 0.80) distinguishable were higher dithanfferences the bamboo in the threeand evergreen vegetation forest cover values indices in were the apparentSOS to EOS in theseason LSP, stage.and its The vegetation bamboo average-fittedindex values were vegetation lower indexthan b valuesamboo were and e Minvergreen= 0.71 values and Max during= 0.83 the from LSP 2015 season to 2017(Figure and 7 Min). = 0.71 and Max = 0.84 in the LFP stageHowever, in 2018 during (Figure the6). start The of fitted the valuesLSP stage, for evergreen bamboo vegetation in the LSP index stage revealvalues lower (Max vegetation= 0.86 and indexMin = values0.83 from than November those for bamboo to January) in all were selected greater phenology than those years of (Figure evergreen8). We (Max found = 0.71 that evergreenand Min = had0.69) fitted and rubber average (Max index = 0.80 values and Min of Min = 0.76)= 0.63 in the and overall Max SOS= 0.70 and in EOS the or LFP LFP stage period. from The 2015 separation to 2018. Theof fitted rubber indices plantation of bamboo, values (Min rubber= 0.75, and and evergreen Max = 0.80) phenological were higher profiles than the in bamboo the mid and-LSP evergreen stage is forestshown values in Figure in the 9. SOS to EOS season, and its vegetation index values were lower than bamboo and evergreen values during the LSP season (Figure7). However, during the start of the LSP stage, bamboo vegetation index values (Max = 0.86 and Min = 0.83 from November to January) were greater than those of evergreen (Max = 0.71 and Min = 0.69) and rubber (Max = 0.80 and Min = 0.76) in the overall SOS and EOS or LFP period. The separation of fitted indices of bamboo, rubber, and evergreen phenological profiles in the mid-LSP stage is shown in Figure9. The cumulative average phenology profile of land use categories (Figure9), namely, bamboo, rubber, and evergreen, were assessed by validating 120 index profiles during the LSP and LFP stages. Comparing the phenology-based index profiles revealed that bamboo had greater index values over the entire LSP period from 2015 to 2018. The amplitude of bamboo minimum and maximum peak values (0.860–0.863) occurred at the end of the LSP stage (December–February), and the lowest index values (0.707–0.716) were found at the start of the rainy season (SOS) in May–July. The rubber plantation vegetation index values (0.784–0.798) were greater than the bamboo values (0.707–0.716) in the SOS (monsoon/rainy season) from May to September, and they were slightly lower (0.741–0.748) right after the start of the mid-LSP stage (January–April). However, the bamboo and rubber vegetation index profiles overlapped in the EOS season between September and October, Remote Sens. 2020, 12, 3109 12 of 23 as shown in Figure9. The evergreen phenological values and the phase of amplitudes are smaller than those for bamboo and rubber in all phenological stages. Therefore, classification confusion of evergreen forest and that of bamboo or rubber is not likely to occur. Bamboo was found to have the highest vegetation index in the mid-dry phenological phase. Amplitudes of the minimum and maximum threshold values were determined (0.854–0.882) and are shown in Figure 10. Similarly, rubber plantations showed the highest vegetation index, but the range of the threshold amplitude (0.815–0.841) between bamboo and evergreen forest was smaller (0.581–0.648) (Figure 10). Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 24

0.90 Leaf-flushing phenology (LFP) 0.85 0.80 0.75 0.70

Values 0.65 Leaf-shedding phenology (LSP) 0.60

May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr Vegetation Index Fitted Mean FittedIndex Vegetation Time of Year Bamboo Rubber Evergreen

Figure 9. Time-series average index values of bamboo, rubber, and evergreen forest categories during Figure 9. Time-series average index values of bamboo, rubber, and evergreen forest categories during the middle LSP season from 2015 to 2018. The average phenology index values were generated by the middle LSP season from 2015 to 2018. The average phenology index values were generated by validating 120 index profiles. The box surrounding the profiles indicates the phase of separation of validating 120 index profiles. The box surrounding the profiles indicates the phase of separation of the EVI profile of the three vegetation categories during the middle-leaf-shedding phenology season. the EVILocally, profile May of– theOctober three is vegetation the rainy season categories (LFP stage during), and the November middle-leaf-shedding–April is the dry phenology season (LSP season. Locally,stage). May–October is the rainy season (LFP stage), and November–April is the dry season (LSP stage). Remote Sens. 2020, 12, x FOR PEER REVIEW 13 of 24 The cumulative average phenology profile of land use categories (Figure 9), namely, bamboo, rubber0.95, and evergreen, were assessed by validating 120 index profiles during the LSP and LFP stages. Comparing the phenology-based index profiles revealed that bamboo had greater index values over the entire0.9 LSP period from0.882 2015 to 2018. The amplitude of bamboo minimum and maximum peak values (0.860–0.863) occurred at the end of the LSP stage (December–February), and the lowest index 0.85 0.841 values (0.707–0.716) were found0.854 at the start of the rainy season (SOS) in May–July. The0.8 rubber plantation vegetation index values0.815 (0.784–0.798) were greater than the bamboo values (0.707–0.716) in the SOS (monsoon/rainy season) from May to September,0.769 and they were slightly0.75 lower (0.741–0.748) right after the start of the mid-LSP stage (January–April). However, the bamboo and rubber vegetation index profiles overlapped in the EOS season between SeptemberMin and 0.7 October, as shown in Figure 9. The evergreen phenological values and the phase of amplitudesMax are smaller0.65 than those for bamboo and rubber in all phenological stages. Therefore,0.652 classification confusion of evergreen forest and that of bamboo or rubber is not likely to occur.

Bamboo0.6 was found to have the highest vegetation index in the mid-dry phenological phase. Vegetation Index Mean ThresholdValues MeanIndex Vegetation Amplitudes0.55 of the minimum and maximum threshold values were determined (0.854–0.882) and are shown in Figure 10. Similarly, rubber plantations showed the highest vegetation index, but the range of the threshold0.5 amplitude (0.815–0.841) between bamboo and evergreen forest was smaller (0.581– 0.648) (Figure 10). Bamboo Rubber Evergreen Dec-Feb Dec-Feb Dec-Feb

Figure 10. Minimum and maximum threshold values for bamboo forest, rubber plantation, and Figure 10. Minimum and maximum threshold values for bamboo forest, rubber plantation, evergreen forest determined using Landsat 8 OLI EVI time-series data during the mid-LSP stage from and evergreen forest determined using Landsat 8 OLI EVI time-series data during the mid-LSP 2015 to 2018. stage from 2015 to 2018. 3.2. Natural Distribution of Bamboo Forest Using the obtained threshold values of selected land cover categories, we were able to derive the natural bamboo distribution for the entire Siam Reap Province in 2015 (Figure 11), 2018 (Figure 12), and 2019 (Figure 13). The PBTC map of land use in Siem Reap in 2015 and 2018 (Figures 11 and 12) represents the bamboo, rubber, evergreen, and other land cover distributions. Evergreen forest is widely distributed around Kulen Mountain and the Angkor Wat temple. Bamboo distribution was found largely in the mountain region, and some patches can be observed around the Angkor Wat temple. Rubber distribution can be seen in only a small area (Figure 11C) in 2015, whereas the increase in rubber is clearly seen in 2018 (Figure 12C).

Remote Sens. 2020, 12, 3109 13 of 23

3.2. Natural Distribution of Bamboo Forest Using the obtained threshold values of selected land cover categories, we were able to derive the natural bamboo distribution for the entire Siam Reap Province in 2015 (Figure 11), 2018 (Figure 12), and 2019 (Figure 13). The PBTC map of land use in Siem Reap in 2015 and 2018 (Figures 11 and 12) represents the bamboo, rubber, evergreen, and other land cover distributions. Evergreen forest is widely distributed around Kulen Mountain and the Angkor Wat temple. Bamboo distribution was found largely in the mountain region, and some patches can be observed around the Angkor Wat temple. Rubber distribution can be seen in only a small area (Figure 11C) in 2015, whereas the increase in rubber is clearly seen in 2018 (Figure 12C). In Figure 11A, the map represents the distribution of bamboo around Kulen Mountain and Kulen National Park. Figure 11B (Boeng Peae Wildlife Sanctuary) shows the distribution of bamboo in natural evergreen forests in the northwestern part of the study region, including Chi Kraeng District. Figure 11C shows a mix of vegetation of evergreen forest (green) and rubber (purple) in Chi Kraeng District. Figure 11D shows the rich evergreen forest cover and small bamboo distribution around the Angkor Wat temple.Remote Sens. 2020, 12, x FOR PEER REVIEW 14 of 24

FigureFigure 11. Maps11. Map showings showing the the bamboo bamboo distribution distribution in in Siem Siem Reap Reap in in2015. 2015. Inset Inset maps maps show show the locations the locations of theof natural the natural bamboo bamboo distributions distribution arounds around the Kulenthe Kulen Mountain Mountain (A) and (A) Boeng and Boeng Peae WildlifePeae Wildlife Sanctuary (B) andSanctuary of dense (B evergreen) and of de forestsnse evergreen in southeastern forests in region southeastern of the Boeng region Peaeof the Wildlife Boeng SanctuaryPeae Wildlife (C ) and AngkorSanctuary Wat temple (C) and (D Angkor). Wat temple (D).

As shownIn Figure in 1Figure1A, the 13 map, bamboo represents distribution the distribution was higher of bamboo in 2019 around compared Kulen Mountain with that and in 2015, andKulen the distribution National Park. of Figure rubber 11 plantationsB (Boeng Peae can Wildlife be seen Sanctuary) clearly shows in Varin the distribution and Chi Kraeng of bamboo Districts (Figuresin natural 12C and evergreen 13C). Figure forest s13 in shows the northwestern a higher bamboo part of distribution the study region, area compared including Chi with Kraeng that in the District. Figure 11C shows a mix of vegetation of evergreen forest (green) and rubber (purple) in Chi Landsat OLI maps (2015 and 2018) in most of the study region. On the other hand, rubber plantation Kraeng District. Figure 11D shows the rich evergreen forest cover and small bamboo distribution distributionaround the can Angkor be seen Wat clearly temple. in Varin and Chi Kraeng Districts (Figure 13C). Our analysis found that the bamboo cover was 2674 ha in 2015, 2711 ha in 2018, and 2672 ha in 2019. A larger area of bamboo was in upland areas and along riverbanks [42], especially in Phnom Kulen National Park and the northern part of Chi Kraeng District.

Remote Sens. 2020, 12, 3109 14 of 23 RemoteRemote Sens.Sens. 20202020,, 1122,, xx FORFOR PEERPEER REVIEWREVIEW 1515 ofof 2424

FigureFigureFigure 12. Maps 12.12. MapMap showingss showingshowing the thethe bamboo bbambooamboo distribution distributiondistribution in in SiemSiem ReapReap Reap inin in 2018.2018. 2018. InsetInset Inset mapsmaps maps showshow show naturalnatural natural distributiondistributiondistribution of bamboo ofof bamboobamboo in inin KulenKulen MountainMountain Mountain ((AA))( andAand) andBoengBoeng Boeng PeaePeae WildlifeWildlife Peae Wildlife SanctuarySanctuary Sanctuary regionsregions ((BB)), regions, rrubberubber (B), rubberplantationplantation plantation inin southeasternsoutheastern in southeastern regionregion region ((CC)) andand (C e)evergreenvergreen and evergreen forestforest aroundaround forest the aroundthe AngkorAngkor the WatWat Angkor templetemple Wat ((DD) temple).. (D).

FigureFigureFigure 13. Maps 13.13. MMapap showingss showingshowing the thethe bamboo bamboobamboo distribution distributiondistribution usingusing thethe SentinelSentinel Sentinel-2--22 inin in SiemSiem Siem Reap,Reap, Reap, 2019.2019. 2019. InsetInset Inset mapsmaps maps showshowshow natural natural natural distribution distribution distribution of bamboo of of bamboo bamboo around aroundaround the Kulen the the KulenKulen Mountain Mountain Mountain (A) and ((AA)) Boeng andand Boeng Boeng Peae Wildlife Peae Peae Wildlife Wildlife Sanctuary areas (B), rubber plantation (C) and dense evergreen forest around the Angkor Wat temple (D). Remote Sens. 2020, 12, 3109 15 of 23

3.3. Accuracy Assessment Validation of the 2015 and 2018 maps using VHR imagery in Google Earth revealed that for bamboo classification, producer’s accuracy (PA) is 95.7% and user’s accuracy (UA) is 86.6% for 2015, with values of 97.8% (PA) and 87.9% (UA) for 2018. The Sentinel-2 map shows that the PA is 91.7% with a UA of 86.5% for 2019. The evergreen forest has a UA of 95.7%–89.7% and PA of 84.3%–89.9% (2015–2018). Rubber plantation PA is 93.6%–68.1% and UA is 81.5%–95.5% (2015–2018) for the Landsat 8 OLI maps. We found that the overall accuracy in VHR is 89.72%–89.56% (2015–2018) with a kappa coefficient of 0.83–0.82 for the Landsat OLI phenology-based threshold maps of 2015 and 2018 (Table3, Figure 14).

Table 3. User’s accuracy and producer’s accuracy assessment for classifying the bamboo, rubber, and evergreen forest in Siem Reap.

Forest Category Evergreen Rubber Bamboo User’s Accuracy Landsat OLI imagery in 2015 Evergreen 268 5 7 95.70%> Rubber 17 88 3 81.50% Bamboo 33 1 220 86.60% Producer’s accuracy 84.30% 93.60% 95.70% Overall Accuracy 89.72% Kappa 0.83 Landsat OLI imagery in 2018 Evergreen 286 29 4 89.70% Rubber 2 64 1 95.50% Bamboo 30 1 225 87.90% Producer’s accuracy 89.90% 68.10% 97.80% Overall Accuracy 89.56% Kappa 0.82 Sentinel-2 imagery for 2019 Evergreen 273 11 14 91.60% Rubber 7 88 5 88.00% Bamboo 28 5 211 86.50% Producer’s Accuracy 88.60% 84.60% 91.70% Overall Accuracy 89.10% Kappa 0.82 Note 1: Bold cells indicate the agreement of the accuracy metrics. Note 2: Total reference points 642. Remote Sens. 2020, 12, x FOR PEER REVIEW 17 of 24

Figure 14. Example of Landsat 8 PBTC 2018 map with 89% overall accuracy. Green indicates Figure 14. Example of Landsat 8 PBTC 2018 map with 89% overall accuracy. Green indicates evergreen forest, and yellow represents the distribution of bamboo. The background image is very evergreen forest, and yellow represents the distribution of bamboo. The background image is very high resolution (VHR) imagery in GEE. high resolution (VHR) imagery in GEE. 3.4. Area of Bamboo Forest and Its Carbon Stocks by Districts We estimated a total bamboo area of 2674 ha in 2015, 2711 ha in 2018, and 2672 ha in 2019. Carbon stocks in bamboo forest were estimated at 153,511; 155,583; and 153,367 Mg C in 2015, 2018, and 2019, respectively. Specifically, among the 12 districts, the Banteay Srei District had a higher amount of bamboo carbon stocks (47,500 Mg C) between 2015 and 2019, and Svay Leu and Varin Districts had more than 41,600 Mg C in the same period. Other districts covered smaller portions of bamboo, and their carbon stocks were less than 3000 Mg C (Table 4).

Table 4. Total carbon stocks and emission reductions in bamboo forests by districts in Siem Reap Province in 2015, 2018, and 2019.

2015 2018 2019 District Name Area Area (ha) (Mg C) Area (ha) (Mg C) (Mg C) (ha) Angkor Chum 26 1504 20 1137 20 1142 Angkor Thum 36 2061 18 1039 55 3134 Banteay Srei 826 47,395 862 49,479 831 47,682 Chi Kraeng 270 15,521 253 14,522 215 12,335 Kralanh 2 121 2 126 3 189 Puok 3 149 1 75 1 69 Prasat Bakong 3 178 3 189 5 258 Krong Siem Reab 20 1165 22 1246 30 1693 Soutr Nikom 14 798 8 448 53 3054 Srei Snam 11 603 2 86 1 63 Svay Leu 757 43,469 787 45,145 734 42,132 Varin 706 40,547 733 42,091 725 41,615 Total 2674 153,511 2711 155,583 2672 153,367 Bamboo Emissions (Mg CO2) 562,872 570,470 562,346 Carbon Emissions by tourists visiting 8,693,652 9,107,635 Siem Reap (Mg CO2) Carbon Offsetting Rate (from 6.5% 6.3% Tourists-Based Emissions)

Siem Reap is a popular tourist destination in Cambodia because of its rich historical temple attraction, Angkor Wat heritage, and natural attractions. Siem Reap welcomed 2.2 million tourists in 2018, increasing from 2.1 million in 2015. These visiting tourists accounted for carbon emissions of

Remote Sens. 2020, 12, 3109 16 of 23

3.4. Area of Bamboo Forest and Its Carbon Stocks by Districts We estimated a total bamboo area of 2674 ha in 2015, 2711 ha in 2018, and 2672 ha in 2019. Carbon stocks in bamboo forest were estimated at 153,511; 155,583; and 153,367 Mg C in 2015, 2018, and 2019, respectively. Specifically, among the 12 districts, the Banteay Srei District had a higher amount of bamboo carbon stocks (47,500 Mg C) between 2015 and 2019, and Svay Leu and Varin Districts had more than 41,600 Mg C in the same period. Other districts covered smaller portions of bamboo, and their carbon stocks were less than 3000 Mg C (Table4).

Table 4. Total carbon stocks and emission reductions in bamboo forests by districts in Siem Reap Province in 2015, 2018, and 2019.

2015 2018 2019 District Name Area (ha) (Mg C) Area (ha) (Mg C) Area (ha) (Mg C) Angkor Chum 26 1504 20 1137 20 1142 Angkor Thum 36 2061 18 1039 55 3134 Banteay Srei 826 47,395 862 49,479 831 47,682 Chi Kraeng 270 15,521 253 14,522 215 12,335 Kralanh 2 121 2 126 3 189 Puok 3 149 1 75 1 69 Prasat Bakong 3 178 3 189 5 258 Krong Siem Reab 20 1165 22 1246 30 1693 Soutr Nikom 14 798 8 448 53 3054 Srei Snam 11 603 2 86 1 63 Svay Leu 757 43,469 787 45,145 734 42,132 Varin 706 40,547 733 42,091 725 41,615 Total 2674 153,511 2711 155,583 2672 153,367

Bamboo Emissions (Mg CO2) 562,872 570,470 562,346 Carbon Emissions by tourists visiting 8,693,652 9,107,635 Siem Reap (Mg CO2) Carbon Offsetting Rate 6.5% 6.3% (from Tourists-Based Emissions)

Siem Reap is a popular tourist destination in Cambodia because of its rich historical templeRemote Sens. attraction, 2020, 12, x Angkor FOR PEER Wat REVIEW heritage, and natural attractions. Siem Reap welcomed 2.2 million18 of 24 tourists in 2018, increasing from 2.1 million in 2015. These visiting tourists accounted for carbon 8,693,652 Mg CO2 in 2015 and 9,107,635 Mg CO2 in 2018. Carbon emission reductions from the full emissions of 8,693,652 Mg CO in 2015 and 9,107,635 Mg CO in 2018. Carbon emission reductions protection of natural bamboo2 forests can offset 6% of carbon2 emissions from tourists visiting Siem from the full protection of natural bamboo forests can offset 6% of carbon emissions from tourists Reap Province. visiting Siem Reap Province. The spatial distribution of bamboo forest by districts in 2015, 2018, and 2019 is shown in Figure The spatial distribution of bamboo forest by districts in 2015, 2018, and 2019 is shown in Figure 15. 15.

A B C

FigureFigure 15. 15.Maps Maps showingshowingcarbon carbon stockstockdistribution distribution atat thethe districtdistrict levellevelusing usingLandsat Landsat OLI OLI data data ( A(A)) in in 20152015 and and ( B(B)) in in 2018, 2018, while while ( C(C)) used used Sentinel-2 Sentinel-2 data data in in 2019. 2019.

4. Discussion Our method has limitations in identifying bamboo in dense and tall vegetation areas such as evergreen forest, as well as in smaller bamboo patches in other forests. There are possible errors in our study due to data limitation and the occurrence of natural stands of bamboo. Limited ground- based data also constrains potential accuracy. Different stages of bamboo growth in the study areas were obtained through remotely sensed temporal data. Different species at various growing stages show different phenological behaviors [20], and their spectral values vary during phenological stages [19,20]. Nath et al. [20] found that ground-based data are needed to reduce the possible errors while increasing the map accuracy. For example, in Sichuan Province, China, where small patch of bamboo could be identified through the use of ground-based data and k-nearest neighbor classifier for the Worldview-2 imagery [21]. Even in such a difficult situation, they achieved a bamboo map producer’s accuracy of 82.65% and a user’s accuracy of 93.10%. The second source of error could be the distribution of bamboo mixed with other vegetation types. During the field survey, we found that other lowland areas in Siem Reap have small bamboo patches, usually along riverbanks and intermingled with tall, dense areas of evergreen and deciduous forests. This makes it difficult to identify bamboo from moderate-resolution satellite imagery, which might account for the omission errors in Tables 3 and Figure 16.

Remote Sens. 2020, 12, 3109 17 of 23

4. Discussion Our method has limitations in identifying bamboo in dense and tall vegetation areas such as evergreen forest, as well as in smaller bamboo patches in other forests. There are possible errors in our study due to data limitation and the occurrence of natural stands of bamboo. Limited ground-based data also constrains potential accuracy. Different stages of bamboo growth in the study areas were obtained through remotely sensed temporal data. Different species at various growing stages show different phenological behaviors [20], and their spectral values vary during phenological stages [19,20]. Nath et al. [20] found that ground-based data are needed to reduce the possible errors while increasing the map accuracy. For example, in Sichuan Province, China, where small patch of bamboo could be identified through the use of ground-based data and k-nearest neighbor classifier for the Worldview-2 imagery [21]. Even in such a difficult situation, they achieved a bamboo map producer’s accuracy of 82.65% and a user’s accuracy of 93.10%. The second source of error could be the distribution of bamboo mixed with other vegetation types. During the field survey, we found that other lowland areas in Siem Reap have small bamboo patches, usually along riverbanks and intermingled with tall, dense areas of evergreen and deciduous forests. This makes it difficult to identify bamboo from moderate-resolution satellite imagery, which might account for the omission errors in Table3 and Figure 16. Remote Sens. 2020, 12, x FOR PEER REVIEW 19 of 24

FigureFigure 16. 16.Images Images showing showing thethe locationslocations of our f fieldield survey survey and and unmanned unmanned aerial aerial vehicle vehicle (UAV) (UAV) PhantomPhantom 4’s 4’s captured captured locationslocations for for detecting detecting bamboo bamboo.. (A) (. Aevergreen). evergreen forest forest and bamboo and bamboo patches patches (red boxes outline bamboo distribution). (B). field survey of bamboo distribution along the river. (C). (red boxes outline bamboo distribution). (B). field survey of bamboo distribution along the river. dense evergreen forest and bamboo distribution. (D). young bamboo patches in a deciduous forest. (C). dense evergreen forest and bamboo distribution. (D). young bamboo patches in a deciduous forest.

WhenWhen observing observing Figures Figure9s 9and and 10 10,, the the rubber rubber categorycategory is the main source source of of confusion confusion for for the the bamboobamboo category category because because the the ranges ranges ofof thethe minimumminimum and maximum maximum threshold threshold values values are are close close to to eacheach other. other To. To avoid avoid thisthis problem, big big data data [3 [535] and] and deep deep learning learning-based-based technology technology [68,69 [68] may,69] be may employed. In addition, because bamboo is usually mixed with evergreen forest in most of the study be employed. In addition, because bamboo is usually mixed with evergreen forest in most of the region, selection of ground-reference points for accuracy assessment without ground data can create study region, selection of ground-reference points for accuracy assessment without ground data can confusion between bamboo and evergreen forests. If such incidence occurs, evergreen or bamboo can create confusion between bamboo and evergreen forests. If such incidence occurs, evergreen or bamboo be mistakenly classified into just one category. One possible solution would be to use very high can be mistakenly classified into just one category. One possible solution would be to use very high resolution imagery such as Planet’s daily imagery for bamboo phenological assessment and IKONOS, resolutionQuickBird imagery, WorldView such as [21] Planet’s, GeoEye, daily and imagery Pleiades, for with bamboo 0.5 meter phenological or finer spatial assessment resolutions and IKONOS,, or to QuickBird,acquire ground WorldView-based [21inventory], GeoEye, data and [30 Pleiades,] or imagery with from 0.5 meter advanced or finer unmanned spatial resolutions, aerial vehicles or to acquire(UAV) ground-based [70,71]. Apart inventoryfrom the problems data [30 ]stated or imagery earlier, other from possible advanced errors unmanned when classifying aerial vehicles the bamboo forests could occur if we want to classify the bamboo distribution by ages. Chen et al. [28] and Langner et al. [72] noted the difficulty in obtaining an integrated phenological cycle for young and mature bamboo using moderate-resolution remote sensing data. Errors in estimating carbon stocks in bamboo forest could arise from the use of generalized data of carbon stocks per unit area from Sasaki et al. [30] which was based on average data for the whole country. To improve the accuracy of carbon stocks assessment, field measurement of the targeted bamboo forest in the study region could be a solution, but this is costly and time consuming.

5. Conclusions Using PBTC method and GEE with remote sensing imagery from Landsat 8 OLI in 2015, 2016, 2017, and 2018, we were able to determine the minimum and maximum vegetation thresholds for bamboo forest, evergreen forest, and rubber plantation in Siem Reap province, Cambodia. With these thresholds, we could classify and map the distribution of natural bamboo forest in 2015 and 2018. We achieved the overall Landsat 8 OLI bamboo maps for 2015 and 2018 UAs of 86.6% and 87.9% and PAs of 95.7% and 97.8%, respectively. Similarly, we achieved UAs of 86.5% and PAs of 91.7% for Sentinel- 2 imagery for 2019. Accordingly, spatial distribution of carbon stocks by districts in the Siem Reap was estimated and mapped across the province. We found that area of bamboo was 2674 ha in 2015,

Remote Sens. 2020, 12, 3109 18 of 23

(UAV) [70,71]. Apart from the problems stated earlier, other possible errors when classifying the bamboo forests could occur if we want to classify the bamboo distribution by ages. Chen et al. [28] and Langner et al. [72] noted the difficulty in obtaining an integrated phenological cycle for young and mature bamboo using moderate-resolution remote sensing data. Errors in estimating carbon stocks in bamboo forest could arise from the use of generalized data of carbon stocks per unit area from Sasaki et al. [30] which was based on average data for the whole country. To improve the accuracy of carbon stocks assessment, field measurement of the targeted bamboo forest in the study region could be a solution, but this is costly and time consuming.

5. Conclusions Using PBTC method and GEE with remote sensing imagery from Landsat 8 OLI in 2015, 2016, 2017, and 2018, we were able to determine the minimum and maximum vegetation thresholds for bamboo forest, evergreen forest, and rubber plantation in Siem Reap province, Cambodia. With these thresholds, we could classify and map the distribution of natural bamboo forest in 2015 and 2018. We achieved the overall Landsat 8 OLI bamboo maps for 2015 and 2018 UAs of 86.6% and 87.9% and PAs of 95.7% and 97.8%, respectively. Similarly, we achieved UAs of 86.5% and PAs of 91.7% for Sentinel-2 imagery for 2019. Accordingly, spatial distribution of carbon stocks by districts in the Siem Reap was estimated and mapped across the province. We found that area of bamboo was 2674 ha in 2015, 2711 ha in 2018, and 2672 ha in 2019. Total carbon stocks in the respective years were 153,511, 155,583, and 153,367 Mg C. It can be concluded that GEE can be used to classify and map the natural distribution of the natural bamboo forest and related carbon stocks with the aid of PBTC method, multi-spectral Sentinal-2 and Landsat 8 OLI imagery. However, using very high-resolution imagery, UAV, big data technology, and deep learning could improve the misclassification during the closer ranger of threshold values of various land cover categories. To avoid confusion between natural bamboo and evergreen forests, it is important to select the ground reference points through field visit, forest measurement, and/or using UAV. If bamboo forest is fully protected, the avoided emissions could offset about 6% of carbon emissions by tourists visiting the province, indicating that bamboo can also play an important role in climate change mitigation in addition to providing non-timber forest products to the local people. As bamboo forest has multiple roles in local livelihood improvement as well as climate change mitigation, management of this forest through proper mapping and zoning could increase awareness of the benefits of the bamboo forests, which could help promote effective management for long-term sustainable use.

Author Contributions: Conceptualization, M.V.; methodology, M.V. and N.S.; GEE coding, M.V.; validation, M.V.; formal analysis, M.V.; investigation, M.V.; data curation, M.V.; writing—original draft preparation, M.V.; review and editing, N.S., S.A., J.A.C., and B.S.; visualization, M.V.; supervision, M.V., S.A., and N.S., project administration, M.V., N.S., S.A., and B.S.; funding acquisition, N.S., M.V., and B.S. All authors have read and agreed to the published version of the manuscript. Funding: This research project is supported by the Second Century Fund (C2F), Chulalongkorn University and the Swedish Research Council grant Forest Restoration and Water Availability for Smart Agriculture: A Case Study of Cambodia (FRAWASA), Dnr. 2016-06329. Acknowledgments: We would like to acknowledge the C2F of Chulalongkorn University, Thailand, for their post-doctoral fellowship. We would also like to thank the LEET Intelligence Co., Ltd. and Ecosystems Research and Development Bureau in the Philippines for research support and the FRAWASA’s Cambodian team for their assistance in field data collection. Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2020, 12, 3109 19 of 23

Appendix A

Table A1. GEE JavaScript function for calculating the Landsat 8 OLI EVI.

Landsat 8 OLI EVI function // Add an EVI function for the Landsat 8 TAO collections. function addEVI(Landsat8){ return Landsat8.addBands(Landsat8.expression) ‘2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))’, { ‘NIR’: Landsat8.select(‘B5’), ‘RED’: Landsat8.select(‘B4’), ‘BLUE’: Landsat8.select(‘B2’) }).rename(‘L8EVI’)).float(); }; Sentinel-2 EVI function // Add an EVI function for the Sentinel-2 (SNL) collections. function addEVI(SNL){ return SNL.addBands(SNL.expression( ‘2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))’, { ‘NIR’: SNL.select(‘B8’), ‘RED’: SNL.select(‘B4’), ‘BLUE’: SNL.select(‘B2’) }).rename(‘SNLEVI’)).float(); }; where L = 1 (canopy background adjustment factor) [49,50]; C1 = 6 and C2 = 7.5 (aerosol correction factors) and G = 2.5 (gain factor) [49–51]; and NIR (Landsat band 5 and Sentinel-2 band 8 (B8), RED (band 4 (B4) in both Landsat and Sentinel-2), and BLUE (band 2 (B2) in both Landsat and Sentinel-2) are atmospherically corrected for surface reflectance in near-infrared, red, and blue bands of Sentinel-2 imagery in GEE [50].

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