The complex geological and bioclimatic history of Southeast Asia has resulted in an exceptionally rich biodiversity [1
] and some of the highest concentrations of endemic species in the world [2
]. The floristically-distinct forest types that occur in the region vary in their species assemblages, vulnerability to habitat conversion or degradation, conservation value, and representation within protected area networks [3
]. Forests that are accessible and occur in areas with high human population densities are especially vulnerable to degradation and deforestation. As a result, the region’s remaining forest cover predominantly occurs at high elevations or in areas that are difficult to access due to steep terrain [5
]. Lowland evergreen forests have experienced especially high rates of forest loss [6
]. Similarly, mangrove forests occur exclusively in coastal areas and are undergoing rapid conversion to agriculture [8
]. Due to the unique threats faced by different forest types, conservation strategies and risk assessments in the region should be based on an accurate understanding of current forest distributions and knowledge of where forest loss and degradation are taking place.
Although the extent of forest degradation in some landscapes may be much larger than the extent of outright forest loss (e.g., [9
]), forest degradation is more difficult to monitor with remote sensing techniques than outright deforestation. Various approaches have been applied to the mapping of forest degradation, such as the analysis of time series data (e.g., [11
]), use of canopy cover loss as an indicator of degradation (e.g., [12
]), or mapping of secondary degradation indicators such as log landings and logging roads (e.g., [15
]). Assessing forest degradation in continental Southeast Asia is particularly challenging due to variation among natural forest types in their physical structure and seasonal patterns of canopy cover that range from evergreen to fully deciduous [5
]. This region has a monsoon climate with a distinct wet season (May–October) that corresponds with elevated vegetation growth [16
]. The dry season (November–March) provides greater availability of cloud-free satellite imagery at a time when reduced canopy cover may indicate either forest degradation or the presence of deciduous tree species in their annual leaf-off period. Thus, distinguishing between deciduous forest and canopy thinning due to degradation is an important challenge in continental Southeast Asia and it has been suggested that single-date mapping of forest degradation based on canopy cover may be unreliable without matching data for specific sites in intact condition [5
However, fairly subtle differences in forest type may be resolvable using medium-resolution satellite imagery. Recent studies have demonstrated that local floristic differences in tropical forests can be effectively identified using a combination of medium-resolution multi-spectral (e.g., Landsat) and topographic data [17
]. Scaling these approaches to broader landscapes may be problematic if this results in increased spectral variability within forest classes and reduced ability to distinguish among them. This may be due to factors such as forest disturbance, topographic shadowing, or differences among scenes in a mosaicked image [22
]. Sometimes these limitations are avoided by assessments and studies that focus on mapping a single forest type of interest (e.g., [3
]), yet country- and regional-level conservation planning often require comprehensive assessments of all forest types and their associated vulnerability to loss and degradation.
Despite having less than 1% of the world’s population, forest loss in Myanmar represented 16.5% of global forest loss between 2010 and 2015 [24
]. Myanmar remains one of the most heavily forested countries in Southeast Asia, but had an annual deforestation rate of 0.30% between 2002 and 2014, and lost “intact forest” (generally canopy cover <80%) at an annual rate of 0.98% [12
]. Although some forest areas in Myanmar are selectively logged under the Myanmar Selection System, which sets harvest quotas to sustain long-term timber yields [14
], logging concessions in unmanaged natural forest have far less oversight [25
] and contribute to the rapid loss of relatively intact forest, often from ethnic conflict areas. Furthermore, government policy and land concessions have encouraged the clearing of forest for agricultural plantations, which has sometimes involved the de-gazetting of areas within national forest reserves [26
]. Much of this forest conversion for commercial agriculture is occurring in Myanmar’s Tanintharyi Region, where the rapid expansion of oil palm cultivation, and to a lesser extent rubber, is responsible for ongoing forest loss within the largest remaining areas of lowland wet evergreen forest in the Sundaic region of continental Southeast Asia [12
]. These biologically-rich forest ecosystems, along with mangrove forests, are poorly represented in Myanmar’s protected area system and are priority areas for conservation [28
The motivation for this study was the challenge of distinguishing forest degradation from spectral differences among distinct forest types, such as those that occur due to seasonal leaf-on/leaf-off cycles of deciduous vegetation. We used canopy cover as an indicator of forest degradation (e.g., [12
]) in order to map intact and degraded forest extent in Myanmar’s Tanintharyi Region based on single-date Landsat 8 imagery and topographic data. As a baseline, we evaluated classification accuracy with two natural forest classes, intact and degraded forest, as well as eight non-forest land use/land cover classes. We then compared scenarios with intact and degraded forest partitioned into four ecologically-distinct forest types: mangrove, lowland evergreen forest, upland evergreen forest, and mixed deciduous forest. This study provides insight into mapping tropical forest degradation across a range of distinct forest types, while also providing critical information on the current status of unique forest ecosystems and patterns of human land use in Tanintharyi, Myanmar.
3.1. Mapping of Ecologically-Distinct Forest Types
For land cover classifications with just two natural forest classes (intact and degraded), validation based on withheld training data resulted in kappa coefficients of 0.70 and 0.78 for the GMLC and Random Forest classifications, respectively. The GMLC classifier had mean per-class user’s and producer’s accuracies of 74.2% and 73.2% Intact forest, degraded forest, and bare ground/clearing were frequently misclassified and had the three lowest user’s and producer’s accuracies by class. Overall, 28.0% of reference points for forest were misidentified as non-forest classes and an additional 16.0% were correctly identified as forest but incorrectly assigned as intact or degraded. The Random Forest classifier had mean per-class user’s and producer’s accuracies of 79.2% and 78.8%, respectively. User’s and producer’s accuracies were again low for the intact and degraded forest classes, with 24.0% of forest reference points being misclassified as non-forest classes versus 16.0% confusion of intact and degraded forest.
Partitioning forest cover into separate intact and degraded classes for each of four distinct forest types (mangrove, lowland evergreen, upland evergreen, and mixed deciduous) led to 3.3% and 3.6% increases in mean per-class user’s and producer’s accuracy based on the GMLC classification. The kappa coefficient for this classification improved from 0.70 to 0.75. Mean per-class producer’s accuracy for forest classes improved from 56% with just two forest classes to 72.5% with eight target forest classes. Furthermore, just 4.5% of reference points for forest were misidentified as a non-forest class, 8.5% were misclassified as a different forest type, and 14.5% were assigned the correct forest type but misidentified as intact or degraded. With the partitioning of intact and degraded forest into eight classes, there was a slight decrease in the kappa coefficient for the Random Forest classification, from 0.78 to 0.75. Mean per-class user’s and producer’s accuracies decreased by 1.9% and 2.0%, respectively. The forest type and intact or degraded status of reference points were correctly predicted 74.0% of the time. A further 6.0% of reference points for forest were misidentified as non-forest, 8.5% were misidentified as a different forest type, and 11.5% were assigned the correct forest type but misclassified as intact or degraded. Overall, the GMLC and Random Forest classifications with eight natural forest classes had the same kappa coefficients (0.75) and producer’s accuracies (76.8%), with the GMLC approach having a minimally higher mean per-class user’s accuracy (77.5% vs. 77.3%). All further results, tables, and figures reported are based on the GMLC classification due to the nearly-identical accuracy metrics and an apparent tendency to better predict land cover in several parts of the landscape where fine-resolution data were not available to create training data.
3.2. Extent of Remaining Forest Cover
Intact or degraded forest classes covered 80.7% of Tanintharyi, Myanmar in March 2016 (Figure 1
and Figure 3
, Table 2
). Upland evergreen forest was the most common forest type (42.3% of total area), followed by lowland evergreen forest (21.6%), mixed deciduous forest (10.8%), and mangrove forest (6.0%). The prevalence of forest degradation, or areas showing apparent canopy damage, varied widely among the four distinct forest types. Just 34.0% of mangrove forest was classified as intact (Figure 1
B), compared to 47.1% of mixed deciduous forest, 52.5% of lowland evergreen forest, and 72.9% of remaining upland evergreen forest.
The major forest types in Tanintharyi also differ considerably in their representation within the national forest reserve and protected area systems. Combined, these government reserves encompass 56.2% of remaining intact forest. However, just 12.5% of intact mixed deciduous and 39.9% of intact mangrove forest fall within protected areas and forest reserves, compared to 60.8% and 62.7% of intact lowland and upland evergreen forest, respectively.
3.3. Human Land Use
We estimate that rice cultivation areas and mature oil palm, rubber, and betal nut plantations currently combine to cover 11.5% of Tanintharyi and 16.6% of all land outside protected areas and forest reserves. Rice cultivation covers 3.9% of the region, but is concentrated in flat, coastal areas. Commercial oil palm currently represents 3.4% of the landscape and primarily occurs in the Kawthaung district of southern Tanintharyi where extensive recent forest clearing suggests a continued expansion into some of the remaining tracts of lowland evergreen forest (Figure 1
C). Mature rubber and betal nut plantations cover a further 2.2% and 2.1% of Tanintharyi, respectively. Areas of young agroforestry plantation without mature tree cover are believed to be primarily classified as clearing or degraded forest due to the dominant spectral signature of bare ground or early-successional vegetation. Given the rapid recent expansion of plantation areas and the large combined extents of bare ground and degraded forest classes, estimated plantation area in this study is likely highly conservative.
3.4. Accuracy Assessment
Training data for the eight natural forest classes encompassed a broad range of spectral variability (Figure 4
). The overall per-class accuracies of the Tanintharyi land cover map ranged from 44% to 100% (Table 3
). Of the error associated with natural forest classification, 52.7% of misclassified forest areas were due to confusion of canopy damaged areas and intact forest of the same forest type. Success distinguishing intact from degraded forest varied considerably among forest types, with degraded mixed deciduous forest being correctly identified just 44% of the time and most commonly confused with intact mixed deciduous. Areas of bare ground/clearing also had relatively low accuracy. New clearing is often a short-lived, transitional class and reference imagery may not always match the Landsat data used to perform the classification. Young plantation can also have spectral characteristics that are most similar to bare ground [50
] or young regrowth in degraded forest areas. Finally, with the exception of rice cultivation areas, we observed a general tendency for predicted land cover in human-dominated parts of the landscape to have lower confidence (Figure 2
). Many areas of Tanintharyi are composed of small plots of different land cover types, such as clearing, degraded forest, small-scale plantation, and areas of traditional shifting cultivation (i.e., slash-and-burn), and will often have a mixed spectral signature at 30-m resolution.