Regional Mapping of Plantation Extent Using Multisensor Imagery

Abstract

Industrial forest plantations are expanding rapidly across Monsoon Asia and monitoring extent is critical for understanding environmental and socioeconomic impacts. In this study, new, multisensor imagery were evaluated and integrated to extract the strengths of each sensor for mapping plantation extent at regional scales. Two distinctly different landscapes with multiple plantation types were chosen to consider scalability and transferability. These were Tanintharyi, Myanmar and West Kalimantan, Indonesia. Landsat-8 Operational Land Imager (OLI), Phased Array L-band Synthetic Aperture Radar-2 (PALSAR-2), and Sentinel-1A images were fused within a Classification and Regression Tree (CART) framework using random forest and high-resolution surveys. Multi-criteria evaluations showed both L-and C-band gamma nought γ˝backscatter decibel (dB), Landsat reflectance ρ λ , and texture indices were useful for distinguishing oil palm and rubber plantations from other land types. The classification approach identified 750,822 ha or 23% of the Taninathryi, Myanmar, and 216,086 ha or 25% of western West Kalimantan as plantation with very high cross validation accuracy. The mapping approach was scalable and transferred well across the different geographies and plantation types. As archives for Sentinel-1, Landsat-8, and PALSAR-2 continue to grow, mapping plantation extent and dynamics at moderate resolution over large regions should be feasible.

Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region

“…There have also been reports that oil palm development has been motivated or reinforced by logging interests (e.g., [26]), and our land cover classification indicates that extensive patches of lowland forest have been cleared in areas where oil palm has only been successfully established along narrow roadside strips ( Figure 1C). A recent study estimated that 23% of total land area in Tanintharyi is now agroforestry plantation [31]. Although this total differs considerably from the total areas of mature oil palm, rubber, and betal nut cultivation reported in our study (11.5%), we identified a further 3.9% of the landscape as recent clearing and 30% as degraded forest, which may include young plantation with early-successional vegetation.…”

“…As a result, the region retains extensive forests in the mountainous areas along the Thai border and has the largest remaining areas of biologically-rich lowland wet evergreen forest in the Sundaic region of continental Southeast Asia [6,27]. Concurrent with increased political stability, the landscape has been experiencing widespread deforestation [28] and rapid expansion of agriculture and agroforestry [26,31]. These land-use changes are focused primarily in low-elevation coastal areas, with remaining forest cover being increasingly concentrated in areas of steep terrain.…”

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

“…The archive for Myanmar now has 1367 open access geofield photos. Google Earth high resolution imagery and the geofield photos were used to create polygons to train the classifier following [42]. We developed randomly stratified training data in four main regions (1) northern Myanmar; (2) Central Dry Zone; (3) Irrawaddy Delta and (4) Tanintharyi.…”

“…A Classification and Regression Tree (CART) routine was applied to generate the updated Land Use Land Cover (LULC) map using a stacked data cube ( Figure 3). The data cube was created from stacking the preprocessed Landsat-8, PALSAR-2, and Sentinel-1 observations following the lineage of [42]. The ensemble, machine-learning, random forest algorithm [43] was used as the initial classifier with the training data.…”

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

“…Major techniques used for the detection, classification, and mapping of vegetation using remote sensing imagery are vegetation indices [20,21], spectral mixture analysis [22], temporal image-fusion [23,24], texture based measures [25], and supervised classification using machine learning classifiers such as maximum likelihood [26], random forests [27,28], decision trees [29], support vector machines [30], fuzzy learning [31], and neural networks [32][33][34]. Nevertheless, performance of existing large-scale land cover maps is limited to the discrimination of vegetation physiognomic types, which is still a challenging field [35].…”