Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes

Abstract

Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km 2 ; 39% of total area), as well as total forest conversion into agricultural plantations (3214 km 2). Gross forest loss (5133 km 2) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5471 km 2) and rubber (4025 km 2) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

“…Our results match the findings of other studies that use optical data [15][16][17][18][19]. In the case of SAR data, the situation is less clear.…”

“…Results with L-band SAR data have been close to the results of optical data particularly in forest and non-forest classification [17,[26][27][28] and L-band data have also been shown to be applicable for producing forest cover maps at global scales [29]. However, the results in land cover classification using SAR data are not consistent [17,18,30,31]. In [30] the overall accuracy in forest and non-forest classification was 92.1% for ALOS PALSAR L-band data and 81.2% for Radarsat-2 C-band data.…”

“…Variable land cover classification accuracies based on different approaches in image interpretation and accuracy assessment have been reported. Overall accuracy in forest and land cover classification using Landsat-type data has often been close to or above 90% [15][16][17][18] and several global forest cover maps have been produced (e.g., [19,20]). For detailed class division on national and global levels, also lower overall accuracies have been obtained.…”

“…In forest and non-forest mapping in French Guiana [32], an accuracy of 90% was achieved with multitemporal ERS-1 and ERS-2 C-band data and an accuracy of 94% with Envisat ASAR data. The studies of synergistic use of optical and SAR data sets indicate that adding SAR features to optical data can improve the land cover classification results [18,25,26,33]. Also, L-band SAR has been more effective as additional data than C-band.…”

Assessing environmental impacts and change in Myanmar's mangrove ecosystem service value due to deforestation (2000–2014)

“…For instance, while our 2014 mangrove forest cover estimate for Tanintharyi (0.24 million ha) and the estimate of Gaw, Linkie, and Friess () for the same state and year were relatively close (0.25 million ha), this was not necessarily the case for the year 2000 (0.28 and 0.26 million ha, respectively). For the years 2015 and 2016, De Alban, Connette, Oswald, and Webb () and Connette, Oswald, Songer, and Leimgruber () estimated the state's mangrove area to be 0.34 and 0.24 million ha, respectively. Furthermore, Weber, Keddell, and Kemal () estimated a 0.03 million ha net mangrove forest cover loss from 2000 to 2013 in Rakhine state, whereas in this study, we detected a 0.08 million ha net mangrove forest cover loss from 2000 to 2014 (Table ).…”