Improved assessment of mangrove forests in Sundarbans East Wildlife Sanctuary using WorldView 2 and TanDEM‐X high resolution imagery

Abstract

Recent developments of remote sensing techniques which can capture both the structure and function of the ecosystem provide a more representative view of the landscape. These unique Earth observations were used to help improve traditional forestry surveys by providing species‐specific land cover classes for mangrove forests in the Sundarbans East Wildlife Sanctuary. By combining optical data from WorldView2 (WV2; 2 m pixel) and a canopy height model derived using radar data from TanDEM‐X (TDX; 12 m pixel), we identified nine mangrove and five non‐mangrove classes by following an Iterative Self‐Organizing Data Analysis Algorithm. Three dominant mangrove species accounted for nearly 50% of the sanctuary. Heritieria fomes disproportionately covered the largest area at 43%, overturning previous field‐based estimates of Excoecaria agallocha dominance. E. agallocha and Sonneratia apetala, covered 3% and 1.47% of the sanctuary, respectively. Four mixed species classes were also identified with clear vegetation zonation patterns that trended toward species homogeneity with increasing distance from shore. The overall land cover accuracy (WV2: 89.33%; WV2‐TDX: 89.89%), the Kappa Coefficient (WV2: 0.88; WV2‐TDX: 0.89) and change statistics between WV2 and WV2‐TDX land cover classifications indicate that the WV2 imagery can separate mangrove community types without structural data. The combination of the land cover classifications and the canopy height model indicated that H. fomes were not only the most dominant forest but also, on average, the tallest (12.3 m) among the other eight mangrove types. Our large‐scale mapping with high resolution optical and radar platforms can capture subtle changes in mangrove vegetation and canopy structural gradients more accurately and be used to monitor biodiversity changes and Aichi Biodiversity Targets and Indicators, which would contribute to biodiversity policy updating.

Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

“…In terms of the methods, prior studies use a range of classification techniques such as the iterative self-organizing data analysis (ISODATA) clustering, maximum likelihood classification (MLC), hybrid, random forest (RF), classification and regression trees (CART), support vector machine (SVM), and object oriented classification among others [16,17,34,[37][38][39][40][41]43,66]. With the advent of cloud computing platforms with free access to petabytes of geospatial data, such as Google Earth Engine (GEE), it has now become increasingly accessible and straight-forward to analyze enormous amounts of satellite imagery covering large regions [37,[67][68][69][70][71][72].…”

Radar and optical remote sensing for near real‐time assessments of cyclone impacts on coastal ecosystems

“…This complex ecosystem is a network of numerous tidal waterways, evolving mudflats and islands of salt-tolerant mangrove species, composed of 24 "true mangrove" species and 70 "mangrove associates" including dominant tree species such as Sundri (Heritiera fomes), Gewa (Excoecaria agallocha), Goran (Ceriops decandra), and Keora (Sonneratia apetala) (Rahman, Hossain, et al, 2015). This ecosystem also supports over 1000 documented faunal species including several endangered and threatened vertebrate species such as tiger (Panthera tigris tigris), South Asian river dolphin (Platanista gangetica), Irrawaddy dolphin (Orcaella brevirostris), masked finfoot (Heliopais personatus), Northern river terrapin (Batagur baska), and saltwater crocodile (Crocodylus porosus) (Aziz & Paul, 2015;Rahman et al, 2019). cloud cover.…”

Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

“…We found that there were 15 remote-sensing articles published on forests during the study period. Two-third of these studies used Landsat data in their analyses, while a few used MODIS [22,23], Sentinel-2 [24], and radar products (e.g., ALOS-PALSAR, TanDEM-X) [25,26]. The availability of moderate resolution (i.e., 30 m) Landsat imageries made it a popular choice of data products among the researchers.…”

“…Additionally, the supervised random forest [24], hierarchical [27], hybrid [30,32] and classification approaches have been used by some. Forest health assessment, habitat degradation, species composition related studies used various approaches, in addition to supervised MLC, in their analysis including OLS Ordinary Least Square (OLS) regression [22,23], unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA) [25], and Refined Gamma Maximum-A-Posteriori (RGMAP) filtering [26]. In change detection studies, classification accuracy is very important to ensure the correct estimation of land cover change [38].…”