In response to prevailing classification inconsistency between land cover maps, developed by different organizations in different times at different scales, an object-based National Land Representation System (NLRS) for Bangladesh has been developed. The process, which began in 2013 and was completed in 2016, brought together several national organizations and involved an extensive process of consultation, data collection, translation, and analysis of existing land cover/use classification systems. The process focused on the interpretation of three legends from historic national land cover/use maps. Field inventory data were collected from over 1000 sites across the country to assist the process of land characterization and the development of a dynamic and representative overview of land cover and land use in Bangladesh. The system has been applied to the development of a wall-to-wall national land cover map for the year 2015. In this article, the methodological process and results of NLRS formulation and land cover map 2015 are presented. We also provide examples of how this interoperable system and the land cover dataset are being used for variety of applications including national forest resources assessment, estimation of REDD+ activity data, integration of biophysical and socioeconomic information, and semantic similarity assessment.

Carbon to nitrogen ratio (C:N) of senescent leaf is a crucial functional trait and indicator of litter quality that affects belowground carbon and nitrogen cycles, especially soil decomposition. Although mapping the C:N ratio of fresh mature canopies has been attempted, few studies have attempted to map the C:N ratio of senescent leaves, particularly in mangroves. In this study, four machine learning models (Stochastic Gradient Boosting, SGB; Random Forest, RF; Support Vector Machine, SVM; and Partial Least Square Regression, PLSR) were compared for testing the predictability of using the Landsat TM 5 (LTM5) and Landsat 8 to map spatial and temporal distribution of C:N ratio of senescent leaves in Sundarbans Reserved Forest (SRF), Bangladesh. Surface reflectance of bands, texture metrics of bands and vegetation indices of LTM5 and Landsat 8 yearly composite images were extracted using Google Earth Engine for 2009–2010 and 2019. We found SGB, RF and SVM were significant different from PLSR based on MAE, RMSE, and R² (p < 0.05). Our results indicate that remote sensing data, such as Landsat TM data, can be used to map the C:N ratio of senescent leaves in mangroves with reasonable accuracy. We also found that the mangroves had a high spatial variation of C:N ratio and the C:N ratio map developed in the current study can be used for improving the biogeochemical and ecosystem models in the mangroves.

Reliable land cover information is essential for scaling plot-based measurements in national forest inventories (NFIs). This study compared the precision of key forest indicators in the Bangladesh NFI using remote sensing (RS)-derived and field-assigned land cover data. Field data from 1781 plots, collected as part of the Bangladesh NFI (2015–2019), were integrated with a 2015 national land cover map produced from SPOT-6/7, Landsat, and Sentinel-2 imagery. The precision of forest indicator estimates was evaluated across land cover domains and ecological zones. Results show that, under an unchanged NFI field measurement and estimation framework, RS-derived land cover reduced the width of confidence intervals (i.e., improved statistical precision) of estimates for most biomass related indicators, including above- and below-ground biomass, tree volume, basal area, and carbon pools, by 15–20% on average, with some reductions exceeding 50%. Improvements were less consistent for regeneration-related indicators (saplings, seedlings). The insights from this study highlight the advantages of remote sensing-derived land cover for improving NFI indicator precision, while underscoring the continued need for advancing ontology-driven approaches with necessary strengthening of field crew capacity to ensure the consistent application of land cover standards.

Reliable land cover (LC) information is essential for scaling plot-based measurements in National Forest Inventories (NFIs). This study assessed the impact of using remote sensing (RS)-derived LC compared with field-assigned LC on the precision of key forest indicators in Bangladesh’s NFI (2015–2019). Field data from 1,781 plots were integrated with a 2015 national LC map produced from SPOT-6/7, Landsat, and Sentinel-2 imagery. Sampling errors of forest indicator estimates were evaluated across LC classes and ecological zones. Results show that RS derived LC reduced sampling errors for most biomass related indicators, including above- and below-ground biomass, tree volume, basal area, and carbon pools, by 15–20% on average, with some reductions exceeding 50%. Improvements were less consistent for regeneration related indicators (saplings, seedlings). These findings highlight the advantages of RS derived LC for improving NFI precision, while underscoring the continued need for advancing ontology driven approaches with necessary strengthening of field crew capacity to ensure consistent application of land cover standards.