SAR-Based Estimation of Above-Ground Biomass and Its Changes in Tropical Forests of Kalimantan Using L- and C-Band

Kalimantan poses one of the highest carbon emissions worldwide since its landscape is strongly endangered by deforestation and degradation and, thus, carbon release. The goal of this study is to conduct large-scale monitoring of above-ground biomass (AGB) from space and create more accurate biomass maps of Kalimantan than currently available. AGB was estimated for 2007, 2009, and 2016 in order to give an overview of ongoing forest loss and to estimate changes between the three time steps in a more precise manner. Extensive field inventory and LiDAR data were used as reference AGB. A multivariate linear regression model (MLR) based on backscatter values, ratios, and Haralick textures derived from Sentinel-1 (C-band), ALOS PALSAR (Advanced Land Observing Satellite's Phased Array-type L-band Synthetic Aperture Radar), and ALOS-2 PALSAR-2 polarizations was used to estimate AGB across the country. The selection of the most suitable model parameters was accomplished considering VIF (variable inflation factor), p-value, R-2, and RMSE (root mean square error). The final AGB maps were validated by calculating bias, RMSE, R-2, and NSE (Nash-Sutcliffe efficiency). The results show a correlation (R-2) between the reference biomass and the estimated biomass varying from 0.69 in 2016 to 0.77 in 2007, and a model performance (NSE) in a range of 0.70 in 2016 to 0.76 in 2007. Modelling three different years with a consistent method allows a more accurate estimation of the change than using available biomass maps based on different models. All final biomass products have a resolution of 100 m, which is much finer than other existing maps of this region (>500 m). These high-resolution maps enable identification of even small-scaled biomass variability and changes and can be used for more precise carbon modelling, as well as forest monitoring or risk managing systems under REDD+ (Reducing Emissions from Deforestation, forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks) and other programs, protecting forests and analyzing carbon release

Object-based Image Analysis Using VHR Satellite Imagery for Monitoring the Dismantling of a Refugee Camp after a Crisis: The Case of Lukole, Tanzania. GI_Forum 2014 – Geospatial Innovation for Society|

The use of HR and VHR (high/very high spatial resolution) imagery and OBIA (objectbased image analysis) offers new possibilities for monitoring activities in and around refugee camps to manage, understand, and assess developments and impacts of the camp on its environment (see for example TIEDE et al. 2013, HAGENLOCHER et al. 2012). Here we demonstrate how VHR imagery in combination with OBIA can be used to retrieve and create valuable information about a remote refugee camp and its surroundings before, during, and after the dismantling and the repatriation process. Feature extraction approaches for single dwellings and further information retrieval, as well as land cover classification for the refugee camp Lukole in Tanzania were combined for an integrated monitoring approach

Object-based Image Analysis Using VHR Satellite Imagery for Monitoring the Dismantling of a Refugee Camp after a Crisis: The Case of Lukole, Tanzania. GI_Forum 2014 – Geospatial Innovation for Society|

The use of HR and VHR (high/very high spatial resolution) imagery and OBIA (objectbased image analysis) offers new possibilities for monitoring activities in and around refugee camps to manage, understand, and assess developments and impacts of the camp on its environment (see for example TIEDE et al. 2013, HAGENLOCHER et al. 2012). Here we demonstrate how VHR imagery in combination with OBIA can be used to retrieve and create valuable information about a remote refugee camp and its surroundings before, during, and after the dismantling and the repatriation process. Feature extraction approaches for single dwellings and further information retrieval, as well as land cover classification for the refugee camp Lukole in Tanzania were combined for an integrated monitoring approach