Object-based forest biomass estimation using Landsat ETM+ in Kampong Thom Province, Cambodia

Abstract

Information about forest biomass distribution is important for sustainable forest management and monitoring fuelwood supply. The objective of this study is to develop an accurate forest biomass map for Kampong Thom Province, Cambodia. We used a new technique (object-based approach) and a conventional technique (pixel-based approach) for the estimation of forest biomass using Landsat Enhanced Thematic Mapper Plus (ETM?). The object-based approach created segments of images, and calculated statistical and textural attributes. Our results showed that estimation accuracy of the object-based approach, with the use of band 1 and an exponential fit, was the best (R 2 = 0.76), and this accuracy was comparable to that of the pixel-based approach (R 2 = 0.67). Although several textural variables were related to forest biomass, they did not contribute significantly to improvement of estimation accuracy. However, the object-based method can be used for image segmentation so that the image objects are spectrally more homogeneous within individual regions than with their neighbors. Hence, they can be regarded as management units for policy-related spatial decisions. Therefore, it is possible to select either of the two methods depending upon what the situation demands.

Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

“…Although Landsat TM images have been widely employed for AGB estimation of forest ecosystems [4,52,[72][73][74][75][76][77][78], extracting and selecting spectral variables to accurately derive spatial distribution of AGB is still challenging mainly due to the saturation of spectral reflectance and the presence of mixed pixels [4,58,79]. The image data and spectral bands from different sensors have their own characteristics in reflecting land surfaces [4].…”

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

“…The free and open access of Landsat data since 2008 [25] has removed cost limitations to accessing large numbers of images while also reducing processing overhead through provision of a robust series of standard products [26]. Predicting forest structure variables with Landsat data has been a research topic of great interest (e.g., [27][28][29][30]. However, an asymptotic relationship is typically found when using Landsat data alone to make empirical predictions of forest structure [31], with the asymptote linked to canopy density, crown closure or canopy cover.…”

Reform Earth Observation Science and Applications to Transform Hindu Kush Himalayan Livelihoods—Services-Based Vision 2030

“…Most of the developing countries do not have forest inventory data to get accurate biomass figures. Furthermore, several methods have been proposed for estimating forest biomass using remote sensing techniques that make use of a combination of regression models, vegetation indices, and canopy reflectance models (Cho et al 2012;Gonzalez et al 2010;Huang et al 2013;Kajisa et al 2009). Remote sensing-based biomass estimation, mapping, and accuracy have increasing attraction for scientists (Ahmed et al 2013;Foody et al 2003;Franklin and Hiernaux 1991;Lu 2006;Nelson et al 1988;Steininger 2000;Zheng et al 2008).…”