Retrieval of forest attributes in complex successional forests of Central Indonesia: Modeling and estimation of bitemporal data

Quantification of forest parameters in different successional stages is required because of its importance as a source of global emissions and ecosystem changes. This study focuses on a successional tropical forest under logging practices in East Kalimantan province, Indonesia. We modeled the forest attributes using both a parametric multiple linear regression analysis and neural networks approach, with Landsat ETM data acquired in 2000 (ETM00). We compiled sample plot data using forest inventory data collected from 1997 to 1998. A total of 226 plots were used to train the models and 112 plots were used for the validation. The remote sensing data (spectral values, vegetation indices, texture, etc.) coupled with digital elevation model (DEM) were experimented with and selectively used to model basal area, stem volume and above ground biomass (AGB). We investigated the possibility to estimate the forest attributes from bitemporal ETM data by calibrating radiometric properties of the ETM image from 2003 (ETM03) using the multivariate alteration detection method. The Pearson correlations showed that the mean texture index is strongly correlated with the forest attributes. We show that neural networks resulted in a higher coefficient of determination (r ²) and lower RMSE than multiple regressions for predicting the forest attributes. The estimated forest properties increased with the forest succession advancement (i.e. from the open forest to advanced secondary forest classes). The modeled basal area, stem volume and AGB varied from 10.7-15.1m² ha⁻¹, 123.2-181.9m³ ha⁻¹, and 132.7-185.3Mgha⁻¹, respectively. The RMSEr values of model fitting ranged from 11.2% to 13.3%, and the test dataset estimated slightly higher RMSEr which varied from 12% to 14.1%. The ETM03 forest attributes revealed favorable estimates, showing considerably higher estimates than the ETM00. The estimation of forest properties using neural networks makes Landsat data a valuable source of information for forest management, mainly with the recent free access to its historical dataset.