Improving Estimation Accuracy of Growing Stock by Multi-Frequency SAR and Multi-Spectral Data over Iran’s Heterogeneously-Structured Broadleaf Hyrcanian Forests

Abstract

Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1’s polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R2 and RMSE in a suggested GA fitness function. We calculated R2, RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model’s stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.

Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

“…It was a pioneering study that built the SVRK hybrid model and utilized it to map stand volume. The optimal RBF-kernel SVR model trained in this study as the first step achieved higher accuracy than multiple linear regressions and SVR models with various kernels based on similar multi-sensor satellite data [31,32], while the SVR model in this study was less accurate than that built by ALOS optical and SAR variables [20]. It was attributed to coarser spatial resolution of L band SAR data and the complex composition of tree species in the study area.…”

“…Stand volume modeling with open-access satellite data has been comparable, repeatable, and has long-term monitoring [28][29][30]. With the global coverage, Sentinel-1 C band synthetic aperture radar (SAR) and Sentinel-2 multispectral instrument (MSI) images provide capabilities for stand volume modeling using both active and passive remote sensing techniques [31,32]. The Advanced Land Observing Satellite (ALOS/ALOS-2) Phased Array type L band SAR (PALSAR/PALSAR-2) from L band SAR have penetrability, which contain comprehensive information on the orientation and structure of tree canopy and stems within the pixel [33,34].…”

“…The ALOS digital surface model (DSM) from L band interferometric SAR (InSAR) with accurate values of elevation and can provide useful topographic indices to estimate stand volume [37][38][39]. Reported studies have explored the potential of multi-sensor data using the SVR in volume mapping [31,40,41]. However, how volume predictions would be affected by using SAR and MSI predictors based on the SVR deserves further exploration.…”

Current Trends in Forest Ecological Applications of Three-Dimensional Remote Sensing: Transition from Experimental to Operational Solutions?

“…Thus, the Forests special issue "3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function" was conceptualized by the authors of this paper and finally hosted 10 peer-reviewed contributions in which 3D sources of remote sensing data were applied either as a preliminary or auxiliary sources of information to understand, classify, augment, model and predict forest ecological attributes. Geographically, the contributions published within this special issue were well distributed around the globe, including China (four contributions) [32][33][34][35], Canada [36], Germany [37], India [38], Iran [39], Panama [40] and the United States [41]. The geographical distribution of the countries in which the published contributions were carried out are summarized in Figure 1.…”

“…In terms of global climatic regimes and ecological biomes, the temperate biome included the majority of works with seven studies [33][34][35][36][37]39,41], followed by sub-tropical [32,38] and tropical [40] biomes. The topics covered within the published contributions can be divided into multiple groups: There were studies with rather classical applications such as single tree-level prediction of forest structural attributes by terrestrial laser scanning or visual estimation from Google Street View [33,41] and area-based prediction of forest structural attributes by space-borne stereo imagery, laser scanning or combination of passive optical with multi-frequency SAR data [34,35,39]. As an example, Ataee et al [39] proved that a combination of space-borne SAR and optical data could improve performance and reduce uncertainties in the retrieval of tree volume.…”