Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar

Abstract

Light Detection and Ranging (Lidar) is a state of the art technology to assess forest aboveground biomass (AGB). To date, methods developed to relate Lidar metrics with forest parameters were built upon the vertical component of the data. In multi-layered tropical forests, signal penetration might be restricted, limiting the efficiency of these methods. A potential way for improving AGB models in such forests would be to combine traditional approaches by descriptors of the horizontal canopy structure. We assessed the capability and complementarity of three recently proposed methods for assessing AGB at the plot level OPEN ACCESSRemote Sens. 2015, 7 10608 using point distributional approach (DM), canopy volume profile approach (CVP), 2D canopy grain approach (FOTO), and further evaluated the potential of a topographical complexity index (TCI) to explain part of the variability of AGB with slope. This research has been conducted in a mountainous wet evergreen tropical forest of Western Ghats in India. AGB biomass models were developed using a best subset regression approach, and model performance was assessed through cross-validation. Results demonstrated that the variability in AGB could be efficiently captured when variables describing both the vertical (DM or CVP) and horizontal (FOTO) structure were combined. Integrating FOTO metrics with those of either DM or CVP decreased the root mean squared error of the models by 4.42% and 6.01%, respectively. These results are of high interest for AGB mapping in the tropics and could significantly contribute to the REDD+ program. Model quality could be further enhanced by improving the robustness of field-based biomass models and influence of topography on area-based Lidar descriptors of the forest structure.

Transectos de datos LiDAR: una estrategia de muestreo para estimar biomasa aérea en áreas forestales

“…En el caso de la SMSP, la altura media y dos percentiles bajos (P20 y P25, 4.79 m -5.32 m) fueron los que correlacionaron mejor con la biomasa de este tipo de selva, lo cual es consistente con lo indicado por Véga et al (2015). En el estudio de d'Oliveira, Reutebuch, McGaughey y Andersen (2012), realizado con el propósito de estimar la biomasa aérea en un bosque tropical en Brasil, los autores señalaron que el P25 y la varianza de la altura fueron las mejores variables explicativas de los modelos.…”

“…No obstante, cuando se comparan los resultados de este trabajo con estudios similares de estimación de biomasa aérea en bosques tropicales existen diferencias notables. Por ejemplo, en un estudio realizado en la amazonia brasileña, d 'Oliveira et al (2012) lograron explicar la varianza hasta 72% (RMSE = 40.2 Mg ha -1 ); mientras que Véga et al (2015) estimaron la biomasa aérea en un bosque tropical húmedo perenne en Western Ghats, India, con una R 2 = 0.96 (RMSEvc = 28.83 Mg ha -1 ). Por otro lado, Meyer et al (2013), utilizando sitios de muestreo del mismo tamaño que este estudio (0.04 ha), en un bosque tropical húmedo, hallaron una R 2 de 0.19 (RMSE = 184.6 Mg ha -1 ) y 0.28 (RMSE = 173.8 Mg ha -1 ) para dos tipos de sensores LiDAR, respectivamente.…”

Wavelet Based Analysis of TanDEM-X and LiDAR DEMs across a Tropical Vegetation Heterogeneity Gradient Driven by Fire Disturbance in Indonesia

“…Tropical forests structural heterogeneity is driven by a combination of processes: underlying topographic structure and canopy structure [54]. De-coupling the two processes to gain a better understanding of variations in canopy structure alone was considered by analysis of wavelet variance and wavelet co-variance.…”

“…Canopy structure (roughness) measures derived from 3D datasets can provide information on landscape disturbance and recovery [25] but can be affected by environmental factors [53] such as the underlying topography especially in steep terrain [54]. Since the transect is characterized by low-lying undulating terrain (52.7˘3.2 m) and often the presence of mild slopes (12.1˝˘10˝) and at higher elevation (81.4˘11.2 m) and slopes (12˝˘4.3˝) in AG and MS plots (elevation: 64.9˘21.4 m, slopes: 11.9˝˘6.2˝), understanding of topographic structure is important to gain a better insight on the processes that play a role and the extent to which they influence the 3D information provided by LiDAR and InSAR.…”

“…Also forest disturbance has been addressed using these techniques (e.g., selective logging: [52,53]). Canopy grain analysis from FOTO applied to LiDAR derived Canopy Height Model (CHM) and Digital Surface Model (DSM) was also used to generate metrics related to structure and to ultimately improve above-ground biomass prediction [32,54]. The extraction of single crowns from LiDAR has also been explored [55].…”