• Energy Research

Knowledge of forest productivity status is an important indicator of the amount of biomass accumulated and the role of terrestrial ecosystems in the carbon cycle. However, accurate and up-to-date information on forest biomass and forest succession remain rudimentary within natural forests. This study sought to understand and establish the potential of a new-generation sensor in estimating aboveground biomass (AGB) stored in the natural forest, also known as ‘community forest’ or buffer zone community forest (BZCF), in the Parsa National Park, Nepal. The utility of the 30-m resolution Landsat 8 Operational Land Imager (OLI) and in situ data was tested using two statistical approaches, namely multiple linear regression (MLR) and random forest (RF). The analysis was done based on four computational procedures. These included spectral bands, vegetation indices and pooled dataset (spectral bands + vegetation indices), and model selected important variables. AGB estimation based on pooled data showed that the RF algorithm produced better results when compared to the use of the MLR model. For instance, the RF model estimated AGB with an R2 value of 0.87 and a root mean square error of 20.50 t ha−1, as well as an R2 value of 0.95 and a RMSE of 13.3 t ha−1 when using selected important variables. Comparatively, the MLR using pooled data produced an R2 value of 0.56 and RMSE value of 37.01 t ha−1. The RF model selected Optimized Soil Adjusted Vegetation index (OSAVI), Simple ratio (SR), Modified simple ratio (MSR), and Normalized difference Vegetation index (NDVI) as the most important variables for estimating AGB, whereas MLR selected band 5 and SR. These findings demonstrate the relevance of the relatively new Landsat 8 sensor in the estimation of AGB in community buffer zones.

Changing climate will jeopardize biodiversity, particularly the geographic distribution of endemic species. One such species is the Javan Hawk-Eagle (JHE, Nisaetus bartelsi), a charismatic raptor found only on Java Island, Indonesia. Thus, it is crucial to develop an appropriate conservation strategy to preserve the species. Ecological niche modeling is considered a valuable tool for designing conservation plans for the JHE. We provide an ecological niche modeling approach and transfer its model to future climate scenarios for the JHE. We utilize various machine learning algorithms under sustainability and business-as-usual (BAU) scenarios for 2050. Additionally, we investigate the conservation vulnerability of the JHE, capturing multifaceted pressures on the species from climate dissimilarities and human disturbance variables. Our study reveals that the ensemble model performs exceptionally well, with temperature emerging as the most critical factor affecting the JHE distribution. This finding indicates that climate change will have a significant impact on the JHE species. Our results suggest that the JHE distribution will likely decrease by 28.41% and 40.16% from the current JHE distribution under sustainability and BAU scenarios, respectively. Furthermore, our study reveals high-potential refugia for future JHE, covering 7,596 km2 (61%) under the sustainability scenario and only 4,403 km2 (35%) under the BAU scenario. Therefore, effective management and planning, including habitat restoration, refugia preservation, habitat connectivity, and local community inclusivity, should be well-managed to achieve JHE conservation targets.