An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

Quantifying Urban Expansion from the Perspective of Geographic Data: A Case Study of Guangzhou, China

Understanding and quantifying urban expansion is critical to urban management and urban planning. The accurate delineation of built-up areas (BUAs) is the foundation for quantifying urban expansion. To quantify urban expansion simply and efficiently, we proposed a method for delineating BUAs using geographic data, taking Guangzhou as the study area. First, Guangzhou’s natural cities (NCs) in 2014 and 2020 were derived from the point of interest (POI) data. Second, multiple grid maps were combined with NCs to delineate BUAs. Third, the optimal grid map for delineating BUA was determined based on the real BUA data and applying accuracy evaluation indexes. Finally, by comparing the 2014 and 2020 BUAs delineated by the optimal grid maps, we quantified the urban expansion occurring in Guangzhou. The results demonstrated the following. (1) The accuracy score of the BUAs delineated by the 200 m × 200 m grid map reaches a maximum. (2) The BUAs in the central urban area of Guangzhou had a smaller area of expansion, while the northern and southern areas of Guangzhou experienced considerable urban expansion. (3) The BUA expansion was smaller in all spatial orientations in the developed district, while the BUA expansion was larger in all spatial orientations in the developing district. This study provides a new method for delineating BUAs and a new perspective for mapping the spatial distribution of urban BUAs, which helps to better understand and quantify urban expansion.

Integrating Multi-Sensors Data for Species Distribution Mapping Using Deep Learning and Envelope Models

The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit modelling of regional ecological responses and their impact on individual species is a significant prerequisite for any adaptation strategy. The present study focuses on predicting the regional distribution of Rhododendron arboreum, a medicinal plant species found in the Himalayan region. Advanced Species Distribution Models (SDM) based on the principle of predefined hypothesis, namely BIOCLIM, was used to model the potential distribution of Rhododendron arboreum. This hypothesis tends to vary with the change in locations, and thus, robust models are required to establish nonlinear complex relations between the input parameters. To address this nonlinear relation, a class of deep neural networks, Convolutional Neural Network (CNN) architecture is proposed, designed, and tested, which eventually gave much better accuracy than the BIOCLIM model. Both of the models were given 16 input parameters, including ecological and atmospheric variables, which were statistically resampled and were then utilized in establishing the linear and nonlinear relationship to better fit the occurrence scenarios of the species. The input parameters were mostly acquired from the recent satellite missions, including MODIS, Sentinel-2, Sentinel-5p, the Shuttle Radar Topography Mission (SRTM), and ECOSTRESS. The performance across all the thresholds was evaluated using the value of the Area Under Curve (AUC) evaluation metrics. The AUC value was found to be 0.917 with CNN, whereas it was 0.68 with BIOCLIM, respectively. The performance evaluation metrics indicate the superiority of CNN for species distribution over BIOCLIM.