An integrated system for rapid assessment of ecological quality based on remote sensing data.

Ecological quality assessment (EQA) is important for regional socio-economic development and its sustainability. To assess the status of land ecological quality more precisely, an ecological quality assessment system with 11 indicators of ecological stability, ecosystem service function, and habitat stress was established using the analytic hierarchy process for Guangdong Province, a highly urbanized region of China. Remotely sensed data were mainly used to quantify the 11 indicators and acquire regional EQA graphs at high spatial resolution. In addition, we used the spatial autocorrelation measure Moran's I to capture dynamic signatures of spatial organization of ecological quality in the study area. The results show that the ecological quality of the study area is heterogeneous spatially but relatively consistent in different regions. Significant positive spatial autocorrelation for EQI in Guangdong was revealed by global Moran's I. Potential ecological hot spot or cold spot were detected based on the spatial clustering patterns that were obtained by local Moran's I. Lands with low ecological quality is mainly distributed in economically developed areas such as the Pearl River Delta and coastal cities in eastern and western Guangdong, while those with high ecological quality are mostly situated in northern mountainous areas that have lush vegetation. The low assessment scores for Guangdong, especially for the Pearl River Delta, are highly correlated with large populations and degrees of industrial agglomeration; this is mainly because urbanization and economic development jeopardize the environment. The presented case study can facilitate information provision and targeted strategy making for environmental protection. This study provides a helpful approach to assess and to analyze the ecological status in the future research. In contrast with methods that employ a single metric and limited data, the assessment system proposed in this study expands the potential application of the remotely sensed data and enriches the methodological system for EQAs.