These are data files and code associated with, Satellite-driven evaluation of ecological environmental quality based on the PSR framework
Abstract:
Ever-increasing human activities have resulted in significant environmental degradation. It is crucial for environmental protection to monitor and evaluate the ecological environmental quality (EEQ) in a timely and accurate manner. Remote sensing technology has been widely used to quantify EEQ. However, current remote sensing EEQ evaluation methods suffer from deficiencies with regard to the indicator system and the EEQ quantification, reducing the accuracy of EEQ evaluations. Therefore, this study proposes a novel method to evaluate EEQ. Remote-sensing indicators used in the pressure-state-response (PSR) framework are selected based on the traditional EEQ evaluation system, and deep neural networks (DNNs) are used to quantify EEQ. A case study is conducted in Guangdong and Guangzhou city, China, to validate the method. The trends in EEQ from 2013 to 2019 are analyzed using the Sen and Mann-Kendall (MK) tests in Guangzhou city. The results show the following. (1) The proposed method has a significantly higher EEQ estimation accuracy (determination coefficient (R2) of 0.75 and normalized root mean square error (NRMSE) of 13.61%) than the commonly used remote sensing ecological index (RSEI) method (R2 of 0.51 and NRMSE of 19.13%). (2) The five remote sensing indicators (RSI) in the PSR framework are highly correlated with EEQ, with a minimum |r| = 0.52. (3) In Guangzhou, the EEQ increased from southwest to northeast and showed an increasing trend from 2013 to 2019, consistent with actual conditions. This study provides a new strategy for the high-accuracy estimation of EEQ based on remote sensing data.
File list: EEQ measurements by district and county in Guangdong Province( EEQ measurements.zip ) PM2.5 concentration data ( PM2.5_ECHAP_PM2.5_Y1K_2018_V3_flip.zip ) Land cover data ( CLCD_v01_2016_albert_guangdong.zip ) ASTER GDEM ( DEM.zip ) GPM_3IMERGM L3 1-month V06 ( GPM monthly precipitation data downloading(GEE code).txt ) Landsat 8 OLI/TIRS ( NDVI LSM and EVI downloading(GEE code).txt )
Data from the Google Earth Enging (GEE) platform were provided in the form of code. Soil type data was provided by the local land reclamation centre under a confidentiality agreement. In respect of this agreement, we are unable to make this data publicly available. Fur...
Abstract:
Ever-increasing human activities have resulted in significant environmental degradation. It is crucial for environmental protection to monitor and evaluate the ecological environmental quality (EEQ) in a timely and accurate manner. Remote sensing technology has been widely used to quantify EEQ. However, current remote sensing EEQ evaluation methods suffer from deficiencies with regard to the indicator system and the EEQ quantification, reducing the accuracy of EEQ evaluations. Therefore, this study proposes a novel method to evaluate EEQ. Remote-sensing indicators used in the pressure-state-response (PSR) framework are selected based on the traditional EEQ evaluation system, and deep neural networks (DNNs) are used to quantify EEQ. A case study is conducted in Guangdong and Guangzhou city, China, to validate the method. The trends in EEQ from 2013 to 2019 are analyzed using the Sen and Mann-Kendall (MK) tests in Guangzhou city. The results show the following. (1) The proposed method has a significantly higher EEQ estimation accuracy (determination coefficient (R2) of 0.75 and normalized root mean square error (NRMSE) of 13.61%) than the commonly used remote sensing ecological index (RSEI) method (R2 of 0.51 and NRMSE of 19.13%). (2) The five remote sensing indicators (RSI) in the PSR framework are highly correlated with EEQ, with a minimum |r| = 0.52. (3) In Guangzhou, the EEQ increased from southwest to northeast and showed an increasing trend from 2013 to 2019, consistent with actual conditions. This study provides a new strategy for the high-accuracy estimation of EEQ based on remote sensing data.
File list: EEQ measurements by district and county in Guangdong Province( EEQ measurements.zip ) PM2.5 concentration data ( PM2.5_ECHAP_PM2.5_Y1K_2018_V3_flip.zip ) Land cover data ( CLCD_v01_2016_albert_guangdong.zip ) ASTER GDEM ( DEM.zip ) GPM_3IMERGM L3 1-month V06 ( GPM monthly precipitation data downloading(GEE code).txt ) Landsat 8 OLI/TIRS ( NDVI LSM and EVI downloading(GEE code).txt )
Data from the Google Earth Enging (GEE) platform were provided in the form of code. Soil type data was provided by the local land reclamation centre under a confidentiality agreement. In respect of this agreement, we are unable to make this data publicly available. Fur...