Progress and future of China’s annual report on remote sensing monitoring of global ecosystem and environment

NIU, Zheng; Li, J.; Gao, Zhong‐Hua; Gong, Enying; ZHANG, Songmei; Zhang, Jing; Liu, Shuang; OUYANG, Xiaoying; Zhang, Rui

doi:10.11834/jrs.20188060

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ecological environment is the basic guarantee for human survival and the foundation for social development. Dynamic changes in the ecological environment will directly affect the quality of human life [1]. Ecological environment quality (EEQ) generally refers to the quality of the ecological environment under the resources and environmental factors within a specific time and space range [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Eco-Environmental Quality of an Urban Forest Park Using LTSS and Modified RSEI from 1990 to 2020—A Case Study of Zijin Mountain National Forest Park, Nanjing, China

Ren,

Xu,

et al. 2023

Forests

View full text Add to dashboard Cite

Evaluating the long-term urban forest ecological environmental quality (EEQ) and analyzing the drivers of its spatiotemporal changes can provide a scientific basis for making long-term urban forest planning decisions. Taking into account the characteristics of urban forest parks with low area proportions of construction land and bare land, high vegetation coverage, and serious forest disturbances, we constructed a modified urban forest park EEQ evaluation index based on a remote sensing ecological index named MRSEI, which is composed of the Landsat enhanced vegetation index (EVI), wetness, land surface temperature (LST), and forest disturbance index (FDI). We selected the Nanjing Zijin Mountain National Forest Park as the study area, used landsat time series stack (LTSS) remote sensing images from 1990 to 2020 as the main data source, and adopted the suggested modified MRSEI, the Theil-Sen median method, and the Hurst index to evaluate the EEQ to analyze its spatiotemporal variations and its driving factors in the study area. The main research results were as follows: (1) the EEQ of Zijin Mountain showed an up-and-down, overall slowly increasing trend from 1990 to 2020, while the spatial auto-correlation coefficient showed an overall decreasing trend; (2) the area percentage of the EEQ-persistent region accounted for 78.69%, and the anti-sustainable region accounted for 21.31%; (3) the spatial centers of the EEQ in the study area were mainly concentrated on the middle and upper part of the southern slope of Zijin Mountain, moving southward from 1990 to 2020; (4) the analysis of drivers showed that climate factors, forest landscape structure, forest disturbances, and forest growth conditions were the main driving factors affecting the EEQ in the study area. These results provide a research framework for the analysis of EEQ changes over a long-term period in the urban forest parks of China.

show abstract

Section: Introductionmentioning

confidence: 99%

Analysis of Eco-Environmental Quality of an Urban Forest Park Using LTSS and Modified RSEI from 1990 to 2020—A Case Study of Zijin Mountain National Forest Park, Nanjing, China

Ren,

Xu,

et al. 2023

Forests

View full text Add to dashboard Cite

show abstract

Sugarcane Planting Area Classification, Extraction and Accuracy Comparison Based on Chinese High-Resolution Remote Sensing Satellite Data: A Case Study of Ningming Sugarcane Demonstration Area, Chongzuo City, Guangxi

Lou

Chen

Han

et al. 2020

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Remote sensing techniques are effective in sugarcane extraction and monitoring, but most of the existing research is based on low- and medium-resolution image. Thus, the technical methodology for high-resolution image needs to be improved. Due to the good performances of deep learning algorithms in solving classification problems for the very high resolution (VHR) images, the target mask U-Net model is introduced to research VHR satellite data from China, i. e., the GaoFen-1 (GF-1), GaoFen-2 (GF-2) and ZiYuan-3 (ZY-3). First, a sugarcane area was classified and extracted in the Ningming Sugarcane Demonstration Area in Chongzuo City, Guangxi. Further, we validated and compared the extraction accuracies for different satellite data. The results showed that the extraction accuracies of the GF-1, GF-2 and ZY-3 were 79.97% (Kappa coefficient of 0.19), 94.02% (Kappa coefficient of 0.82) and 81.94% (Kappa coefficient of 0.35), respectively. The spectral and textural information of high-resolution images can effectively guarantee improvements to the accuracy of crop extraction. By comparison of data sources and traditional supervision classification methods, the GF-2 data features the best results for sugarcane extraction. The technical methods and experimental results in this paper not only confirm the feasibility of applying China’s VHR data to monitor sugarcane planting areas, but also provides reference for the relevant future studies.

show abstract

Remote Sensing Monitoring of Ecological-Economic Impacts in the Belt and Road Initiatives Mining Project: A Case Study in Sino Iron and Taldybulak Levoberezhny

Jiang

Lin

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Under the Belt and Road Initiatives, China’s overseas cooperation in constructing mining projects has developed rapidly. The development and utilization of mining resources are essential requirements for socio-economic development. At the same time, the ecological impacts of the exploitation and utilization of mining resources have increasingly aroused the widespread concern of the international community. This paper uses Landsat images, high-resolution images, and nighttime light (NTL) data to remotely monitor Sino Iron in Australia and Taldybulak Levoberezhny in Kyrgyzstan in different development periods to provide a reference for the rational development of mineral resources and environmental management. The results show that the Chinese enterprises have achieved good results in the ecological protection of the mining area during the construction period. The development of the mine has caused minor damage to the surrounding environment and has not destroyed the local natural ecological pattern. The different NTL indices show an overall rising trend, indicating that the construction of mines has dramatically promoted the socio-economic development of countries along the Belt and Road in both time and space. Therefore, relevant departments should practice green development in overseas projects, establish a scientific mine governance system, and promote a win-win economic growth and environmental governance situation.

show abstract

Progress and future of China’s annual report on remote sensing monitoring of global ecosystem and environment

Cited by 3 publications

References 0 publications

Analysis of Eco-Environmental Quality of an Urban Forest Park Using LTSS and Modified RSEI from 1990 to 2020—A Case Study of Zijin Mountain National Forest Park, Nanjing, China

Analysis of Eco-Environmental Quality of an Urban Forest Park Using LTSS and Modified RSEI from 1990 to 2020—A Case Study of Zijin Mountain National Forest Park, Nanjing, China

Sugarcane Planting Area Classification, Extraction and Accuracy Comparison Based on Chinese High-Resolution Remote Sensing Satellite Data: A Case Study of Ningming Sugarcane Demonstration Area, Chongzuo City, Guangxi

Remote Sensing Monitoring of Ecological-Economic Impacts in the Belt and Road Initiatives Mining Project: A Case Study in Sino Iron and Taldybulak Levoberezhny

Contact Info

Product

Resources

About