Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review
Ecosystem conditions at the regional level are critical factors for environmental management, public awareness, and land use decision making. Regional ecosystem conditions may be examined from the perspectives of ecosystem health, vulnerability, and security, as well as other conceptual frameworks....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/8/4101 |
| _version_ | 1797603484564979712 |
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| author | Chunhua Zhang Kelin Wang Yuemin Yue Xiangkun Qi Mingyang Zhang |
| author_facet | Chunhua Zhang Kelin Wang Yuemin Yue Xiangkun Qi Mingyang Zhang |
| author_sort | Chunhua Zhang |
| collection | DOAJ |
| description | Ecosystem conditions at the regional level are critical factors for environmental management, public awareness, and land use decision making. Regional ecosystem conditions may be examined from the perspectives of ecosystem health, vulnerability, and security, as well as other conceptual frameworks. Vigor, organization, and resilience (VOR) and pressure–stress–response (PSR) are two commonly adopted conceptual models for indicator selection and organization. The analytical hierarchy process (AHP) is primarily used to determine model weights and indicator combinations. Although there have been many successful efforts in assessing regional ecosystems, they remain affected by a lack of spatially explicit data, weak integration of natural and human dimensions, and uncertain data quality and analyses. In the future, regional ecosystem condition assessments may be advanced by incorporating recent improvements in spatial big data and machine learning to create more operative indicators based on Earth observations and social metrics. The collaboration between ecologists, remote sensing scientists, data analysts, and scientists in other relevant disciplines is critical for the success of future assessments. |
| first_indexed | 2024-03-11T04:32:47Z |
| format | Article |
| id | doaj.art-3005371841154b86a610a32c5d15ec4c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:32:47Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3005371841154b86a610a32c5d15ec4c2023-11-17T21:19:05ZengMDPI AGSensors1424-82202023-04-01238410110.3390/s23084101Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A ReviewChunhua Zhang0Kelin Wang1Yuemin Yue2Xiangkun Qi3Mingyang Zhang4Department of Biology, Algoma University, Sault Ste. Marie, ON P6A2G4, CanadaKey Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, ChinaKey Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, ChinaKey Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, ChinaKey Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, ChinaEcosystem conditions at the regional level are critical factors for environmental management, public awareness, and land use decision making. Regional ecosystem conditions may be examined from the perspectives of ecosystem health, vulnerability, and security, as well as other conceptual frameworks. Vigor, organization, and resilience (VOR) and pressure–stress–response (PSR) are two commonly adopted conceptual models for indicator selection and organization. The analytical hierarchy process (AHP) is primarily used to determine model weights and indicator combinations. Although there have been many successful efforts in assessing regional ecosystems, they remain affected by a lack of spatially explicit data, weak integration of natural and human dimensions, and uncertain data quality and analyses. In the future, regional ecosystem condition assessments may be advanced by incorporating recent improvements in spatial big data and machine learning to create more operative indicators based on Earth observations and social metrics. The collaboration between ecologists, remote sensing scientists, data analysts, and scientists in other relevant disciplines is critical for the success of future assessments.https://www.mdpi.com/1424-8220/23/8/4101ecosystem conditionsregional assessmentlandscape patternremote sensingspatial big data |
| spellingShingle | Chunhua Zhang Kelin Wang Yuemin Yue Xiangkun Qi Mingyang Zhang Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review Sensors ecosystem conditions regional assessment landscape pattern remote sensing spatial big data |
| title | Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review |
| title_full | Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review |
| title_fullStr | Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review |
| title_full_unstemmed | Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review |
| title_short | Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review |
| title_sort | assessing regional ecosystem conditions using geospatial techniques a review |
| topic | ecosystem conditions regional assessment landscape pattern remote sensing spatial big data |
| url | https://www.mdpi.com/1424-8220/23/8/4101 |
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