Mapping terrestrial ecosystem health in drylands: comparison of field-based information with remotely sensed data at watershed level

Autor: Mojdeh Safaei, Hossein Bashari, Till Kleinebecker, Sima Fakheran, Reza Jafari, André Große-Stoltenberg
Rok vydání: 2022
Předmět:
Zdroj: Landscape Ecology. 38:705-724
ISSN: 1572-9761
0921-2973
Popis: Context Combining field-based assessments with remote-sensing proxies of landscape patterns provides the opportunity to monitor terrestrial ecosystem health status in support of sustainable development goals (SDG). Objectives Linking qualitative field data with quantitative remote-sensing imagery to map terrestrial ecosystem health (SDG15.3.1 “land degradation neutrality”). Methods A field-based approach using the Interpreting Indicators of Rangeland-Health (IIRH) protocol was applied to classify terrestrial ecosystem health status at the watershed level as “healthy”, “at-risk”, and “unhealthy”. Quantitative complex landscape metrics derived from Landsat spaceborne data were used to explore whether similar health statuses can be retrieved on a broader scale. The assignment of terrestrial ecosystem health classes based on field and the remotely sensed metrics were tested using multivariate and cluster analysis methods. Results According to the IIRH assessments, soil surface loss, plant mortality, and invasive species were identified as important indicators of health. According to the quantitative landscape metrics, “healthy” sites had lower amounts of spectral heterogeneity, edge density, and resource leakage. We found a high agreement between health clusters based on field and remote-sensing data (NMI = 0.91) when using a combined approach of DBSCAN and k-means clustering together with non-metric multi-dimensional scaling (NMDS). Conclusions We provide an exemplary workflow on how to combine qualitative field data and quantitative remote-sensing data to assess SDGs indicators related to terrestrial ecosystem health. As we used a standardized method for field assessments together with publicly available satellite data, there is potential to test the generalizability and context-dependency of our approach in other arid and semi-arid rangelands.
Databáze: OpenAIRE
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