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Estimating plant traits of alpine grasslands on the Qinghai-Tibetan plateau using remote sensing


Li, Chengxiu; Wulf, Hendrik; Schmid, Bernhard; He, Jin-Sheng; Schaepman, Michael E (2018). Estimating plant traits of alpine grasslands on the Qinghai-Tibetan plateau using remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(7):2263-2275.

Abstract

Mapping plants traits on the Qinghai-Tibetan Plateau grassland is important for understanding ecosystem functions and how plants respond to global change. Detailed trait maps for the complete Qinghai-Tibetan Plateau are missing. Here, we addressed this issue by combining Sentinel-2 and Landsat-8 multispectral satellite data with field measurements to map and compare plant traits of meadow and steppe communities across the complete Qinghai-Tibetan Plateau. We measured in-situ plant-level traits of CHLorophyll content (CHL), specific plant area (SPA = plant area / plant dry mass), and plant dry matter content (PDMC = plant dry mass / fresh mass). We hypothesized that plant-level traits of SPA and PDMC are close to community-weighted means (CWMs) of specific leaf area (SLA) and leaf dry matter content (LDMC) because leaves represent the largest fraction of aboveground biomass in the Qinghai-Tibetan Plateau grasslands. Despite vastly different measurement methods, we found that the remotely sensed traits (SPA and PDMC) correlated with literature-derived leaf traits of CWMs of SLA and LDMC. Both remotely sensed and field-measured results showed that alpine meadow plants reveal a wider range and higher averages of CHL and SPA but lower PDMC compared with alpine steppe plants. These trait differences between vegetation types indicate faster growth of alpine meadow and higher resilience to harsh conditions of alpine steppe, representing differences in adaptation strategies to environmental conditions. Our study demonstrates that remote sensing can be used to estimate plant traits in alpine grasslands with potential applications to retrieve functional diversity and correlated ecosystem functions in future studies.

Abstract

Mapping plants traits on the Qinghai-Tibetan Plateau grassland is important for understanding ecosystem functions and how plants respond to global change. Detailed trait maps for the complete Qinghai-Tibetan Plateau are missing. Here, we addressed this issue by combining Sentinel-2 and Landsat-8 multispectral satellite data with field measurements to map and compare plant traits of meadow and steppe communities across the complete Qinghai-Tibetan Plateau. We measured in-situ plant-level traits of CHLorophyll content (CHL), specific plant area (SPA = plant area / plant dry mass), and plant dry matter content (PDMC = plant dry mass / fresh mass). We hypothesized that plant-level traits of SPA and PDMC are close to community-weighted means (CWMs) of specific leaf area (SLA) and leaf dry matter content (LDMC) because leaves represent the largest fraction of aboveground biomass in the Qinghai-Tibetan Plateau grasslands. Despite vastly different measurement methods, we found that the remotely sensed traits (SPA and PDMC) correlated with literature-derived leaf traits of CWMs of SLA and LDMC. Both remotely sensed and field-measured results showed that alpine meadow plants reveal a wider range and higher averages of CHL and SPA but lower PDMC compared with alpine steppe plants. These trait differences between vegetation types indicate faster growth of alpine meadow and higher resilience to harsh conditions of alpine steppe, representing differences in adaptation strategies to environmental conditions. Our study demonstrates that remote sensing can be used to estimate plant traits in alpine grasslands with potential applications to retrieve functional diversity and correlated ecosystem functions in future studies.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Uncontrolled Keywords:Computers in Earth Sciences, Atmospheric Science
Language:English
Date:2018
Deposited On:27 Jun 2018 12:34
Last Modified:24 Sep 2019 23:31
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1939-1404
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/JSTARS.2018.2824901

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