Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

被引:17
|
作者
Abdi, Abdulhakim M. [1 ,2 ]
Brandt, Martin [1 ]
Abel, Christin [1 ]
Fensholt, Rasmus [1 ]
机构
[1] Univ Copenhagen, Dept Geosci & Nat Resource Management, Copenhagen, Denmark
[2] Lund Univ, Ctr Environm & Climate Sci, Lund, Sweden
来源
JOURNAL OF REMOTE SENSING | 2022年 / 2022卷
基金
瑞典研究理事会;
关键词
SUB-SAHARAN AFRICA; WOODY PLANT ENCROACHMENT; SURFACE SOIL-MOISTURE; ACTIVE FIRE DETECTION; LEAF-AREA INDEX; HERBACEOUS VEGETATION; WORLDVIEW-2; IMAGERY; TROPICAL SAVANNAS; FRACTIONAL COVER; TIME-SERIES;
D O I
10.34133/2022/9835284
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Savannas cover a wide climatic gradient across large portions of the Earth's land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010-present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.
引用
收藏
页数:20
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