Arid
DOI10.1080/01431161.2018.1463114
A comparative evaluation of arid inflow-dependent vegetation maps derived from LANDSAT top-of-atmosphere and surface reflectances
Emelyanova, Irina1; Barron, Olga2; Alaibakhsh, Masoomeh3
通讯作者Emelyanova, Irina
来源期刊INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
EISSN1366-5901
出版年2018
卷号39期号:20页码:6607-6630
英文摘要

In remote sensing, it is commonly accepted that land remote-sensing satellite (LANDSAT) top-of-atmosphere (TOA) reflectance is less accurate than atmospheric correction (AC) reflectance, as the former is not calibrated for possible modifications in the electromagnetic radiation signals due to atmospheric scattering and absorption. This article investigates whether LANDSAT data calibrated for TOA reflectance are an appropriate information source for delineating inflow-dependent vegetation (IDV) in regions with an arid and desert climate, such as the Pilbara region in Western Australia. Knowledge of where IDVs are in the landscape underpins planning their protection and define the baseline for their monitoring when water resource management options are considered. The appropriateness of TOA calibration for the delineation of IDV in the Pilbara was assessed through its comparison with IDV maps derived from AC reflectance. Both radiometric calibration methods (TOA and AC) were applied to a multi-date LANDSAT 5 TM (Thematic Mapper) dataset of 10 images acquired in 2009 and 2010. Two methods based on the application of remote-sensing techniques to identify the extent of temporally invariant vegetation were applied for IDV delineation in the study area. The first method, groundwater-dependent ecosystems mapping (GEM), employs a two-date normalized difference vegetation index (NDVI) dataset for identifying ’no-change’ clusters of land cover and detecting those related to IDV. The second method applies principal component analysis (PCA) to a multi-date NDVI dataset. The first principal component (PC1) typically contains features that remain unchanged over time. This includes vegetation with continuous or frequent access to surface and/or groundwater, such as IDV. To delineate the extent of IDV, a thresholding technique was further employed. Spatial similarity between IDV maps produced from TOA and AC reflectance was quantitatively evaluated by the Kappa coefficient. The results showed that TOA and AC IDV maps are in ’almost perfect’ agreement with the Kappa values above 0.83. This suggests that TOA reflectance is equally appropriate to AC reflectance for mapping in arid and desert climate such as in Pilbara. When the GEM- and PCA-based methods are applied in other study areas with arid or desert climate, the accuracy of the delineated IDV extent may vary. Therefore, the results need to be validated using ground-truth information about known IDV occurrences in the area of interest.


类型Article
语种英语
国家Australia
收录类别SCI-E
WOS记录号WOS:000450862400009
WOS关键词DIFFERENCE WATER INDEX ; RADIOMETRIC CORRECTION ; BRDF CORRECTION ; COVER ; TM ; CLASSIFICATION ; ECOSYSTEMS ; MOISTURE ; IMAGES ; FOREST
WOS类目Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向Remote Sensing ; Imaging Science & Photographic Technology
来源机构Commonwealth Scientific and Industrial Research Organisation
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/210257
作者单位1.CSIRO, Energy, Bentley, WA, Australia;
2.CSIRO, Land & Water, Wembley, WA, Australia;
3.Edith Cowan Univ, Sch Engn, Mt Lawley, WA, Australia
推荐引用方式
GB/T 7714
Emelyanova, Irina,Barron, Olga,Alaibakhsh, Masoomeh. A comparative evaluation of arid inflow-dependent vegetation maps derived from LANDSAT top-of-atmosphere and surface reflectances[J]. Commonwealth Scientific and Industrial Research Organisation,2018,39(20):6607-6630.
APA Emelyanova, Irina,Barron, Olga,&Alaibakhsh, Masoomeh.(2018).A comparative evaluation of arid inflow-dependent vegetation maps derived from LANDSAT top-of-atmosphere and surface reflectances.INTERNATIONAL JOURNAL OF REMOTE SENSING,39(20),6607-6630.
MLA Emelyanova, Irina,et al."A comparative evaluation of arid inflow-dependent vegetation maps derived from LANDSAT top-of-atmosphere and surface reflectances".INTERNATIONAL JOURNAL OF REMOTE SENSING 39.20(2018):6607-6630.
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