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Quantitive Retrieval of Sparse Vegetation Cover in Arid Regions Using Hyperspectral Data

Author: LiXiaoSong
Tutor: LiZengYuan
School: Chinese Academy of Forestry
Course: Forest Management
Keywords: hyperspectral remote sensing sparse vegetation cover vegetation index stepwise multiple linear regression partial least squares artifical neural net spectral mixture analysis
CLC: Q948
Type: PhD thesis
Year: 2008
Downloads: 1187
Quote: 7
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Accurately acquiring sparse vegetation cover in arid regions, which is the foundation work for desertification evaluation, can provide effective technical support for the establishment of scientific anti-desertification decision-making. Field measurement is the traditional way to get the vegetation cover. However, it is time-consuming, laborious, and feasible only to small-scale vegetation cover survey. Remote sensing provides new means for vegetation acquirement, especially for fast, accurate access to vegetation cover in large areas. Now, remote sensing has become a primary means to large-scale vegetation cover investigation. However, owing to arid regions’particularity, retrieving sparse vegetation cover from remote sensing presents some siginificant challenges. The first and most obvious is the fact that because vegetation cover is low, the contribution of vegetation to the area-averaged reflectance of a pixel is small. Furthermore, because of their low organic matter content, soils in arid regions tend to be bright and mineralogically heterogeneous. All of these factors tend to swamp out the spectral contribution of vegetation in individual pixels; Secondly, open canopies and bright soils in arid regions can contribute to significant multiple scattering and nonlinear mixing; Finally, vegetation is spectrally dissimilar to its humid counterparts lacking, most notably, a strong red edge in arid regions. Therefore, improving detection ability of sparse vegetation cover in arid regions, based on remote sensing, is the most serious challenge.Hyperspectral remote sensing, one of the major technological breakthroughs in earth-observing field in the last two decades in the 20th century, is currently the forefront of remote sensing technology, which utilize a lot of very narrow (<10 nm) and continuous electromagnetic wave bands to obtain relevant data information, can produce a complete and continuous spectrum, and with incomparable advantages over commonly used multi-spectral remote sensing. However, acquisition of hyperspectral data is very difficult, therefore, large-scale vegetation cover quantitative inversion based on hyperspectral remote sensing did not receive in-depth study and widely application, related research on sparse vegetation in arid regions is scanty. Recently, satellite hyperspectral data is open to civilian, which solve the hyperspectral data access problem successfully. Therefore, it is high time to systematically, in-depth study sparse vegetation cove quantitative retrieval methods in arid regions based on hyperspectral remote sensing, fully tap the potential of hyperspectral remote sensing, and successfully expand hyperspectral remote sensing application field.Aim to this, we conduct a comprehensive and systematic study to retrieve sparse vegetation cover in Minqin oasis-desert transitional zone based on Hyperion image. Methods we used include vegetation index, regression model and spectral mixture analysis. We exploit each method’s potential to retrieve sparse vegetation cover, and then conduct a comparative analysis among different methods. The results show that:(1) Hyperspectral vegetation indices are significantly better than the corresponding wide-band vegetation indices for sparse vegetation detection, atmospherically resistant vegetation index (ARVI) based on specific Hyperion narrow-band (833.3/640.5 nm) performs best, with a high R2 (up to 0.7294), and a low cross validation RMSE (5.5488);(2) Partial least squares (PLS) regression and artificial neural net (ANN) have great potential to estimate sparse vegetation cover. From validation results with five independent samples, we can find: PLS regression models based on the original 176-band reflectance performs best in all PLS regression models, with a low validation RMSE (3.8197, 19% of mean); ANN models, taking principal components as input, which is compact, not related, but include most original image’s useful information, performs best in all ANN models, with a low validation RMSE (3.2806, 16% of mean);(3) Sparse vegetation fraction, based on fully constrained spectral mixture analysis with sparse vegetation, false Gobi and sand three endmembers, and field measured vegetation cover are highly correlated(R~2 =0.9141). The differences are less than 5% for all samples between them, and RMSE is 3.0681, about 22% of mean;(4) With highest extensibility and accuracy, spectral mixture analysis is the best methods for retrieving sparse vegetation cover in arid regions, and which performs better than vegetation index and regression model obviously.Vegetation index method’s accuracy is relative minimum, but it’s simple, and when applies to other regions, it just need simple, necessary sample investigation. Regression model’s accuracy and maneuverability is both moderate, but its extensibility is worst, and which need a lot of samples to support. Overall, we realize high-precision quantitative retrieval based on Hyperion image of sparse vegetation cover in arid regions, fully tap the potential of hyperspectral remote sensing, and extend another hyperspectral remote sensing successful application field.

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