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Ma L.,Xinjiang University | Ma L.,Laboratory for Oasis Ecosystem | Ma F.,Xinjiang Institute of Ecology and Geography | Ji Z.,Wuhan University | And 5 more authors.
Canadian Journal of Remote Sensing | Year: 2015

The economic and technological development zone (ETDZ) is a critical urban economic and functional area. Inefficient land exploitation and insufficient supervision have led to a great waste of land resources. The timely and precise extraction of residential and industrial building type, area, and density information is urgently needed and essential for sustainable land use development. This study attempted to extract residential and industrial buildings by integrating light detection and ranging (LiDAR) geometric, local indicators of spatial association (LISA), and lacunarity features with an object-based classification and postclassification processing approach. Geometric and LISA features were selected based on random forest cross-validation (rfcv) method. Grayscale lacunarity features were then incorporated for detailed classification using a support vector machine classifier and spatial neighbor processing method. An accuracy assessment indicated that the proposed method can effectively identify residential and industrial buildings. The overall classification accuracy and kappa statistics were 96.72% and 0.9538, respectively. A comparison with results derived from the normalized digital surface model (nDSM) alone and nDSM + multiple features (LiDAR geometric, LISA, and lacunarity) showed a significantly improved classification accuracy using kappa analysis. The results not only confirmed the applicability and effectiveness of the proposed method but also provided fundamental information for evaluating land use in the ETDZ. © 2015, Copyright © Taylor & Francis Group, LLC. Source

Wang F.,Xinjiang University | Wang F.,Laboratory for Oasis Ecosystem | Ding J.-L.,Xinjiang University | Ding J.-L.,Laboratory for Oasis Ecosystem
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2016

Only using soil spectrum to model soil salinity is not enough to meet the actual demands because of the complicated soil context. As a remotely sensed indicator, the vegetation type and its growing condition can provide a spatial overview of salinity distribution. Based on the synergistic relationship between soil salinity and vegetation in arid land, this paper tries to combine the spectrum of soil and vegetation to quantitatively estimate the salt content with the help of the concept of two-dimensional feature space. After the analysis of scatter diagram, the soil salinity detecting model was constructed to improve reasoning precision. However, because the impact of soil reflectance on the quantification of vegetation parameters under the individual pixel, the Normalized Difference Vegetation Index (NDVI) was difficult to accurately obtain sparse vegetation cover in arid areas. Therefore, in order to avoid the limitations of NDVI, the Combined Vegetation Indicative Factor(CVIF)was created and supported by Linear Spectral Unmixing Model (LSUM). Then, the study constructed the feature space based on the CVIF and salinity index (SI) and analyzed the response relationship between soil salinity and the trend of scattered points. Finally, a new and operational model termed Salinity Inference Model (SID) was developed. The results showed that the CVIF (R2 > 0.84, RMSE=3.92) performed better than NDVI(R2 > 0.66, RMSE=13.77), which means the CVIF was more appropriate for analyzing variations in vegetation cover (particularly halophytes) than NDVI in the study area. The SID was then compared to the Combined Cpectral Response Index (COSRI)(NDVI-based) from field measurements with respect to the soil salt content. The results indicated that the SID values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SID (R2 > 0.86, RMSE<6.86) compared to COSRI (R2=0.71, RMSE=16.21). These results suggested that the feature space related to biophysical properties combined with CVIF and SI can effectively provide information on soil salinity. © 2016, Peking University Press. All right reserved. Source

Zhang T.,Xinjiang University | Zhang T.,Laboratory for Oasis Ecosystem | Ding J.-L.,Xinjiang University | Ding J.-L.,Laboratory for Oasis Ecosystem | And 3 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2010

The characteristic of landscape spectrum is the basic of application of remote sensing and plays an important role in quantitative analysis of remote sensing. However, in spectrum-based application of remote sensing, because the difference of measuring scale and instrument resolution yield serious error in spectral curve and reflectance for the same landscape, there exists difficulty in quantitative retrieval of special information extraction of remote sensing. Firstly, the imaging simulation principles of the optics image was described and proposed A method using field measured endmember spectrum with higher spectrum resolutions to simulate spectrum of Multi-spectrum images with lower spectrum resolution was proposed. In the present paper, the authors take the delta oasis of Weigan and Kuqa rivers ocated in the North of Tarim Basin as study area, and choose vegetation and soil as study object. At first, we accomplished the simulation from field measured endmember for multi-spectrum by using the spectral response function of AVNIR-2, and found the large correlation between simulated multi-spectrum and pixel spectrum of AVNIR-2 by using the statistical analyse. Finally, the authors set up the linear model to accomplish the quantitative transformation from edmember scale to pixel scale. The result of this study has the realistic meaning for the quantitative application of remote sensing. Source

Ding J.-L.,Xinjiang University | Ding J.-L.,Laboratory for Oasis Ecosystem | Wu M.-C.,Xinjiang University | Wu M.-C.,Laboratory for Oasis Ecosystem | And 4 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2012

The present paper selected the spectral reflectivity of saline soil and vegetation of Weigan-Kuqa River Delta Oasis in the northern margin of the Tarim Basin in Xinjiang as objects, and used various spectral transforms to process the data with continum removed methods, derivate spectra, reciprocal, first order differential and root mean square etc, then analyzed the spectrum features and decided the most sensitive band ranges most relevant to salinization, and used field hyperspectral vegetation index, soil salinity index and measured synthetical spectral index to respectively establish hyperspectral quantitative models which could evaluate the soil salinization degrees. By comparing various spectral transformations of hyperspectral data the result showed that the first derivative of measured soil and vegetation hyperspectral were most sensitive to soil salinization degrees. The hyperspectral quantitative model based on measured synthetical spectral index could monitor soil salinization accurately and was better than the models simply based on vegetation index or soil salinity index. The research provided some scientific basis with soil salinization detection. Source

Yao Y.,Xinjiang University | Yao Y.,Laboratory for Oasis Ecosystem | Ding J.-L.,Xinjiang University | Ding J.-L.,Laboratory for Oasis Ecosystem | And 6 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2013

The present research attempts to establish a soil salinization monitoring model through a combination of remote and near sensing technologies. An electromagnetic induction instrument (EM38) was used to measure the electronic conductivity of soil samples collected from the delta oasis between the Weigan River and the Kuqa River in the north rim of the Tarim basin. Hyperspectral images were obtained via ASD Field Specpro FR and were transformed via 11 different approaches including root mean squares, logarithm, inversion, inversion-logarithm, continuum removal, and first order differentiation, etc. After the transformation, the obtained soil spectra that correlate well with soil electronic conductivity as measured by the EM38 were used to calculate five salinity indexes (salinity index 1, 2, and 3, normalized differential salinity index, and brightness index). Our analyses suggest that the salinity index 2 obtained via first order differentiation transformation of the spectra with the wavelengths of 456, 686 and 1 373 nm generated the highest correlation with salinity information derived via the EM38. By incorporating near sensing information (soil electronic conductivity information obtained via EM38), the current research provides a potentially more accurate approach to monitoring and predicting soil salinization in the future. Source

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