Abdulla Abliz

Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.

A WFS-SVM Model for Soil Salinity Mapping in Keriya Oasis, Northwestern China Using Polarimetric Decomposition and Fully PolSAR Data

Timely monitoring and mapping of salt-affected areas are essential for the prevention of land degradation and sustainable soil management in arid and semi-arid regions. The main objective of this study was to develop Synthetic Aperture Radar (SAR) polarimetry techniques for improved soil salinity mapping in the Keriya Oasis in the Xinjiang Uyghur Autonomous Region (Xinjiang), China, where salinized soil appears to be a major threat to local agricultural productivity. Multiple polarimetric target decomposition, optimal feature subset selection (wrapper feature selector, WFS), and support vector machine (SVM) algorithms were used for optimal soil salinization classification using quad-polarized PALSAR-2 data. A threefold exercise was conducted. First, 16 polarimetric decomposition methods were implemented and a wide range of polarimetric parameters and SAR discriminators were derived in order to mine hidden information in PolSAR data. Second, the optimal polarimetric feature subset that constitutes 19 polarimetric elements was selected adopting the WFS approach; optimum classification parameters were identified, and the optimal SVM classification model was obtained by employing a cross-validation method. Third, the WFS-SVM classification model was constructed, optimized, and implemented based on the optimal match of polarimetric features and optimum classification parameters. Soils with different salinization degrees (i.e., highly, moderately and slightly salinized soils) were extracted. Finally, classification results were compared with the Wishart supervised classification and conventional SVM classification to examine the performance of the proposed method for salinity mapping. Detailed field investigations and ground data were used for the validation of the adopted methods. The overall accuracy and kappa coefficient of the proposed WFS-SVM model were 87.57% and 0.85, respectively that were much higher than those obtained by the Wishart supervised classification with values of 73.87% and 0.68, as well as those of the commonly applied SVM classification of 83.61% and 0.80. Accuracy of different salinized soil mapping was also enhanced with the proposed methodology. The results showed that the proposed method outperformed the Wishart and SVM classification, and demonstrated the advantages offered by the WFS-SVM classification and potentials of PolSAR data in the monitoring soil salinization.

Providing Urban Ecosystem Services for Human Well-Being: A Comparison of Exemplary Institutional Arrangements in China and Germany

Urbanization is a worldwide phenomenon and a major driver of global environmental change. For example, 75 % of the annual CO2 emissions are produced in cities and towns. Currently, more than half of the world’s population lives in cities. By 2050, this proportion is predicted to increase to 66 % (UN World Urbanization Prospects 2014).