Shudong Wang

A Simple Enhanced Water Index (EWI) for Percent Surface Water Estimation Using Landsat Data

To timely obtain accurate pixel water surface proportion information through remote sensing is extremely significant to the ecological restoration in inland river basins and for the precise management of water resources. In respect to the insufficient extraction of water surface proportion information present in pixels in most of the current water information models, a simple model Enhanced Water Index (EWI) based on Modified Normalized Difference Water Index (MNDWI) has been introduced. EWI, which is oriented toward the sub-pixel level analysis of water surface proportion mapping of inland river basin, has been put forward based on the analysis of typical spectral signatures such as desert, soil, and vegetation along with MNDWI in accordance with the Landsat TM band features. The analysis is done by using methods of pixel-based EWI value with different water proportions which are analyzed through the introduction of the linear hybrid simulation between the water body and the corresponding background. Lastly, the effect of EWI model has been tested in the medium and lower reaches of Tarim. The correction coefficient for sub-pixel level water surface proportion predicted by the EWI model and the experimental data is R2 = 0.72. Results showed that the model was able to effectively extract the information about pixel water surface proportion in inland river basins. This study proves that EWI model has great potential in its application for water proportion mapping applications.

Hyperspectral Feature Extraction Based On The Reference Spectral Background Removal Method

In spectral analysis, diagnostic absorption features can indicate the existence of specific materials. Absorption parameters such as absorption center, absorption width, and absorption depth can be used in not only identification and quantitative analysis of minerals, but also in retrieval of surface physical properties. Continuum removal (CR) is commonly used to extract absorption features. However, for a band range containing more than one absorption contribution factors, the feature extracted by CR could be a result of comprehensive effect of different factors. In this paper, a new spectral feature extraction method named reference spectral background removal (RSBR) is proposed. Given the reference spectral background, RSBR can eliminate the influence of unwanted contribution factor, and extract the absorption feature of target contribution factor. Using RSBR, the basic absorption feature parameters including the absorption center, absorption width, and absorption depth are extracted. The results are compared with those obtained from the CR. It is shown that RSBR can effectively extract pure absorption features of target material, while more accurate absorption parameters can also be achieved.

Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images

Remote sensing plays a significant role for crop classification. Accurate crop classification is a common requirement to precision agriculture, including crop area estimation, crop yield estimation, precision crop management, etc. This paper developed a new crop classification method involving the construction and optimization of the vegetation feature band set (FBS) and combination of FBS and object-oriented classification (OOC) approach. In addition to the spectral and textural features of the original image, 20 spectral indices sensitive to the vegetations biological parameters are added to the FBS to distinguish specific vegetation. A spectral dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed to improve the separability between class pairs while reducing data redundancy. OOC approach is conducted on the optimized FBS based on CPS to reduce the salt-and-pepper noise. The proposed classification method was validated by two airborne hyperspectral images. The first image acquired in an agricultural area of Japan includes seven crop types, and the second image acquired in a rice breeding area consists of six varieties of rice. For the first image, the proposed method distinguished different vegetation with an overall accuracy of 97.84% and kappa coefficient of 0.96. For the second image, the proposed method distinguished the rice varieties accurately, achieving the highest overall accuracy (98.65%) and kappa coefficient (0.98). Results demonstrate that the proposed method can significantly improve crop classification accuracy and reduce edge effects, and that textural features combined with spectral indices sensitive to the chlorophyll, carotenoid, and Anthocyanin indicators contribute significantly to crop classification. Therefore, it is an effective approach for classifying crop species, monitoring invasive species, as well as precision agriculture related applications.

A Spectral Angle Distance-Weighting Reconstruction Method for Filled Pixels of the MODIS Land Surface Temperature Product

Land surface temperature (LST) is an important parameter in the physics of land surface processes, but a large number of pixels are often filled as zero due to cloud, heavy aerosols, and so on in the Moderate-resolution Imaging Spectroradiometer (MODIS) LST product. This letter presents the spectral angle distance (SAD)-weighting reconstruction (SADWER) method of reconstruction of zero-filled pixels of the MODIS LST product. It relies on the hypothesis that pixels with the same land-cover type have nearly the same LST in a localized area. SAD can measure the similarity of land-cover types of different pixels, and pixels with higher land-cover similarity can contribute more to the reconstruction using the weighting method. The result shows that the reconstruction ratio could be as high as 95% using only the SADWER method and nearly 100% after spatial filter postprocessing. The reconstruction accuracy is validated using artificially generated 20-, 50-, and 80-km-diameter concentrically filled areas in both forest and crop land-cover types. The statistical result shows that the standard deviations of the reconstruction errors are less than 2 Kelvin.

COmparison of MNDWI and DFI for water mapping in flooding season

Water delineation during flooding is an important research issue in the context of different background regions for protecting both people and agriculture. This paper compares two very important water indices for practical water mapping especially in the context of river flood management. Desert Flood Index (DFI) is a new index which has been introduced as an modification to famous MNDWI for flooding in the river basin having both desert and vegetation areas. In this study disastrous Indus River flooding of 2010 in Pakistan is considered as a case study by using MODIS and Landsat TM data. Images for both Pre-flooding and flooding are analyzed for the most significant part of river flooding. Results proved that DFI appeared to be more efficient than MNDWI in delineating water body from its background features by enhancing contrast.

Spatially explicit estimation of soil-water resources by coupling of an eco-hydrological model with remote sensing data in the Weihe River Basin of China

Abstract Soil-water resources are key components for agriculture and have great potential. Strategic and significant efforts are, therefore, required to make full use of soil-water resources, especially in dry or semidry areas. We coupled a soil-water model with remotely sensed data and associated techniques to analyze the spatial-temporal dynamics of soil-water resources in the Weihe River Basin in China. The moderate-resolution imaging spectroradiometer (MODIS), Chinese meteorological satellite precipitation estimation data (FY-2), and global land data assimilation system (GLDAS) products were used for spatial land surface characteristics interpretation and model parameters derivation. The modeling results were compared and validated using data from a nearby observation site. The average soil-water resources of the Weihe River Basin vary between 40 and 100 mm during the simulation period from January to December, with a maximum of 99 mm appearing in August and a minimum of 38 mm in December. Forest land was characterized by large soil-water resources, with an average annual rate of 1094.7 mm. Farmland and grassland exhibited low values, with average annual rates of 986.7 and 893.5 mm, respectively. The results could be taken into consideration for soil-water resources management.

A method for extracting vegetation information of Urban underlaying surface oriented to ECO-environmental quality assessment

In the traditional classification method, for the pixels, which the proportion of vegetation distribution is small, that is, the area occupied by vegetation in the pixel is less than half a pixel, they are often considered as a non-vegetation area. Such problems, in the highly developed cities, are the most common. Therefore, in order to more accurately compute the highly developed, especially hardened urban underlying surface, in this study, a new method for estimating vegetation area was proposed. The main urban area of Beijing was chosen as the study area, and the 1km grid was used as the evaluation unit. Finally, we calculated the urban development intensity index and analyzed the effectiveness of the new method of vegetation area extraction in practical application. The results show that, compared with the traditional classification methods, the index model can significantly improve the accuracy of urban vegetation information extraction and can effectively extract the information of urban underlying surface vegetation information, reflecting the intensity of urban development, and provide technical support for ecological environmental quality assessment.

The evolution of landscape ecological security in Beijing under the influence of different policies in recent decades.

Urbanization is an important force driving the development of the social, economic, and ecological environments in urban China. As the capital of China, Beijing has experienced a shift in the development process from emphasizing economic development to emphasizing ecological livability in recent decades. During this period, the Olympic Games, real estate development, and environmentally friendly construction policies were major events that affected Beijings urban ecosystem and its safety. Based on the Pressure-State-Response (P-S-R) framework model, this paper establishes an indicator system for assessing the ecological security of Beijing from 1995 to 2015. The indicators were generated through coupling of an ecological model with time-series multi-source remote sensing data such as night light images and Landsat ETM images. We assessed ecological security during different policy periods and developed an ecological security early warning system for Beijing. After the effects of the economic development policy and the bid for the Olympic Games from 1995 to 2005, the urban area of Beijing with falling ecological security continues to expand. From 2005 to 2010, due to the joint effect of 2008 Olympic venue construction, urban environmental remediation policies, and real estate policies, the overall safety level in the central city was better, but the suburbs showed the opposite trend. In 2010-2015, real estate developed explosively in Beijing, while environmentally friendly development became strongly emphasized and the economic status of the capital weakened. The ecological security of the main urban area began to improve significantly, but the outer urban area and suburban areas were greatly affected by real estate development and exhibited a clear decline in ecological security.

Estimating the area burned by agricultural fires from Landsat 8 Data using the Vegetation Difference Index and Burn Scar Index

Obtaining an accurate estimate of the area of burned crops through remote sensing provides extremely useful data for the assessment of fire-induced trace gas emissions and grain loss in agricultural areas. A new method, incorporating the Vegetation Difference Index (VDI) and Burn Scar Index (BSI) models, is proposed for the extraction of burned crops area. The VDI model can greatly reduce the confounding effect of background information pertaining to green vegetation (forests and grasslands), water bodies and buildings; subsequent use of the BSI model could improve the accuracy of burned area estimations because of the reduction in the influence of background information. The combination of VDI and BSI enables the VDI to reduce the effect of non-farmland information, which in turn improves the accuracy and speed of the BSI model. The model parameters were established, and an effects analysis was performed, using a normalized dispersion value simulation based on a comparison of different types of background information. The efficacy of the VDI and BSI models was tested for a winter wheat planting area in the Haihe River Basin in central China. In comparison with other models, it was found that this method could effectively extract burned area information.

Sensitivity analysis for Chl-a retrieval of water body using hyperspectral remote sensing data with different spectral indicators

Some spectrum indicators, such as central length, SNR, spectral resolution affect accuracy of water quality retrieval, but are ignored due to the limitations of a single sensor. Take Chlorophyll a retrieval as an example, the sensitivity analysis was conducted using single band, first-order differential and band ratio methods. The results indicated that optimal central wavelength would change because of differences of models for Chl-a retrieval using same samples; the coefficients gradually decreased with decreasing of spectral resolution, but these changes were not significant; responses of these models to different SNR were different, and first-differential methods was worse compared with that of single band and band ratio model.