Shanlong Lu

Water body mapping method with HJ-1A/B satellite imagery

Abstract This paper proposes an integrated water body mapping method with HJ-1A/B satellite imagery, the CCD (charge coupled device) data of the Chinese environmental satellites that were launched on September 6th, 2008. It combines the difference between NDVI and NDWI (NDVI–NDWI) with SLOPE and near-infrared (NIR) band. The NDVI–NDWI index is used to enhance the contrast between water bodies and the surrounding surface features; the topographic SLOPE is used to eliminate the mountain shadow; and the NIR band is used to reduce the effects of artificial construction land. The objectives are evaluating the potential of the HJ-1A/B imagery on water body monitoring, and proposing ideally mapping method. The test study results indicated that the NDVI–NDWI index is superior to the single index of NDVI and NDWI to enhance the contrast between water bodies and the rest of the features. On the basis of the accurately mapped water bodies in the HJ-1A/B CCD images of the study area, we conclude that the HJ-1A/B multi-spectral satellite images is an ideal data source for high spatial and temporal resolution water bodies monitoring. And the integrated water body mapping method is suitable for the applications of HJ-1A/B multi-spectral satellite images in this field.

Lake water volume calculation with time series remote-sensing images

The volume of water in lakes is commonly estimated by combining data of water level variations with accurate bathymetry and shore topographic maps. However, bathymetry and shore topography data are often difficult to acquire, due to high costs for labour and equipment. This article presents an innovative method for calculating lake water volumes by using long-term time series remote-sensing data. Multi-spectral satellite remote-sensing images were used to map a lake’s water surface area. The lake water surface boundaries for each year were combined with field-observed water levels to generate a description of the underwater terrain. The lake water volume was then calculated from the water surface area and the underwater terrain data using a constructed TIN (triangulated irregular network) volume model. Lake Baiyangdian, the largest shallow freshwater lake in the North China Plain, was chosen as the case study area. For the last 40 years the water levels of Lake Baiyangdian have reflected multiple dry and wet periods, which provide a good data series for the study of the proposed method. Archived Landsat MSS/TM/ETM+ and HJ-1A/B images from 1973 to 2011 were used as the basic data. The NDWI (normalized difference water index) and MNDWI (modified NDWI) were used to map the water surface of the lake, and the lake water volumes were calculated with the 3D Analyst tool of ArcMap 9.3. The results show that the estimated water volumes from remote-sensing images were very consistent with the volumes derived from the fitted equation of the lake storage capacity curve based on observed data.

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.

Lake water surface mapping in the Tibetan Plateau using the MODIS MOD09Q1 product

ABSTRACT The Tibetan Plateau (TP) has the largest number of inland lakes with the highest elevation on the planet. Mapping the distribution of lake water in space and time is crucial for scientific research of interactions among the regional cryosphere, hydrosphere, and atmosphere. In this study, a lake water surface mapping algorithm is developed for Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09Q1 surface reflectance images, which is used to produce the 8-day lake water surface data set (lake water surface area larger than 1 km2) of theTP (Qinghai–Tibet Plateau) for the period of 2000–2012. The accuracy analysis indicate that compared with water surface data of the 134 sample lakes extracted from the 30 m Landsat Thematic Mapper (TM) images, the average overall accuracy of the results is 91.81% with average commission and omission error of 3.26% and 5.38%; the results also show strong linear (the coefficient of determination R2 is 0.9991) correlation with the global MODIS water mask data set with overall accuracy of 86.30%; and the lake area difference between the Second National Lake Survey and this study is only 4.74%, respectively. This study provides reliable data set for the lake change research of theTP in the recent decade.

Multi-scale object-based measurement of arid plant community structure

ABSTRACT The measurement of plant community structure provides an extensive understanding of its function, succession and ecological process. The detection of plant community boundary is rather a challenge despite in situ work. Recent advances in object-based image analysis (OBIA) and machine learning algorithms offer new opportunities to address this challenge. This study presents a multi-scale segmentation approach to accurately identify the boundaries of each vegetation and plant community for mapping plant community structure. Initially, a very high resolution (VHR) Worldview-2 image of a desert area is hierarchically segmented from scale parameter 2 to 500. Afterward, the peak values of the standard deviation of brightness and normalized difference vegetation index (NDVI) across the segmentation scales are detected to determine the optimal segmentation scales of homogeneous single plant and plant community boundaries. A multi-scale classification of vegetation characterization with features of multiple bands, NDVI, grey-level co-occurrence matrix (GLCM) entropy and shape index is performed to identify dryland vegetation types. Finally, the four vegetation structural features on the type, diversity, object size and shape are calculated within the plant community boundaries and composed to plant community structure categories. Comparing the results with the object fitting index (FI) of the reference data, the validation indicates that the optimal segmentations of tree, shrub and plant communities are consistent with the identified peak values.