Li X.,CAS Institute of Remote Sensing |
Zhang X.,Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions |
Zhang L.,CAS Institute of Remote Sensing |
Wu B.,CAS Institute of Remote Sensing
Journal of Soil and Water Conservation | Year: 2014
Information about vegetation cover based on remote sensing data is widely used for soil erosion risk mapping, but no clear guidelines exist to select the most appropriate temporal satellite data. Soil erosion risk varies with rainfall and vegetation cover dynamics during a year, and the month with the highest erosion risk varies too. This paper proposes the use of a Rainfall and Vegetation Coupling Index (RVCI) to quickly identify the month with the highest soil erosion risk by using easily available rainfall and vegetation cover data. The appropriateness of the RVCI is validated against field measured sediment yield data in the upper watershed of Miyun Reservoir (UWMR) for different years. Based on the above, the soil erosion risk in the UWMR for 2005 was mapped and analyzed. Our results show that RVCI has the ability to cut through the rainfall-Normalized Difference Vegetation Index complexity and provides a more intuitive means for inferring the relative soil erosion risk. The soil erosion risk in the UWMR for 2005 shows that severe soil loss or worse tends to occur on dry lands or grasslands on steep slopes, which will require attention for soil and water conservation in the future. Copyright © 2014 Soil and Water Conservation Society. All rights reserved.
Zhang X.W.,CAS Institute of Remote Sensing |
Zhang X.W.,Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions |
Wu B.F.,CAS Institute of Remote Sensing
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2015
Fractional vegetation cover (FVC) is an important index of land surface vegetation status. It is also an indicator of ecological environment changes and an important spatial parameter for various ecological modeling. The traditional methods of FVC measurement are time-consuming and labor-intensive, and thus difficult to obtain large-scale time series FVC data. Remote sensing technique is an effective approach to estimate FVC, but it is very difficult to acquire high and moderate resolution remote sensing images covering the entire study area during the same period because of the cloud cover and other weather conditions. Consequently, the FVC data derived from multi-temporal images inevitably lead to uncertain research results. To address the problem, this paper proposes a novel method to eliminate the impact of acquisition time differences on FVC from high and moderate resolution remote sensing images. For FVC data derived from images with different resolutions and acquisition dates, this proposed temporal transformation method is used to estimate high resolution FVC combined with a low-resolution time series FVC. Firstly, low and high resolution FVC data can be calculated from time series MODIS images and acquired SPOT images respectively using the dimidiate pixel model. Secondly, the vegetation cover is divided into different vegetation types based on the land use map derived from these SPOT images. And for each MODIS pixel, the area percentages of various types of vegetation cover are calculated based on the spatial overlay of MODIS image and land use data. As a result, the area percentage data represent that the area ratio of the different vegetation types within each MODIS pixel. Thirdly, the pure pixels of various types of vegetation cover can be extracted based on the area percentage data where the ratio is equal to 1, and the FVC time series curve of each type of vegetation cover can be generated based on these pure pixels and time series MODIS FVC data. Finally, the sub-pixel FVC variation of each type of vegetation cover can be extracted from MODIS pixels based on the pixel unmixing technique, and then apply them to the same location of SPOT FVC. Thus, the SPOT FVC can be transformed from its acquisition date to the specific date, which satisfies the need of our research. The feasibility of this temporal transformation method is examined in the upstream of Miyun Reservoir. The FVC data derived from 10 SPOT images are transformed to the same date of early July. The case study results show that: (1)The visual effects of the transformed FVC are significantly improved and consistent with the spatial patterns of vegetation cover; (2)The changes of FVC statistics information before and after the transformation are also in line with the laws of vegetation growth; (3) The linear regression of the FCV data on field measurement samples shows strong positive correlations between them, and the R2 is about 0.8 for each vegetation cover indicating the transformation results is close to the field measured values. The transformation results with higher precision can promote the accuracy of related researches. This method has also a certain reference value for the transformation of other parameters. © 2015, Ecological Society of China. All rights reserved.
Cui C.,Henan University |
Han Z.,Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions |
Song W.,Henan University |
Liu G.-J.,University of Melbourne
International Conference on Geoinformatics | Year: 2016
Regional comprehensive accessibility is the proximity of all locations to other specified locations in a region. Studies of regional accessibility have been primarily performed on a city or county scale. As the nerve ending of a road network, rural roads are distributed throughout a region. However, rural roads were rarely considered in previous studies. In this study, we focus on the scale of townships and include rural roads in the road network to measure accessibility. Using the GIS grid analysis method, we assessed the comprehensive accessibilities of Kaifeng City in China on a township scale. The regional accessibility analysis method on a city or county scale cannot be completely applied to township scale studies, which is reflected in the buffering area processing of closed roads. The accessibility of the townships in Kaifeng are characterized as irregular distributions in circular layers and primarily influenced by road network. Meanwhile, the spatial distribution of the medium and high accessibility values exhibits clustering, the low values are dispersed. © 2015 IEEE.
Zhang X.,Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions |
Zhang X.,Henan University |
Qin Y.,Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions |
Qin Y.,Henan University |
And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013
Research on winter wheat has an important significance for timely and accurately obtaining the crop acreage and their spatial distribution at regional and national scales. In traditional methods combining medium-resolution and low-resolution remote sensing data, only the area percentage of crops in a low-resolution pixel is extracted, thus the crop area is obtained. For this limitation, this paper proposes a new crop identification method. The land cover of the study area is summarized in six categories (farmland, forestland, shrub land, grassland, waters, and other). Each type of land cover's purity is calculated in the corresponding MODIS pixel. First, NDVI time series curves are extracted for various types of land cover based on MODIS time advantage, analyzed for identifying characteristics of winter wheat on the seasonal rhythm, and used to build the identification model. Then, MODIS pixels are classified based on the purity of farmland, including farmland pure pixel, other crop pure pixel, mixed pixel from farmland and other land cover, mixed pixel from winter wheat and other crops, and other pixel. The MODIS pixels involving winter wheat include three types, i.e. the farmland pure pixel, mixed pixel from farmland and other land cover, mixed pixel from winter wheat, and/or other crops. For the farmland pure pixels, the winter wheat is identified according to seasonal characteristics of winter wheat. For the mixed pixel from farmland and other land cover, their sub-pixel NDVI time series are extracted based on the pixel un-mixing method, in order to identify whether the sub-pixel belongs to winter wheat. Further, the identification results are repositioned to the medium-resolution scales according to the spatial relationship. The mixed pixels area from winter wheat and other crops are identified based on spectral differences of Landsat TM remote sensing images. Finally, these three types of identified results can be integrated into the medium-resolution scales. In this paper, the winter wheat identified method is applied to the dominating agricultural area of the Yiluo basin. A total of 11 016 MODIS farmland pure pixels with 250 m spatial resolution, corresponding 1 101 600 farmland pixel with 25 m spatial resolution, were identified as winter wheat; 18 630 MODIS mixed pixels integrating farmland and other land cover, corresponding 882 192 farmland pixels, were identified as winter wheat; 10 275 MODIS mixed pixels integrating winter wheat and other crops, corresponding 595 296 farmland pixels, were identified as winter wheat. Winter wheat acreage of our study area is 161 193.00 hm2. By random sampling, the identified results of winter wheat show an accuracy of 96.3%. The error rate is 2.79% compared with statistical data of Yearbook. The superiority of this identified method, compared with the other methods combining medium-resolution and low-resolution remote sensing data, is that not only was the acreage of crops accurately extracted, but also its spatial distribution was determined at the medium-resolution scales. This paper provides a new way to solve problems for extraction of crop cultivation area and spatial distribution information. It can be applied not only to the identification of winter wheat, but also has important reference value for the identification of other types of crops.