Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data
Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data
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DOI：
10.3390/rs3040816
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发表时间：
2011-04-01
期刊：
影响因子：
5
通讯作者：
Rala, Arnel
中科院分区：
文献类型：
--
作者：
Gumma, Murali Krishna;Thenkabail, Prasad S.;Rala, Arnel
灌溉区地图对加纳的农业发展至关重要。这项研究的目的是绘制灌溉农业区的地图，并解释使用遥感的方法和协议。利用Landsat增强型专题制图器（ETM+）数据和时间序列中分辨率成像光谱仪（MODIS）数据绘制了加纳灌溉农业区以及其他土地利用/土地覆盖（LULC）类别的地图。利用在LULC类中获得的归一化植被指数（NDVI）格局的时间变化特征来识别灌区和非灌区。首先，长期灌溉作物的NDVI格局的时间变化比短期旱作作物更一致，因为灌区的供水更有保障。其次，灌区的地表水可用性取决于浅挖井（河岸）和挖坑（河底），它们影响作物播种和生长阶段的时间，这反过来又反映在季节性NDVI模式中。采用Landsat 30 m一次性数据与MODIS 250 m时间序列数据融合的决策树方法对类别进行分类、分组和标记。最后，使用真实数据和国家统计数据对类进行测试和验证。灌溉类的模糊分类精度评价在67 ~ 93%之间。从遥感得到的灌溉面积（32,421公顷）比加纳灌溉发展局（GIDA）报告的灌溉面积高20-57%。这是由于以下因素所涉及的不确定性：(a) GIDA统计中缺乏浅层灌区统计；(b) GIDA统计中灌区的使用不明确、发展不足和发展潜力；(c)遥感方法中的遗漏和委托错误；(d)在使用GIDA和遥感确定灌区统计时涉及广泛不同的数据类型、方法和方法的比较。利用高（30米）至非常高（< 5米）分辨率遥感数据与MODIS等多时相数据融合，开展广泛的实地活动，帮助更好地对灌溉区进行分类和验证，是未来的发展方向。在计算由挖井和防空洞形成的小而连续的灌溉区域时尤其如此。
Maps of irrigated areas are essential for Ghana's agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20-57% higher than irrigated areas reported by Ghana's Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m) to very high (< 5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.