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101.
利用遥感和GIS研究塔里木河下游阿拉干地区土地沙漠化 总被引:66,自引:0,他引:66
新疆塔里木河流域受人类活动的影响,特别是由于水资源利用的不合理,不同区域出现了一系列生态环境问题,下游地区普遍存在的沙漠化现象表现得尤为突出。通过应用多时相(1959年、1983年、1992年)、多波段、多平台的遥感信息,在野外调研的基础上编制阿拉干地区不同年代沙漠化类型图,并在ARC/INFO软件支持下,对图件进行编辑处理,制作沙漠化动态图;通过GIS数据库提供的资源环境定量数据,应用系统论、信息论及控制论的观点分析阿拉干地区沙漠化的演化过程,并借助于GM(1,1)模型,预测阿拉干地区土地沙漠化的发展趋势。 相似文献
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渭北高原西段新构造活动强烈,崩塌、滑坡地质灾害频繁发生。通过1:5万专题遥感解译和重点地质调查,全区遥感解译崩塌、滑坡地质灾害点共计611处,其中大型滑坡17处、中型滑坡194处、小型滑坡385处,小型崩塌15处。结合区域地理、地质环境特征,将区内大、中型滑坡地质灾害点划分为5个集中分布区、6个集中分布带。区内崩塌、滑坡地质灾害主要受地形地貌、新构造与岩性条件的控制,以地质灾害点集中分布区、带为基础,参考重点地质灾害点的影响范围及人类活动的状况,将千阳县城关镇—崔家头镇、陇县杜阳镇—千阳县草碧镇、陇县峡口河—杨河沟地区列为地质灾害防治监测重点地区。 相似文献
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居住单元是与人们生活密切相关的社区范围,其交通环境的优劣是衡量居住单元环境好坏的重要标准之一。高分辨遥感影像
的高空间分辨率特点为我们提供了研究居住单元交通环境的可能性。本文从区域交通环境、路网可达性出发,对城市居住单元交通
环境质量的遥感评价方法进行了研究,根据高分辨率遥感影像的特点构建了居住单元交通环境的多级指标评价体系,并对厦门市的
50个居住单元进行了评价分析。实验表明,利用高分辨率影像进行城市交通环境质量评价是一种成本较低,简便可行的方法。 相似文献
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Jianqiang Ren Zhongxin Chen Qingbo Zhou Huajun Tang 《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):403
The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha−1 lower than the RMSE (233.35 kg ha−1) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation. 相似文献
110.
S. Fritz M. Massart I. Savin J. Gallego F. Rembold 《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):453
Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior. 相似文献