基于遥感影像的土地利用分类研究

土地利用信息对于土地利用的动态监测、非点源污染物的迁移、生态环境保护规划和决策都有重要意义。以河南境内的丹江口水库汇水区2007年9月份的TM影像为数据源,结合遥感图像处理软件,利用监督分类方法对该区域的土地利用进行分类,最后利用ArcGIS软件对提取的土地利用信息制作出土地利用专题图。     

新疆北山地区侵入岩遥感识别方法

基于ASTER数据、Landsat 8数据及WorldView-2数据,采用波段组合法、最小噪声分离法、主成分分析法以及波段比值法,结合岩石光谱曲线,综合提取了新疆北山地区侵入岩的岩性特征。对WorldView-2进行主成分分析和最小噪声分离,并将R(PCA2)G(PCA1)B(PCA4)、R(MNF2)G(MNF1)B(PCA1)、OLI数据R(B7)G(B6)B(B5)进行假彩色合成,利用OLI数据中的B5/B2、B2/B1突出花岗岩。这些方法解决了复杂岩性的影像色调差异较小、细节不清晰等问题,降低了岩性划分及解译的难度,使各种岩性界线更加明晰。结合前人的区域地质调查成果,对研究区侵入岩岩性进行了系统解译及分析。多源遥感数据的综合利用,有助于更有效地识别复杂岩性并进行更详细的岩性分类,提高遥感岩性识别的正确率。     

基于TM影像的上海地区人口密度数字模拟

人口主要分布在居住区内, 而以人工建筑物为主要特征的居住区正是人类生命财产受地震灾害威胁最大的区域, 所以人口在居住区内的空间分布模拟对震害调查与评估具有独特的作用。 该文利用上海地区TM影像提取出居住区影像, 然后对其进行1 km的格网重采样, 并统计每个格网内的居住面积; 同时根据城镇人口距离衰减加幂指数模型生成人口分布影响参数, 最后生成人口密度图。 结果表明, 上海地区农村区域的人口密度小于5000人/ km², 城镇区域人口密度大于5000人/km², 上海中心区人口密度呈现从1023人/km²到49159人/km²的非均匀分布状态。     

基于长时间序列Landsat影像的嘎龙错冰湖变化特征分析及溃决风险评估

冰湖溃决灾害是我国青藏高原地区最严重的自然灾害之一。作为聂拉木县最大的冰川终碛湖,嘎龙错冰湖溃决风险直接威胁着下游居民的生产生活。利用1991—2011年Landsat-5/TM、2012年Landsat-7/ETM+、2013—2017年Landsat-8/OLI近27年的遥感影像、降水量和气温资料对嘎龙错冰湖变化特征进行了研究,对其溃决风险作出评估。结果表明:嘎龙错冰湖明显扩张,面积呈现显著增长趋势,由1991年的2.209 4 km^2增长到2017年的5.465 7 km^2,增幅为147.38%;同时,发现聂拉木气象站年平均气温和年降水量在波动中上升,其中气温上升较为显著,气温和降水量的增加对冰湖扩张有重要影响,特别是气温上升导致的冰川融水增多是造成冰湖面积增加的重要原因。嘎龙错冰湖在遇到冰崩或者冰湖面积持续扩张情况下,湖水会瞬间大规模外泄,形成山洪、泥石流灾害,给下游造成严重的人员伤亡和财产损失。     

Landsat 8多光谱数据辅助下的高分影像多尺度分割

为解决高分影像分割的边缘锯齿性明显等问题,本文以黑龙江省伊春市桦皮羌子林场为研究区开展了有无多光谱数据辅助的高分影像分割对比试验。首先,本文设计了多尺度分割算法的相同尺度参数下分割试验,确定了该算法分割GF-2影像时应采用的最佳同质性准则组合参数;然后,基于影像分割对象同质性局部方差变化率反映最优分割尺度的思想,利用ESP2工具找出固定尺度范围内的最优分割尺度范围;最后执行最佳同质性准则组合参数配合下的最优分割尺度范围内各个尺度下的多尺度分割,并采用矢量距离指数、紧密度指数、形状指数对2种分割试验结果进行了评价。结果表明,与GF-2影像独立分割相比,Landsat 8多光谱数据辅助下的GF-2影像分割在矢量距离指数、紧密度指数、形状指数的质量上均有提升,平均提升率分别为8.05%、28.40%、11.76%。     

Towards automated 10–30 m resolution land cover mapping in insular South-East Asia

In this study, we create and critically analyse an automated decision tree classification approach for regional level land cover mapping in insular South-East Asian conditions, using a combination of 10–30 m resolution optical and radar data. The resulting map contains 11 land cover classes and reveals a great deal of contextual information due to high spatial resolution. A limited accuracy assessment indicates 59–97% class wise accuracies. The unprecedented spatial detail of closed canopy oil palm mapping (with user’s accuracy of 90%) is seen as the most promising feature of the mapping approach. The incapability of separating primary forests from other tree cover, and the large variety of different landscapes (e.g. home gardens and tea plantations) classified as shrubland, are considered the main areas for future improvement. Overall, the study demonstrates the great potential of multi-source 10–30 m resolution high data volume land cover mapping approaches in insular South-East Asian conditions.     

Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover

Developing techniques are required to generate agricultural land cover maps to monitor agricultural fields. Landsat 8 Operational Land Imager (OLI) offers reflectance data over the visible to shortwave-infrared range. OLI offers several advantages, such as adequate spatial and spectral resolution, and 16 day repeat coverage, furthermore, spectral indices derived from Landsat 8 OLI possess great potential for evaluating the status of vegetation. Additionally, classification algorithms are essential for generating accurate maps. Recently, multi-Grained Cascade Forest, which is also called deep forest, was proposed, and it was shown to give highly competitive performance for classification. However, the ability of this algorithm to generate crop maps with satellite data had not yet been evaluated. In this study, the reflectance at 7 bands and 57 spectral indices calculated from Landsat 8 OLI data were evaluated for its potential for crop type identification.     

MODIS与Landsat 8地表温度融合拼接——以粤港澳大湾区为例

以粤港澳大湾区为例,基于时空影像融合模型(STI-FM),提出大区域多源LST(Land Surface Temperature,LST)影像融合拼接模型(Multi-source Image Fusion Stitching Model,MI-FSM),消除时间不同、天气差异的影响,生成覆盖大湾区的中高分辨率地表温度数据。首先,应用STI-FM融合MODISLST与Landsat LST,将不同时相的多幅LandsatLST合成为具有统一特定时相的LST影像。然后,对合成的LST影像进行镶嵌处理,实现粤港澳大湾区多幅Landsat 8 LST图像的拼接。为了验证STI-FM在研究区的适用性,选取研究区中心“夏-夏、冬-夏”2组Landsat 8 LST图像,将合成的Landsat LST与验证数据进行对比与评价,结果表明:STI-FM在研究区具有较强的适用性。对精度进行评价,验证模型的可靠性,结果表明:不同时相MODISLST图像间拟合程度较高,其回归分析的确定系数(M)均在0.6-0.9之间,RMSE均<1.5;最后对整体以及局部细节的目视分析表明:融合拼接的成果较为理想。     

Optical and radar remote sensing data for forest cover mapping in Peninsular Malaysia

This study aims to map forest cover in Peninsular Malaysia using satellite images as deforestation is of concern in the recent decades, and is an important environmental issue for the future too. The Carnegie Landsat Analysis System‐Lite (CLASlite) program was used in this study to detect forest cover in Peninsular Malaysia using Landsat satellite data. The results of the study show that CLASlite algorithm misclassified some oil palm, rubber and urban areas as forest vegetation. A reliable forest cover map was produced by first combining Landsat and ALOS PALSAR images to identify oil palm, rubber and urban areas, and then subsequently removing them. The HH and HV polarization data of ALOS PALSAR (threshold method) could detect oil palm plantations with 85.26 per cent of overall accuracy. For urban area detection, Enhance Build up Index (EBBI) using spectral bands from Landsat provided higher overall accuracy of 94 per cent. These methods produced a forest cover reading of 5 914 421 ha with an overall classification accuracy of 94.5 per cent. The forest cover (including rubber areas) detected in this study is 0.38 per cent higher than the percentage of 2010 forest cover detected by the Forestry Department of Peninsular Malaysia. The technique described in this paper presents an alternative and viable approach for updating forest cover maps in Malaysia.     

Satellite Remote Sensing as a Reconnaissance Tool for Assessing Nautical Chart Adequacy and Completeness

National hydrographic offices need a better means of assessing the adequacy of existing nautical charts in order to plan and prioritize future hydrographic surveys. The ability to derive bathymetry from multispectral satellite imagery is a topic that has received considerable attention in scientific literature. However, published studies have not addressed the ability of satellite-derived bathymetry to meet specific hydrographic survey requirements. Specifically, the bathymetry needs to be referenced to a chart datum and statistical uncertainty estimates of the bathymetry should be provided. Ideally, the procedure should be based on readily-available, low-cost software, tools, and data. This paper describes the development and testing of a procedure using publicly-available, multispectral satellite imagery to map and portray shallow-water bathymetry in a GIS environment for three study sites: Northeast United States, Nigeria, and Belize. Landsat imagery and published algorithms were used to derive estimates of the bathymetry in shallow waters, and uncertainty of the satellite-derived bathymetry was then assessed using a Monte Carlo method. Results indicate that the practical procedures developed in this study are suitable for use by national hydrographic offices.