新疆棉花遥感识别模型研究

依据新疆主要农作物的农时历及作物的地面光谱数据,论证了在TM遥感影像中识别棉花的最佳波段为第4波段,最佳识别月份为9月,最后通过TM遥感影像上对应的波段数据进行了验证,确定了棉花的识别模式。     

基于TM/ETM+影像的新乡县土地利用演变研究

加强新乡县土地利用演变研究,为新乡县区域土地的集约化利用和经济社会可持续发展提供科学依据。运用多时相Landsat TM/ETM+影像,综合考虑波段间相关系数和OIF指数,选择最佳波段组合进行图像解译,在此基础上对遥感影像进行分类,并利用SVM分类结果进行新乡县土地利用演变分析。     

用遥感影像提取大别山区水稻种植面积——以Landsat 8为例

本研究选取安徽省安庆市大别山为研究区,包括太湖县、岳西县、宿松县和潜山县,选取2016年和2017年的6景Landsat 8卫星遥感影像,通过遥感影像提取水稻种植面积,分析大别山区的水稻种植面积分布,并对研究区域的水稻种植面积进行动态实时监测。用遥感解译方法分别提取了研究区内的晚稻种植面积,并利用随机点验证和Kappa系数验证结果精度。结果表明:2017年和2016年水稻种植面积的提取精度分别为93.44%、93.78%,Kappa系数分别为0.86、0.83,证明水稻提取效果精确;对比研究区域各个县内的2017年和2016年晚稻种植面积,发现安庆市大别山区太湖县、潜山县、岳西县的水稻种植面积变化率均在5%以内,属于正常变化,由于2016年宿松县遭遇了水灾,部分农田被淹没,故宿松县2017年水稻种植面积相比2016年增加了13.01%;对比传统的农作物种植面积统计方法,利用遥感的方法更省人力、物力、财力,并且能精确、快速地实现对农作物的实时动态监测。     

基于Landsat TM/OLI影像的寿光市盐田信息提取与动态变化分析

寿光市是我国重要的海盐生产基地,亟需选取典型数据对其在体量及空间分布格局发生的显著变化进行分析与管理.以Landsat TM/OLI遥感影像及土地利用现状图为基础数据源,结合野外实地调查形成的专家先验知识,对研究区1985、1995、2005和2015年4期遥感影像图进行监督分类及处理,并利用土地利用转移矩阵监测寿光市...     

1989—2019年库布齐沙漠植被覆盖度的时空变化

[目的]探究库布齐沙漠近1989—2019年植被覆盖度的时空动态变化规律,为库布齐沙漠植被恢复及生态建设提供理论参考和基础数据。[方法]以1989—2019年每5 a为1期(共7期)的Landsat影像为数据源,结合归一化植被指数(NDVI)像元二分法模型,利用ENVI 5.3和ArcGIS 10.2分析库布齐沙漠1989—2019年植被覆盖度(FVC)时空动态变化特征。[结果](1)在时间变化上,近30 a间库布齐沙漠植被覆盖度整体呈增长趋势,平均植被覆盖度由0.104增长到0.243。在空间分布上,库布齐沙漠植被覆盖度呈现由西向东、向北增加的特征。(2)库布齐沙漠植被覆盖在1999—2004年和2009—2014年均呈现退化趋势,平均植被覆盖度分别减少0.053和0.054,退化面积分别为3 870.22和6 093.59 km~2,退化程度均以植被覆盖度减少10%～30%为主。[结论] 1989—2019年库布齐沙漠植被覆盖度总体有所改善,未来该区生态修复重点关注低植被覆盖区域。     

Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.     

Analysis of Land use and Land Cover Changes in the Coastal Area of Bangladesh using Landsat Imagery

Coastal land use across the globe has experienced remarkable rapid change over the recent decades because of extraordinary anthropogenic pressure and climate variability and change. Therefore, quantitative information about coastal land use change is imperative for effective management and planning resources for sustainable development. We analysed the quantitative land use and land cover changes during 1989–2000–2010 periods in three important agroecological zones of the most vulnerable coastal region of Bangladesh using Landsat images (Thematic Mapper/Enhanced Thematic Mapper Plus). In the Ganges Tidal Floodplain, the area under shrimp cultivation greatly increased at the rate of 2·05% per annum. The majority of the shrimp area gained from conversion of single cropland. In the Meghna Estuarine Floodplain, decreased mudflat and water bodies were observed, which was predominantly converted into cropland. In Chittagong Coastal Plain, salt pan–shrimp area increased with the expense of single and/or double cropland. In all the study areas, settlement area considerably increased over time. The dynamics of land use change have been attributed to low and unstable food production in the coastal region. The approach adopted in study and the results obtained from the study would likely be useful for policy making and identifying direction for future studies on the coastal land use in Bangladesh. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluating Modified Atmospheric Correction Methods for Landsat Imagery: Image‐Based and Model‐Based Calibration Methods

Abstract

To increase the accuracy of remotely sensed data for agricultural forecasting, pixel values must be corrected for atmospheric effects and converted to spectral reflectance. The objective of this research was to compare two atmospheric correction methods of Landsat imagery under a range of atmospheric conditions. Ground‐based dark‐object subtraction (GDOS) is an image‐based calibration method that used in situ ground data that the dark‐object subtraction (DOS) method did not use, whereas atmospheric calibration (AC) is a model‐based calibration method that required a standard atmospheric profile refined with the use of in situ atmospheric data. GDOS and AC methods improved the reflectance values and had relationships with measured bands, which were approximately 1 to 1 in all bands. However, the GDOS generally had lower root‐mean‐square errors (RMSE) than AC. Data from this study suggest that at the present time the GDOS method may be more accurate than the AC method.     

模拟多光谱卫星传感器数据的冬小麦白粉病遥感监测

为了解利用遥感技术快速大范围监测小麦白粉病病害情况的可行性,以Landsat5TM波段响应函数为基础,将地面实测冠层高光谱数据模拟为TM多光谱数据,从而分析卫星传感器多光谱波段对病害的响应情况,并构建多光谱指数(PMSI)估测白粉病严重度。在此基础上,采用2010年星-地配套数据对PMSI估测精度进行验证。结果表明,PMSI能够较准确地反映冬小麦白粉病发生的程度,获得较理想的病情严重度反演精度(r2=0.475,RMSE=0.129)。因此采用多光谱卫星遥感影像在小麦大面积种植区域进行病害监测具有应用潜力。     

基于Landsat 8的东莞市热岛效应研究

以2014年广东省东莞市Landsat 8卫星的热红外波段数据建立模型,反演东莞市地表温度,研究东莞市热岛效应分布情况,从优势度指数、分维数指数和形状指数来分析热力场景观格局,根据热岛效应的不同影响因素分析东莞市热岛现象的成因。结果表明：Landsat 8卫星影像反演2014年东莞市地表温度是可行的;东莞市热力景观以中热岛为主,热岛效应不明显,分布呈西北偏高,中部地区和东南区域偏低;热力景观斑块较复杂、热力场呈条带状零星分布;受绿化、水体影响区域的热岛强度较小,而受人为热源、道路、城市下垫面和建筑等因素影响的区域,热岛强度较大。