Visualization and quantification of significant anthropogenic drivers influencing rangeland degradation trends using Landsat imagery and GIS spatial dependence models: A case study in Northeast Iran     

Abdi OMID  Shirvani ZEINAB  F. Buchroithner MANFRED 《地理学报(英文版)》2018,28(12):1933-1952

Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main steps: (1) conducting a trend analysis of rangeland degradation and anthropogenic dynamics in 1986-2000 and 2000-2015, (2) visualizing the effects of anthropogenic drivers on rangeland degradation using bivariate local spatial autocorrelation (BiLISA), and (3) quantifying spatial dependence between anthropogenic driving forces and rangeland degradation using spatial regression approaches. The results show that 0.77% and 0.56% of rangelands are degraded annually during the first and second periods. The BiLISA results indicate that dry-farming, irrigated farming and construction areas were significant drivers in both periods and grazing intensity was a significant driver in the second period. The spatial lag (SL) model (wi=0.3943, Ei=1.4139) with two drivers of dry-farming and irrigated farming in the first period and the spatial error (SE) model (wi=0.4853, Ei=1.515) with livestock density, dry-farming and irrigated farming in the second period showed robust performance in quantifying the driving forces of rangeland degradation. To conclude, the BiLISA maps and spatial models indicate a serious intensification of the anthropogenic impacts of ongoing conditions on the rangelands of northeast Iran in the future.  相似文献   

Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves     

《International Journal of Applied Earth Observation and Geoinformation》2017

Green-leaf phenology describes the development of vegetation throughout a growing season and greatly affects the interaction between climate and the biosphere. Remote sensing is a valuable tool to characterize phenology over large areas but doing at fine- to medium resolution (e.g., with Landsat data) is difficult because of low numbers of cloud-free images in a single year. One way to overcome data availability limitations is to merge multi-year imagery into one time series, but this requires accounting for phenological differences among years. Here we present a new approach that employed a time series of a MODIS vegetation index data to quantify interannual differences in phenology, and Dynamic Time Warping (DTW) to re-align multi-year Landsat images to a common phenology that eliminates year-to-year phenological differences. This allowed us to estimate annual phenology curves from Landsat between 2002 and 2012 from which we extracted key phenological dates in a Monte-Carlo simulation design, including green-up (GU), start-of-season (SoS), maturity (Mat), senescence (Sen), end-of-season (EoS) and dormancy (Dorm). We tested our approach in eight locations across the United States that represented forests of different types and without signs of recent forest disturbance. We compared Landsat-based phenological transition dates to those derived from MODIS and ground-based camera data from the PhenoCam-network. The Landsat and MODIS comparison showed strong agreement. Dates of green-up, start-of-season and maturity were highly correlated (r 0.86-0.95), as were senescence and end-of-season dates (r > 0.85) and dormancy (r > 0.75). Agreement between the Landsat and PhenoCam was generally lower, but correlation coefficients still exceeded 0.8 for all dates. In addition, because of the high data density in the new Landsat time series, the confidence intervals of the estimated keydates were substantially lower than in case of MODIS and PhenoCam. Our study thus suggests that by exploiting multi-year Landsat imagery and calibrating it with MODIS data it is possible to describe green-leaf phenology at much finer spatial resolution than previously possible, highlighting the potential for fine scale phenology maps using the rich Landsat data archive over large areas.  相似文献   

Mapping trees in high resolution imagery across large areas using locally variable thresholds guided by medium resolution tree maps     

《International Journal of Applied Earth Observation and Geoinformation》2017

Large area tree maps, important for environmental monitoring and natural resource management, are often based on medium resolution satellite imagery. These data have difficulty in detecting trees in fragmented woodlands, and have significant omission errors in modified agricultural areas. High resolution imagery can better detect these trees, however, as most high resolution imagery is not normalised it is difficult to automate a tree classification method over large areas. The method developed here used an existing medium resolution map derived from either Landsat or SPOT5 satellite imagery to guide the classification of the high resolution imagery. It selected a spatially-variable threshold on the green band, calculated based on the spatially-variable percentage of trees in the existing map of tree cover. The green band proved more consistent at classifying trees across different images than several common band combinations. The method was tested on 0.5 m resolution imagery from airborne digital sensor (ADS) imagery across New South Wales (NSW), Australia using both Landsat and SPOT5 derived tree maps to guide the threshold selection. Accuracy was assessed across 6 large image mosaics revealing a more accurate result when the more accurate tree map from SPOT5 imagery was used. The resulting maps achieved an overall accuracy with 95% confidence intervals of 93% (90–95%), while the overall accuracy of the previous SPOT5 tree map was 87% (86–89%). The method reduced omission errors by mapping more scattered trees, although it did increase commission errors caused by dark pixels from water, building shadows, topographic shadows, and some soils and crops. The method allows trees to be automatically mapped at 5 m resolution from high resolution imagery, provided a medium resolution tree map already exists.  相似文献   

基于landsat 8-OLI/TIRS和HJ-1B太湖叶绿素含量和温度反演研究   总被引：1，自引：0，他引：1  

谷俊鹏  裴亮 《测绘与空间地理信息》2017,40(5)

水体的表面温度是研究环境气候变化的一个重要参数,同时,也是研究生物物理过程的一个不可或缺的因子。卫星遥感对大面积水体表面温度的监测有着传统的测量手段无法比拟的效率。水体表面的温度变化不仅对水中生物的生存有着重要的意义,同时,水温的变化也常常会导致水中浮游生物和植物疯长,进而引起水资源的污染,对人们的生活造成严重的影响。本文利用ENVI/IDL 5.1对Landsat 8卫星的OLI/TIRS数据和HJ-1CCD多光谱数据在太湖水体区域开展了其在温度反演和叶绿素含量的监测等领域的研究。研究结果表明:1)使用HJ_1B反演的结果和Landsat 8反演的结果呈现一致性。2)通过实测的数据和反演数据建立了模型并通过同名点实测数据对实验结果进行了验证,证明了建立的模型的可靠性,找到了误差来源。最后分析了造成叶绿素含量较高的原因及反演的难点。  相似文献   

唐山市区土地利用的Landsat 8影像分层分类     

郭力娜  张梦华  张永彬  牛振国  李孟倩 《测绘科学》2017,42(10)

针对土地利用遥感分类方法多样、分类精度高低不一等问题,该文以土地利用变化明显的唐山市路南区、路北区为研究区域,并以中分遥感影像Landsat 8OLI为信息源,在对地类样本进行可分离性分析的基础上,建立研究区土地利用分层分类体系。通过监督分类实验,选择分类效果最好、分类精度最高的最大似然分类器进行地类初分;通过绘制归一化植被指数(NDVI)、归一化建筑指数(NDBI)、两指数差值(NDVI-NDBI)的曲线及地类光谱特征曲线,建立决策树分类规则,进行地类再分。该方法可以较好地完成多种土地利用二级地类的划分,有助于提高中分影像土地利用分类效率。  相似文献   

基于混合像元分解的辽河口滨海湿地分类     

李微  刘伟男  陈官滨  贾越平  刘长发 《湿地科学与管理》2017,(1):25-28

滨海湿地高精度的地物分类可以为湿地监测与保护提供数据支持和决策依据。以辽河口湿地为研究对象,以Landsat8 OLI多光谱影像为数据源,结合研究区域实际地物情况,采用像元纯度指数和均值波谱法确定端元光谱,并利用全约束最小二乘混合像元技术和决策树技术制定分类规则,最后将研究区域分为芦苇、翅碱蓬、水稻、滩涂、水体(海水、虾池水、河水等)和人工建筑(包括路面、人工设施、房屋等)六大类。结果表明:该算法分类精度高于90%,结合目视判读与野外实地调查,发现分类结果符合实际地物情况。  相似文献   

玛纳斯湖的形成和演变     

王道经 《遥感学报》1991,(4)

据根卫星影像图解译,结合其他资料分析,论述玛纳斯湖的形成和演化,它既受地质构造和古气候的制约,也受人类活动的影响。玛纳斯古湖盆形成于中更新世柯克台不爽冰期,晚更新世开始退缩,全新世进而萎缩、解体,以致衰竭、消亡。  相似文献   

唐山地震前华北地区的构造活动信息     

李建华  李望洲 《地震地质》1991,13(2):152-160,T002

本文综合分析了华北地区18幅630张不同时相、不同波段的卫星图象,发现唐山地震前,卫星图象上显示出了压扭性的构造活动信息及张性的构造活动信息。这些信息出现在唐山地震前13—16个月之间,是一种地震中期前兆信息。运用我国地面站卫星图象,监测京津及敏感点上的构造活动信息,捕捉未来大地震的中期前兆信息,对地震减灾是十分必要  相似文献   

Rapid,high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube     

Adam Lewis  Leo Lymburner  Matthew B. J. Purss  Brendan Brooke  Ben Evans  Alex Ip 《International Journal of Digital Earth》2016,9(1):106-111

The effort and cost required to convert satellite Earth Observation (EO) data into meaningful geophysical variables has prevented the systematic analysis of all available observations. To overcome these problems, we utilise an integrated High Performance Computing and Data environment to rapidly process, restructure and analyse the Australian Landsat data archive. In this approach, the EO data are assigned to a common grid framework that spans the full geospatial and temporal extent of the observations – the EO Data Cube. This approach is pixel-based and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance measurement. We demonstrate the utility of the approach with rapid time-series mapping of surface water across the entire Australian continent using 27 years of continuous, 25?m resolution observations. Our preliminary analysis of the Landsat archive shows how the EO Data Cube can effectively liberate high-resolution EO data from their complex sensor-specific data structures and revolutionise our ability to measure environmental change.  相似文献   

Mapping urban growth of the capital city of Honduras from Landsat data using the impervious surface fraction algorithm     

Nguyen-Thanh Son  Chi-Farn Chen  Cheng-Ru Chen  Shou-Hao Chiang 《国际地球制图》2016,31(3):328-341

This study developed an impervious surface fraction algorithm (ISFA) for automatic mapping of urban areas from Landsat data. We processed the data for 2001 and 2014 to trace the urbanization of Tegucigalpa, the capital city of Honduras, using a four-step procedure: (1) data pre-processing to perform image reflectance normalization, (2) quantification of impervious surface area (ISA) using ISFA, (3) accuracy assessment of mapping results and (4) change analysis of urban growth. The mapping results compared with the ground reference data confirmed the validity of ISFA for automatic delineation of ISA in the study region. The overall accuracy and Kappa coefficient achieved for 2001 were 92.8% and 0.86, while the values for 2014 were 91.8% and 0.84, respectively. The results of change detection between the classification maps indicated that ISA increased approximately 1956.7 ha from 2001 to 2014, mainly attributing to the increase of the city’s population.  相似文献