Change detection and controlling forest information using multi-temporal Landsat TM imagery.

A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each others, and several multitemporal images covering different geographic locations. The radiometricly calibrated difference images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field. The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

基于Landsat TM的西安市城市热岛效应研究

IEECAS SKLLQG

Uncertainties in estimates of cropland area in China: a comparison between an AVHRR-derived dataset and a Landsat TM-derived dataset

The large uncertainties in estimates of cropland area in China may have significant implications for major cross-cutting themes of global environmental change-food production and trade, water resources, and the carbon and nitrogen cycles. Many earlier studies have indicated significant under-reporting of cropland area in China from official agricultural census statistics datasets. Space-borne remote sensing analyses provide an alternative and independent approach for estimating cropland area in China. In this study, we report estimates of cropland area from the National Land Cover Dataset (NLCD-96) at the 1:100,000 scale, which was generated by a multi-year National Land Cover Project in China through visual interpretation and digitization of Landsat TM images acquired mostly in 1995 and 1996. We compared the NLCD-96 dataset to another land cover dataset at I-km spatial resolution (the IGBP DIScover dataset version 2.0), which was generated from monthly Advanced Very High Resolution Radiometer (AVHRR)-derived Normalized Difference Vegetation Index (NDVI) from April, 1992 to March, 1993. The data comparison highlighted the limitation and uncertainty of cropland area estimates from the DIScover dataset. (C) 2003 Elsevier Science B.V. All rights reserved.

Cyanobacteria detection in Guarapiranga Reservoir (São Paulo State, Brazil) using Landsat TM and ETM+ images

Algae bloom is one of the major consequences of the eutrophication of aquatic systems, including algae capable of producing toxic substances. Among these are several species of cyanobacteria, also known as blue-green algae, that have the capacity to adapt themselves to changes in the water column. Thus, the horizontal distribution of cyanobacteria harmful algae blooms (CHABs) is essential, not only to the environment, but also for public health. The use of remote sensing techniques for mapping CHABs has been explored by means of bio-optical modeling of phycocyanin (PC), a unique inland waters cyanobacteria pigment. However, due to the small number of sensors with a spectral band of the PC absorption feature, it is difficult to develop semi-analytical models. This study evaluated the use of an empirical model to identify CHABs using TM and ETM+ sensors aboard Landsat 5 and 7 satellites. Five images were acquired for applying the model. Besides the images, data was also collected in the Guarapiranga Reservoir, in São Paulo Metropolitan Region, regarding the cyanobacteria cell count (cells/mL), which was used as an indicator of CHABs biomass. When model values were analyzed excluding calibration factors for temperate lakes, they showed a medium correlation (R²=0.81, p=0.036), while when the factors were included the model showed a high correlation (R²=0.96, p=0.003) to the cyanobacteria cell count. The empirical model analyzed proved useful as an important tool for policy makers, since it provided information regarding the horizontal distribution of CHABs which could not be acquired from traditional monitoring techniques.

A hybrid methodology for detecting cartographically significant features using Landsat TM imagery /

"ETL-0589."

Mapeamento e descrição do padráo de uso e cobertura da terra em municípios do sudoeste goiano a partir de imagens orbitais TM/Landsat-5.

A partir dos anos 1970, a ocupação pelo homem do espaço do centro-oeste brasileiro apresentou um elevado crescimento devido a políticas de expansão agrícola. Este fato ocorreu por meio do alto grau de mecanização agrícola e aplicação de fertilizantes, visando elevados níveis de produção em diversas localidades, como o sudoeste do Estado de Goiás. Tal predicado da alta produtividade mantém-se até os dias atuais, indicando a grande intensidade da dinâmica de uso e cobertura das terras nesta região. Desta forma, tornase necessário o conhecimento da dinâmica e distribuição espacial dos padrões de uso e cobertura da terra, podendo fornecer subsídios a ações de planejamento agrícola sobre o espaço em alguns municípios do sudoeste goiano. Para isto, imagens orbitais do satélite Landsat TM-5 foram adquiridas em diferentes períodos do ciclo agrícola ao longo de 2007. Informações complementares acerca do uso regional foram utilizadas para apoiar a interpretação e classificação, principalmente a partir dos dados obtidos em campo. Os mapas de uso e cobertura da terra para os municípios de Rio Verde, Acreúna, Santo Antônio da Barra, Santa Helena de Goiás, Montividiu e Paraúna foram obtidos utilizando ferramentas do programa Spring 4.3.3 como a segmentação de imagens, bem como o classificador semi-automático Bhattacharya Distance, sendo estabelecidas dez classes temáticas, com base na legenda proposta pelo IBGE e Corine. A análise multitemporal, assim como a segmentação mostraram-se eficientes na distinção das classes de uso e cobertura da terra da região. A classe de uso destinada ao plantio da soja apresentou o maior percentual da área, mudando para culturas safrinha, solo exposto ou pousio no inverno. Outras classes também merecem destaque como a Pastagem e a Cana-de-açúcar, que apresentaram distribuição espacial bastante concentrada. Este mapeamento fornece subsídios ao planejamento do uso e ocupação das terras na região, considerando os aspectos ambientais e sociais, assegurando maior produtividade agrícola, visando um manejo sustentável das terras e a qualidade de vida ao homem do campo.

Detecção de áreas degradadas na sub-bacia hidrográfica do rio taperoá/pb, utilizando parâmetros físicos dos sensores modis/terra e tm/landsat

This study includes the results of the analysis of areas susceptible to degradation by remote sensing in semi-arid region, which is a matter of concern and affects the whole population and the catalyst of this process occurs by the deforestation of the savanna and improper practices by the use of soil. The objective of this research is to use biophysical parameters of the MODIS / Terra and images TM/Landsat-5 to determine areas susceptible to degradation in semi-arid Paraiba. The study area is located in the central interior of Paraíba, in the sub-basin of the River Taperoá, with average annual rainfall below 400 mm and average annual temperature of 28 ° C. To draw up the map of vegetation were used TM/Landsat-5 images, specifically, the composition 5R4G3B colored, commonly used for mapping land use. This map was produced by unsupervised classification by maximum likelihood. The legend corresponds to the following targets: savanna vegetation sparse and dense, riparian vegetation and exposed soil. The biophysical parameters used in the MODIS were emissivity, albedo and vegetation index for NDVI (NDVI). The GIS computer programs used were Modis Reprojections Tools and System Information Processing Georeferenced (SPRING), which was set up and worked the bank of information from sensors MODIS and TM and ArcGIS software for making maps more customizable. Initially, we evaluated the behavior of the vegetation emissivity by adapting equation Bastiaanssen on NDVI for spatialize emissivity and observe changes during the year 2006. The albedo was used to view your percentage of increase in the periods December 2003 and 2004. The image sensor of Landsat TM were used for the month of December 2005, according to the availability of images and in periods of low emissivity. For these applications were made in language programs for GIS Algebraic Space (LEGAL), which is a routine programming SPRING, which allows you to perform various types of algebras of spatial data and maps. For the detection of areas susceptible to environmental degradation took into account the behavior of the emissivity of the savanna that showed seasonal coinciding with the rainy season, reaching a maximum emissivity in the months April to July and in the remaining months of a low emissivity . With the images of the albedo of December 2003 and 2004, it was verified the percentage increase, which allowed the generation of two distinct classes: areas with increased variation percentage of 1 to 11.6% and the percentage change in areas with less than 1 % albedo. It was then possible to generate the map of susceptibility to environmental degradation, with the intersection of the class of exposed soil with varying percentage of the albedo, resulting in classes susceptibility to environmental degradation

Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

基于TM影像的黑土区表层土壤有机碳空间格局研究

土壤有机碳是陆地生态系统的一个动态组成部分，其储量、分布及其转化在陆地碳循环中起着重要的作用，是土壤肥力的核心指标之一。土壤有机碳含量的多少及其空间分布特性受气候、母质、地形等结构性因子以及施肥、耕作等随机性因子的影响。作为陆地生态系统中的一个区域化随机变量，土壤有机碳在系统内部垂直和水平方向上发生变化的同时，参与大气圈和生物圈这两个碳库之间的循环。为了揭示土壤有机碳的空间分布特性，传统统计学、地统计学、遥感与地理信息系统等方法被相继引入且逐渐走向成熟，其中，利用遥感与地理信息系统相结合的方法来反演土壤有机碳库储量及其空间分布格局为越来越多的专家学者所重视。本文在对不同方法基本理论进行简单阐述的基础上，对利用Landsat TM影像分析表层土壤有机碳格局的可行性进行了分析，并就遥感技术在反演表层土壤有机碳的空间分布格局中的应用前景进行了展望。 以黑龙江省部分黑土地区为研究对象，在不同时空尺度下分析了表层土壤有机碳与Landsat TM影像的TM1－TM5、TM7六个波段以及由其计算出的NDVI、NDTI、NDI5、NDI7、NDSVI、SAVI、RDVI和MSAVI 8个遥感指数之间的相关性。结果表明当Landsat TM影像的成像时间为2002年9月份时：(1)小尺度下表层土壤有机碳与TM1极显著相关(r=0.32, p<0.01)，与TM2、TM3和NDSVI显著相关(p<0.05)，相关系数分别为r=0.27，r=-0.29，r=0.26。(2)大尺度下，表层土壤有机碳与TM1、TM2、TM5和NDI5存在极显著的正相关(p<0.01)，相关系数分别为r=0.30，r=0.34，r=0.35，r=0.32；而与NDI7之间存在显著相关性(r=0.27, p=0.02)。(3)当空间尺度一定时，在不同的时间尺度下与表层土壤有机碳具有显著相关性的遥感指标不同。(4)在大尺度下利用遥感技术测定法得到的回归模型对表层土壤有机碳空间分布格局具有较好的预测效果(R2=0.7097, p<0.05)；(5)在大尺度下海拔高于200 m的地区表层土壤有机碳浓度显著高于海拔低于200 m的地区(p<0.05)。 通过分别利用地统计方法和遥感技术测定法分析海伦市表层土壤有机碳的空间分布格局，以步长为6170m时球状模型对表层土壤有机碳进行了拟合并利用Kriging插值方法得到了海伦市表层土壤有机碳分布格局；将这一结果与我们利用Landsat TM影像通过遥感技术测定方法得到表层土壤有机碳空间格局进行比较发现：在精度一致的前提下，遥感技术测定法在所需样本数量约为地统计方法的一半，同时在所消耗的人力、物力以及在时间上的循环周期等方面，遥感技术测定方法与地统计方法相比也有着明显的优势。