Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors

Retrospective identification of fire severity can improve our understanding of fire behaviour and ecological responses. However, burnt area records for many ecosystems are non-existent or incomplete, and those that are documented rarely include fire severity data. Retrospective analysis using satellite remote sensing data captured over extended periods can provide better estimates of fire history. This study aimed to assess the relationship between the Landsat differenced normalised burn ratio (dNBR) and field measured geometrically structured composite burn index (GeoCBI) for retrospective analysis of fire severity over a 23 year period in sclerophyll woodland and heath ecosystems. Further, we assessed for reduced dNBR fire severity classification accuracies associated with vegetation regrowth at increasing time between ignition and image capture. This was achieved by assessing four Landsat images captured at increasing time since ignition of the most recent burnt area. We found significant linear GeoCBI–dNBR relationships (R2 = 0.81 and 0.71) for data collected across ecosystems and for Eucalyptus racemosa ecosystems, respectively. Non-significant and weak linear relationships were observed for heath and Melaleuca quinquenervia ecosystems, suggesting that GeoCBI–dNBR was not appropriate for fire severity classification in specific ecosystems. Therefore, retrospective fire severity was classified across ecosystems. Landsat images captured within ~ 30 days after fire events were minimally affected by post burn vegetation regrowth.

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.

BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data

A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGIS (TM) libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool's structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset.

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.

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.