A comparison of support vector machines and manual change detection for land-cover map updating in Massachusetts, USA

The remote sensing community has recently adopted land-cover map updating methodologies using spectral image differencing, change masking and concatenation procedures to monitor land change accurately and consistently. Unfortunately, map updating requires costly, time-consuming manual image interpretation to achieve accurate spectral threshold placement for land-change masking. The purpose of this study is to minimize time and costs associated with manual image interpretation of change thresholds by developing a new, semi-automated method using support vector machines (SVM). The results of this study show that the SVM change detection method produced more accurate results and required considerably less time and user effort than the manual change detection method, and is thus an effective alternative to manual methods of land-cover map updating. © 2013 Taylor & Francis Group, LLC

Análise espectral e temporal da cultura do café em imagens Landsat Spectral and temporal behavior analysis of coffee crop in Landsat images

A definição da resposta espectral da cultura do café é uma das etapas na identificação de lavouras cafeeiras em imagens de satélites de sensoriamento remoto, para fins de mapeamento e estimativa de área plantada. O objetivo deste trabalho foi avaliar o potencial das imagens adquiridas pelos satélites da série Landsat, no mapeamento da cultura do café para a previsão de safras. Foi feita uma análise temporal do comportamento espectral de lavouras de café-formação e café-produção por meio de imagens livres de nuvens adquiridas nos anos de 1999 e 2001. Também foi analisado o comportamento espectral das classes pastagem e mata, que compõem os alvos de maior ocupação na área de estudo. As imagens do período seco foram mais eficientes no mapeamento de lavouras de café-formação e café-produção. As imagens da banda 4 dos dois sensores apresentaram melhor diferenciação espectral entre café e os demais alvos da cena. A reflectância do café-produção apresentou grande variabilidade entre lavouras, que pode ser atribuída à idade, espaçamento de plantas, cultivar, indicando a necessidade de trabalho em campo para a correta identificação das lavouras de café nas imagens Landsat.<br>The definition of the spectral response of coffee crop is one of the steps to identify coffee fields in remote sensing images in order to map and estimate planted area. The objective of this work was to analyze the potential of the images acquired by the Landsat series satellites, for coffee crop mapping and forecast. A temporal analysis of the spectral behavior of coffee crop fields under development and under active production was performed through cloud free images acquired in the years of 1999 and 2001. The spectral behavior of pasture and forest was also analyzed due to their relevance in the study area. The results showed that images acquired during the dry season were more efficient to map coffee crop at early development and under production. Band 4 (near infrared) of both sensors (TM and ETM+) presented best performance for spectral differentiation between coffee crop and other scene targets. The analysis of the reflectance values for active producing coffee crop showed a high spectral variability which may be attributed to age, plants spacing, cultivar, indicating a need for field work for the identification of coffee crop in Landsat scenes

Mapping Selective Logging in Mixed Deciduous Forest

Abstract This study assesses the performance of five Machine Learning Algorithms (MLAs) Sustainable Development of Forests). Monitoring programs cover large spatial extents, and require sizable quantities of remotely sensed data, thus presenting a unique set of data processing and image interpretation challenges. Aside from the large volume of data to be processed, most complications are related to the paucity of ground reference data caused by cost and time constraint