Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru
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Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.Keywords:
Deforestation
Moderate-resolution imaging spectroradiometer
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This paper presents a novel multitemporal spectral unmixing (MSU) approach to address the challenging multiple-change detection problem in bitemporal hyperspectral (HS) images. Differently from the state-of-the-art methods that are mainly designed at a pixel level, the proposed technique investigates the spectral-temporal variations at a subpixel level. The considered change detection (CD) problem is analyzed in a multitemporal domain, where a bitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted according to an automatic and unsupervised technique. Then, a change analysis strategy is designed to distinguish the change and no-change MT-EMs. An endmember-grouping scheme is applied to the changed MT-EMs to detect the unique change classes. Finally, the considered multiple-change detection problem is solved by analyzing the abundances of the change and no-change classes and their contribution to each pixel. The proposed approach has been validated on both simulated and real multitemporal HS data sets presenting multiple changes. Experimental results confirmed the effectiveness of the proposed method.
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This study proposes an approach to unsupervised change detection in which two different change maps are fused using different trade-off parameters of an active contour model. First, the change vector analysis method is conducted to produce a difference image from multitemporal and multispectral remotely sensed images. Second, two change maps are obtained based on the difference image using an active contour model using two different values of the trade-off parameter. Finally, an advantage fusion strategy is proposed to yield a final change map by fusing the two change maps, thereby reducing false alarms and preserving the accurate outlines of the changed regions. Two experiments are conducted with Landsat-7 Enhanced Thematic Mapper Plus and Landsat-5 Thematic Mapper data sets to evaluate the performance of the proposed method. Results confirm the effectiveness of the proposed approach vis-à-vis some of the state-of-the-art methods. This work contributes to the reduction of the effect of the trade-off parameter on the accuracy of the change map.
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This study investigated percent impervious surface area (PISA) extracted by a four-endmember normalized spectral mixture analysis (NSMA) method and evaluated the reliability of PISA as an indicator of land surface temperature (LST). Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images for Yantai city, eastern China obtained from USGS were used as the main data source. The results demonstrated that four-endmember NSMA method performed better than the typical three-endmember one, and there was a strong linear relationship between LST and PISA for the two images, which suggest percent impervious surface area provides an alternative parameter for analyzing LST quantitatively in urban areas.
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We examine the utility of linear mixture modelling in the sub-pixel analysis of Landsat Enhanced Thematic Mapper (ETM) imagery to estimate the three key land cover components in an urban/suburban setting: impervious surface, managed/unmanaged lawn and tree cover. The relative effectiveness of two different endmember sets was also compared. The interior endmember set consisted of the median pixel value of the training pixels of each land cover and the exterior endmember set was the extreme pixel value. As a means of accuracy assessment, the resulting land cover estimates were compared with independent estimates obtained from the visual interpretation of digital orthophotography and classified IKONOS imagery. Impervious surface estimates from the Landsat ETM showed a high degree of similarity (RMS error (RMSE) within approximately ±10 to 15%) to that obtained using high spatial resolution digital orthophotography and IKONOS imagery. The partition of the vegetation component into tree vs grass cover was more problematic due to the greater spectral similarity between these land cover types with RMSE of approximately ±12 to 22%. The interior endmember set appeared to provide better differentiation between grass and urban tree cover than the exterior endmember set. The ability to separate the grass vs tree components in urban vegetation is of major importance to the study of the urban/suburban ecosystems as well as watershed assessment.
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This report provides results of an independent assessment of the geopositional accuracy of the Earth Satellite (EarthSat) Corporation's GeoCover, Orthorectified Landsat Thematic Mapper (TM) imagery over Northeast Asia. This imagery was purchased through NASA's Earth Science Enterprise (ESE) Scientific Data Purchase (SDP) program.
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The Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data afford the remote sensing community a unique opportunity to investigate the frequency and distribution of fires. Previous research that validated the MODIS burned area product (MCD45A1) in South Africa was only limited to two Landsat 7 Enhanced Thematic Mapper plus (ETM+) scenes in savanna vegetation, which is not adequate for robust assessment of fire distribution across diverse environments. In this study, validation of the MCD45A1 and the Backup MODIS burned area product (hereafter BMBAP) was extended over different South African vegetation types by quantifying their burned area detection and estimation accuracy using Landsat 5 Thematic Mapper (TM) imagery. Results from the four validation sites reveal that there are subtle differences in the accuracy of the two products. These differences could be influenced for example by, vegetation type, spectral characteristics, and size distribution of the burned areas. These results have significant implications for fire monitoring in Southern Africa.
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Moderate-resolution imaging spectroradiometer
Biome
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This letter examines the effect of the prior elimination of strong changes on the results of change detection in bitemporal multispectral images using the previously published iteratively reweighted multivariate alteration detection (IR-MAD) method. An initial change mask is calculated by identifying strong changes between two images. By using the mask and hence eliminating the strong changes from the analysis, the IR-MAD method is able to identify a better no-change background. This effect is demonstrated on a multitemporal Landsat Enhanced Thematic Mapper Plus data set from an agricultural region in Germany with substantial improvement in the results even for the scenes which have a large number of changes.
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In this study we apply the methodology of post-classification change detection to map and monitor land cover changes and urban expansion in wider Ljubljana region. We used multitemporal Landsat Thematic Mapper (TM)/ Enhanced Thematic Mapper Plus (ETM+) images from 1992, 1999 and 2005 to produce three land cover/land use maps. Post classification comparison of these maps was used to obtain »from-to« statistics and change detection maps.
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