A Spectral Mixture Analysis and Landscape Metrics Based Framework for Monitoring Spatiotemporal Forest Cover Changes: A Case Study in Mato Grosso, Brazil

Halbgewachs, M.; Wegmann, Martin; Ponte, Emmanuel Da

doi:10.3390/rs14081907

Cited by 8 publications

(4 citation statements)

References 72 publications

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“…It is very important to point out that some authors took advantage of the online availability of reference data to validate reference data (e.g., [114,123,127,140,145,152,231,448,496]). Many efforts are being made to create the networks of accurate validation data [48,[538][539][540].…”

Section: Validation Of the Reference Data With Other Reference Datamentioning

confidence: 99%

“…Many efforts are being made to create the networks of accurate validation data [48,[538][539][540]. For example, Zhao et al [140] exploited in situ measurements of the Leaf Area Index (LAI) that were provided by the VALERI project [540], whereas Halbgewachs et al [123], Lu et al [423], Shimabukuro et al [353], and Tarazona Coronel [127] utilized validation data that were provided by the Program for Monitoring Deforestation in the Brazilian Amazon (PRODES) [541].…”

Section: Validation Of the Reference Data With Other Reference Datamentioning

confidence: 99%

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Spatial Validation of Spectral Unmixing Results: A Systematic Review

Cavalli

2023

Remote Sensing

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The pixels of remote images often contain more than one distinct material (mixed pixels), and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared effort has enabled the development of techniques for retrieving information from mixed pixels. The most analyzed, implemented, and employed procedure is spectral unmixing. Among the extensive literature on the spectral unmixing, nineteen reviews were identified, and each highlighted the many shortcomings of spatial validation. Although an overview of the approaches used to spatially validate could be very helpful in overcoming its shortcomings, a review of them was never provided. Therefore, this systematic review provides an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing the papers that were published not only in 2011and 2010, but also in 1996 and 1995. The key criterion is that the results of the spectral unmixing were spatially validated. The Web of Science and Scopus databases were searched, using all the names that were assigned to spectral unmixing as keywords. A total of 454 eligible papers were included in this systematic review. Their analysis revealed that six key issues in spatial validation were considered and differently addressed: the number of validated endmembers; sample sizes and sampling designs of the reference data; sources of the reference data; the creation of reference fractional abundance maps; the validation of the reference data with other reference data; the minimization and evaluation of the errors in co-localization and spatial resampling. Since addressing these key issues enabled the authors to overcome some of the shortcomings of spatial validation, it is recommended that all these key issues be addressed together. However, few authors addressed all the key issues together, and many authors did not specify the spatial validation approach used or did not adequately explain the methods employed.

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Section: Validation Of the Reference Data With Other Reference Datamentioning

confidence: 99%

Section: Validation Of the Reference Data With Other Reference Datamentioning

confidence: 99%

Spatial Validation of Spectral Unmixing Results: A Systematic Review

Cavalli

2023

Remote Sensing

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“…Many recent papers employed Landsat medium-resolution imagery time series, together with selected landscape metrics, to map changes in forest patterns and to evaluate the fragmentation of forest habitats [98][99][100][101]. Part of them are dedicated to environmentally protected areas, such as Natura 2000 forested sites [62,102], fragile forest ecosystems under anthropogenic pressure [103,104] or urban-forested grounds [105], but there are other studies with predictive modelling of forest habitats fragmentation [106] based on long-time series analysis.…”

Section: Study Limitations and Future Perspectivesmentioning

confidence: 99%

Forest Habitat Fragmentation in Mountain Protected Areas Using Historical Corona KH-9 and Sentinel-2 Satellite Imagery

et al. 2022

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Forest habitat fragmentation is one of the global environmental issues of concern as a result of forest management practices and socioeconomic drivers. In this context, a constant evaluation of natural habitat conditions still remains a challenge in order to achieve a general image of the environmental state of a protected area for proper sustainable management. The purpose of our study was to evaluate the evolution of forest habitat in the last 40 years, focusing on Bucegi Natural Park, one of the most frequented protected areas in Romania, as relevant for highly human-impacted areas. Our approach integrates a historical panchromatic Corona KH-9 image from 1977 and present-day Sentinel-2 multispectral data from 2020 in order to calculate a series of spatial metrics that reveal changes in the pattern of the forest habitat and illustrate forest habitat fragmentation density. Object-based oriented analysis with supervised maximum likelihood classification was employed for the production of forest cover fragmentation maps. Ten landscape metrics were adapted to the analysis context, from patch statistics to proximity index. The results show a general growth of the forest surface but also an increase in habitat fragmentation in areas where tourism was developed. Fragmentation indices explain that larger and compact patches feature natural park protected forests after the spruce–fir secondary canopies were grown during the last 4–5 decades. The number of patches decreased to half, and their average size is double that of before. The method can be of extensive use for environmental monitoring in protected areas management and for understanding the environmental history connected to present-day problems that are to be fixed under rising human pressure.

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“…However, together with the neighboring municipality of Aripuanã, it is the current deforestation hotspot of the Brazilian Amazon, especially along the BR-174 highway, in the eastern part of the municipality, and where our study area is located nearby. Colniza is already facing intense landscape fragmentation [43].…”

Section: Study Areamentioning

confidence: 99%

Mapping Secondary Vegetation of a Region of Deforestation Hotspot in the Brazilian Amazon: Performance Analysis of C- and L-Band SAR Data Acquired in the Rainy Season

Kiyohara

Sano

2022

Forests

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The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to discriminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwestern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algorithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an overall accuracy and Kappa coefficient of 84% and 0.78, respectively. The RF outperformed the SVM classifier to discriminate these five LULC classes in 14% of overall accuracy for both ALOS-2 and Sentinel-1 data sets. This study also showed that the textural attributes derived from the GLCM method are highly sensitive to the moving window size to be applied to the GLCM method. The results of this study can assist the future development of an operation system based on dual-frequency SAR data to monitor re-suppression of SV in the Brazilian Amazon or in other tropical rainforests.

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A Spectral Mixture Analysis and Landscape Metrics Based Framework for Monitoring Spatiotemporal Forest Cover Changes: A Case Study in Mato Grosso, Brazil

Cited by 8 publications

References 72 publications

Spatial Validation of Spectral Unmixing Results: A Systematic Review

Spatial Validation of Spectral Unmixing Results: A Systematic Review

Forest Habitat Fragmentation in Mountain Protected Areas Using Historical Corona KH-9 and Sentinel-2 Satellite Imagery

Mapping Secondary Vegetation of a Region of Deforestation Hotspot in the Brazilian Amazon: Performance Analysis of C- and L-Band SAR Data Acquired in the Rainy Season

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