%0 Conference Proceedings
%4 sid.inpe.br/plutao/2023/12.11.16.43
%2 sid.inpe.br/plutao/2023/12.11.16.43.49
%F lattes: 1913003589198061 1 ShimabukuroASDHCDM:2023:FRIMDE
%T Fraction images derived from landsat mss, tm and oli images for monitoring forest cover of rondônia state, brazilian amazon
%D 2023
%A Shimabukuro, Yosio Edemir,
%A Arai, Egidio,
%A Silva, Gabriel Máximo da,
%A Dutra, Andeise Cerqueira,
%A Hoffmann, Tania Beatriz,
%A Cassol, Henrique Luís Godinho,
%A Duarte, Valdete,
%A Martini, Paulo Roberto,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress yosio.shimabukuro@inpe.br
%@electronicmailaddress egidio.arai@inpe.br
%@electronicmailaddress gabriel.maximo@inpe.br
%@electronicmailaddress andeise.dutra@inpe.br
%@electronicmailaddress tania.hoffmann@inpe.br
%@electronicmailaddress henrique.cassol@inpe.br
%@electronicmailaddress valdete.duarte@inpe.br
%@electronicmailaddress paulo.martini@inpe.br
%B IEEE International Geoscience and Remote Sensing Symposium
%C Pasadena, CA
%8 2023
%S Proceedings
%K Fraction Image, Image Processing,, Deforestation, Forest, Linear Spectral Mixing Model,, Brazilian Amazon, Landsat series.
%X This article presents a new method for monitoring forest cover in the state of Rondônia, in the Brazilian Amazon. The proposed method applies the Linear Spectral Mixing Model (LSMM) to Landsat datasets (MSS, TM and OLI) to derive annual vegetation, soil, and shade fraction images for the period 1980 2020. These fraction images have the advantages of reducing the volume of data to be analyzed and highlighting the target characteristics. Then, we applied a threshold method to classify forest, non-forest, hydrography, and deforestation areas. The proposed method showed to be consistent and flexible allowing to change the threshold values according to the fraction images to obtain the results with high accuracy. The results obtained by the proposed method can be easily checked over the RGB image mosaic. This kind of information is very important for environmental and climate change studies and for supporting government conservation efforts.
%@language en
%3 Fraction Images Derived from Landsat Mss.pdf