Using MODIS Near Real Time Deforestation Detection and Daily Thermal Anomalies Product for Land Cover Change Monitoring
Freitas, Instituto Nacional de Pesquisas Espaciais – INPE, email@example.com
Shimabukuro, Instituto Nacional de Pesquisas Espaciais – INPE, firstname.lastname@example.org
Rosa, Instituto Nacional de Pesquisas Espaciais – INPE, email@example.com
The detection of deforestation and thermal anomalies in a near real time is of fundamental importance for Government policy and surveillance of forest areas. A near real-time detection would allow control of the increase of new clearings and monitoring of the deforestation pattern and carbon emissions. In this context, this work has the objective to propose a methodology to detect deforestation in near real time using MODIS - Moderate Resolution Imaging Spectroradiometer images. The study area is located in the Mato Grosso State, Brazilian Amazonia, encompassing three micro regions (Arinos, Teles Pires e Sinop) that has been characterized by high deforestation rates in the last years. The methodology consists on to characterize and detect deforested areas using near real time daily MODIS images. The MOD02 product used in this work has 250 m of spatial resolution (red and near infrared-NIR bands), while the other band, Mid Infra-Red- MIR have 500 m resampled to 250 m of spatial resolution. The MODIS Thermal Anomalies product 1km spatial resolution were used for to characterize fire activities on new deforested areas. The total of 114 images, acquired in 2005 to 2006 time period, were used in this analysis. The linear spectral mixture model was applied to the MODIS reflectance images of RED, NIR and MIR spectral bands acquired and processing near real time. The method uses forest map and temporal crisp classification based on vegetation and soil fraction. The daily classification of deforestation hotspot are showed in probability intervals. The field campaign data, PRODES and DETER information, and Landsat TM and CBERS CCD images were utilized as ground truth for validation of the methodology. The use of multitemporal images of MOD02 product presented a global accuracy of 92.72 % to detect the deforestation when compared with ground truth. The initial analysis shows temporal lags from 0 to 300 days between deforestation detect and thermal anomalies product. In another’s hands, the analysis shows that there are hotspots located in forest areas before deforestation detection. This suggest the use of fire in the initial deforestation phase.
Science Theme: LC (Land Use and Land Cover Change)