A5064927225- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Land Use and Ecosystem Services
- Soil and Land Suitability Analysis
- Fire effects on ecosystems
- Forest ecology and management
- Geography and Environmental Studies
- Plant Water Relations and Carbon Dynamics
- Environmental and biological studies
- Leaf Properties and Growth Measurement
- Conservation, Biodiversity, and Resource Management
- Advanced DC-DC Converters
- Landslides and related hazards
- Multilevel Inverters and Converters
- Fire Detection and Safety Systems
- Advanced Battery Technologies Research
- Forest Ecology and Biodiversity Studies
- Agricultural and Food Sciences
- Flood Risk Assessment and Management
- Advanced Image Fusion Techniques
- Precipitation Measurement and Analysis
University of Florida
2025
National Institute for Space Research
2022-2024
Universidade Federal de Santa Catarina
2019
Universidade Federal Rural da Amazônia
2018
Southern U.S. forests are essential for carbon storage and timber production but increasingly impacted by natural disturbances, highlighting the need to understand their dynamics recovery. Canopy cover is a key indicator of forest health resilience. Advances in remote sensing, such as NASA’s GEDI spaceborne LiDAR, enable more precise mapping canopy cover. Although provides accurate data, its limited spatial coverage restricts large-scale assessments. To address this, we combined with...
Abstract Aim Our aim was to quantify the influence of climate and land use on major fires that occurred during 2020 drought over Brazilian Pantanal region. Location Alto Paraguay Basin, central‐western flank Brazil. Time period 2003–2020. Methods We calculated climatic burned area anomalies Spearman's correlation between precipitation sea surface temperature (SST). assessed water coverage identify impact drought. produced fire recurrence maps, identified areas for first time in 2020,...
This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses random machine learning algorithm available Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and Landsat-8 Operational Land Imager (OLI) for 1985 1995 2013 2021 time periods, respectively. These periods were selected planted areas’ rotation,...
This work aims to develop a new method map Land Use and Cover (LULC) classes in the São Paulo State, Brazil, using Landsat-8 Operational Imager (OLI) data. The novelty of proposed consists selecting images based on spectral temporal characteristics LULC classes. First, we defined six be mapped year 2020 as forest, forest plantation, water bodies, urban areas, agriculture, pasture. Second, visually analyzed their variability over year. Then, pre-processed these highlight each class. For...
Secondary forests provide essential ecosystem services, especially in helping to mitigate climate change with the storage of carbon aboveground biomass tree species. In this context, present research aimed analyze spatial distribution secondary and estimate accumulation land cover different ages state Pará. The patterns Pará were evaluated hot spot analysis algorithms using data from TerraClass project for 2004–2014 time period. results showed that did not occur randomly space, but suggested...
The integration of lidar (light detection and ranging) with machine learning offers a promising method for accurately estimating mapping various vegetation attributes. This study demonstrated the effective use terrestrial laser scanning (TLS) random forest (RF) approach to achieve precise total surface aboveground biomass (TSAGB) estimates at high resolution in regularly burned ecosystems southeastern United States. Our site is located Osceola National Forest (ONF), which part USFS Southern...
This article presents a land use and cover (LULC) classification map based on Random Forest (RF) classifier algorithm in the São Paulo State (Brazil), using Landsat-8 OLI data. The method consists time series images from January to December of 2020 spectral temporal characteristics LULC classes. We performed class by considering: water, urban area, forest, agriculture, forest plantation pasture. Then, we pre-processed selected targets highlight each class. After that, was RF for individually...
This article presents a method to map the extent of forest plantation in an area located São Paulo State (Brazil). The proposed applies Linear Spectral Mixing Model (LSMM) Landsat Thematic Mapper (TM) datasets derive annually vegetation, soil and shade fraction images for local analysis. We used 30 m annual mosaics TM during 1985 1995 time period. These have advantage reduce volume data be analyzed highlighting target characteristics. Then, we generated only one mosaic each dataset computing...
This article presents a land use and cover (LULC) classification map using Random Forest algorithm in the São Paulo State (Brazil), an assessment of burned areas two products (MCD64A1 MapBiomas Fire). The method uses Landsat Operational Land Imager (OLI) time series images from January to December 2020. We performed class by considering: water, urban area, forest formation, sugarcane, agriculture, plantation pasture. For each class, we used different spectral bands image fraction according...
Utilizamos mosaicos anuais de 20 m derivados imagens Sentinel-2 MSI entre 2016 e 2022.Para classificar as plantações florestais, foram utilizados das frações vegetação, sombra solo.Também gerados para cada imagem fração, calculando o valor máximo ao longo do período analisado, facilitando a classificação áreas ocupadas por florestais na área estudo.O método proposto permitiu predominantemente Eucalyptus spp.Além disso, uma área-piloto (Mogi-Guaçu) foi utilizada avaliar monitorar os estádios...
This article presents a new method for monitoring forest cover in the state of Rondônia, Brazilian Amazon. The proposed applies Linear Spectral Mixing Model (LSMM) to Landsat datasets (MSS, TM and OLI) derive annual vegetation, soil, shade fraction images period 1980 – 2020. These have advantages reducing volume data be analyzed highlighting target characteristics. Then, we applied threshold classify forest, non-forest, hydrography, deforestation areas. showed consistent flexible allowing...
Objetivou-se analisar a cobertura vegetal e o uso do solo município de Colares, Pará, além caracterizar os fragmentos florestais presentes nesta paisagem. A análise proposta foi realizada por meio produtos técnicas sensoriamento remoto geoprocessamento, visando conhecer estrutura da paisagem, modo oferecer subsídios ao planejamento sua ocupação territorial. Utilizaram-se imagens digitais sensor Operational Land Imager (OLI)/Landsat 8, órbita/ponto 223/061, bandas 4, 5 6, processadas...
This paper addresses the modeling and control of a novel Forward dc-dc converter with multiplexer for active voltage cell balancing bank Lithium-Ion batteries. The is developed using state-space averaging model. system based on cascade feedback linearization approach forward an event-driven cells. Simulation results in time-domain allow to evaluate performance proposed control.