A5018593225- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Remote-Sensing Image Classification
- Forest ecology and management
- Land Use and Ecosystem Services
- Species Distribution and Climate Change
- Fire effects on ecosystems
- Forest Ecology and Biodiversity Studies
- Conservation, Biodiversity, and Resource Management
- Advanced Image Fusion Techniques
- Leaf Properties and Growth Measurement
- Wildlife-Road Interactions and Conservation
- Image Retrieval and Classification Techniques
- Environmental and biological studies
- Automated Road and Building Extraction
- Spectroscopy and Chemometric Analyses
- Wood and Agarwood Research
- Botanical Research and Applications
- Geochemistry and Geologic Mapping
- Bee Products Chemical Analysis
- Cloud Computing and Resource Management
- Remote Sensing and Land Use
- Infrastructure Maintenance and Monitoring
- Urban and spatial planning
- Advanced Neural Network Applications
Military Institute of Engineering
2018-2024
Universidade de São Paulo
2024
Forest Science and Research Institute
2024
Universidade Presbiteriana Mackenzie
2021
National Institute for Space Research
2011-2019
Universidade Federal do Rio Grande do Sul
2013
Instituto Nacional de Tecnologia
2013
Abstract Mapping forest types and tree species at regional scales to provide information for ecologists managers is a new challenge the remote sensing community. Here, we assess potential of U‐net convolutional network, recent deep learning algorithm, identify segment (1) natural forests eucalyptus plantations, (2) an indicator disturbance, Cecropia hololeuca , in very high resolution images (0.3 m) from WorldView‐3 satellite Brazilian Atlantic rainforest region. The networks trees were...
Mapping tropical tree species at landscape scales to provide information for ecologists and forest managers is a new challenge the remote sensing community. For this purpose, detection delineation of individual crowns (ITCs) prerequisite. Here, we present method automatic crown based only on very high resolution images from WorldView-2 satellite apply it region Atlantic rain with highly heterogeneous canopy cover – Santa Genebra reserve in Brazil. The works successive steps that involve...
Tropical forests concentrate the largest diversity of species on planet and play a key role in maintaining environmental processes. Due to importance those forests, there is growing interest mapping their components getting information at an individual tree level conduct reliable satellite-based forest inventory for biomass distribution qualification. Individual crown could be manually gathered from high resolution satellite images; however, achieve this task large-scale, algorithm identify...
Mangrove forests are vulnerable ecosystems that require broad-scale monitoring. Various solutions based on satellite imagery have emerged for this purpose but still suffer from the lack of methods to accurately delineate individual tree crowns (ITCs). Within-stand variability in crown size and shape, clumping fragmentation, understory vegetation hamper delineation these ecosystems. To cope with factors, proposed method combines a deep learning-based enhancement ITCs marker-controlled...
Monitoring ecological restoration has been historically dependent on traditional inventory methods based detailed information obtained from field plots. New paradigms are now needed to successfully achieve as a large‐scale, long‐lasting transformative process. Fortunately, advances in technology allow for unprecedented shifts the way planned, implemented, and monitored. Here, we describe our vision how use of new technologies by generation ecologists may revolutionize monitoring coming...
This study summarizes the advances in mangrove species mapping based on multispectral and hyperspectral imagery achieved over last decade. The influence of diversity sensor specifications performances various classification approaches are discussed. Based limitations previous approaches, we propose a novel framework to map at medium, high, very-high spatial resolution using images. relies multitask convolutional neural network achieve accurate pixel object (i.e. individual tree crown) level....
Most of the current deforestation detection systems rely on cloud-free optical images, which are difficult to obtain in tropical regions. A synthetic aperture radar (SAR) is nearly unaffected by clouds, thus providing valuable insights for detection. In conditions, use images usually provides better results than SAR data alone. Optical-SAR fusion has been hailed as a promising way improve However, it was poorly investigated, particularly when affected clouds. This letter employs optical-SAR...
Natural disturbances like hurricanes can cause extensive disorder in forest structure, composition, and succession. Consequently, ecological, social, economic alterations may occur. Terrestrial laser scanning (TLS) deep learning have been used for estimating attributes with high accuracy, but to date, no study has combined both TLS assessing the impact of hurricane disturbance at individual tree level. Here, we aim assess capability convolutional neural networks (CNNs) classifying...
Hyperspectral remote sensing can provide information about species richness over large areas and may be useful for discrimination in tropical environments. Here, we analyze the main sources of variability leaf spectral signatures trees examine potential spectroscopic reflectance measurements (450 to 2450 nm) tree discrimination. We assess within- among-species perform a feature selection procedure identify wavebands which most differ from each other. discriminative power these by calculating...