Papers by Veraldo Liesenberg
Revista Brasileira De Fruticultura, 2022
This study carried out a mapping procedure focusing on apple orchards considering the planted are... more This study carried out a mapping procedure focusing on apple orchards considering the planted area, spatial location, altitude range, slope interval, and presence of anti-hail nets in the city of São Joaquim (Southern Santa Catarina Plateau, Brazil). Spectral images from the Sentinel-2 orbital platform acquired in August 2018 and an enhanced digital elevation model from the Shuttle Radar Topography Mission (SRTM) were used. In a GIS application configured with the SIRGAS 2000,4 reference system and UTM cartographic projection, Sentinel-2 constellation images and digital elevation models from the SRTM mission and more recently refined with sensor data Phased Array type L-band Synthetic Aperture Radar (PALSAR) were added. All images were resampled to a spatial resolution of 10m. The results were validated based on high spatial resolution images available from Google Earth. The results show that São Joaquim has a planted area of 7,974.80 ha, and only 12% use an anti-hail coverage system. The majority of the orchards range from one to five ha and belong to small producers. More than 50% of the orchards are between 1,200 and 1,400 m in altitude, with 45% of orchards located in areas with slopes between 8 to 20%. Interestingly, most of the orchards are concentrated in a radius of up to 20km from the urban center of São Joaquim, where industries and cooperatives are located for packaging, processing, and logistics. This study demonstrated that orbital data from Sentinel-2 can effectively quantify the distribution of apple orchards, being a viable and effective alternative for collecting information for agricultural monitoring. In this way, it enables efficient planning of apple production, such as technical assistance, marketing with producers, and production flow. Index terms: Apple tree culture; Fruit growing; Orchards; Remote Sensing; GIS. Mapeamento dos pomares de macieira no município de São Joaquim (Santa Catarina, Brasil) usando dados Sentinel-2 Resumo-Este estudo realizou um mapeamento das áreas de pomares de macieira considerando área plantada, localização, altitude, declividade e o uso de cobertura antigranizo, no município de São Joaquim (Planalto Sul Catarinense, Brasil). Foram empregadas imagens espectrais da plataforma orbital Sentinel-2 adquiridas em agosto de 2018, e o modelo digital de elevação aprimorado do Shuttle Radar Topography Mission (SRTM). Em aplicativo GIS configurado com o sistema de referência SIRGAS 2000,4 e projeção cartográfica UTM, foram adicionadas as imagens da constelação Sentinel-2 e os modelos digitais de elevação advindos da missão SRTM refinados mais recentemente com dados do sensor Phased Array type L-band Synthetic Aperture Radar (PALSAR). Todas as imagens foram reamostradas para a resolução espacial de 10 m. Os resultados foram validados com base em imagens disponíveis do Google Earth. Os resultados mostram que São Joaquim conta com área plantada de 7.974,80 ha de macieira, e destes 12% com sistema de cobertura antigranizo. A característica da região é de pequenos produtores em áreas de um a cinco ha. Um Percentual superior a 50% dos pomares encontra-se no intervalo entre 1.200 e 1.400 m de altitude. Com 45% dos pomares localizados em áreas com declividade entre 8 e 20%. Em relação à localização, os pomares estão concentrados principalmente no raio de até 20 km de distância em relação ao centro urbano de São Joaquim, onde se encontra quase a totalidade das indústrias e cooperativas para o seu acondicionamento, beneficiamento e logística. Este estudo demonstrou que dados orbitais do Sentinel-2 podem quantificar efetivamente a distribuição dos pomares de macieira, sendo uma alternativa viável e eficaz no levantamento das informações para o monitoramento agrícola. Desse modo, viabiliza o planejamento eficiente da produção de maçã, como a assistência técnica, a comercialização com os produtores e o escoamento da produção. Termos para indexação: Cultura da macieira; Fruticultura; Pomicultores; Sensoriamento remoto; GIS.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Nov 6, 2020
Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of ... more Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of image classification and does not require high performance computers locally. We investigate several data input scenarios for mapping native-vegetation and nonnative-vegetation in the Atlantic Forest region encompassed in a Landsat scene (224/076) acquired on November 28, 2019. The data input scenarios were: I-spectral bands (blue to shortwave infrared); II-NDVI (Normalized Difference Vegetation Index); III-mNDWI (modified Normalized Difference Water Index); IV-scenarios I and II; and V-scenarios I to III. Our results showed that the use of spectral bands added NDVI and mNDWI (scenario V) provided the best performance for the native-vegetation mapping, with accuracy of 96.64% and kappa index of 0.91.
Environmental Earth Sciences, Jul 26, 2011
EGU General Assembly Conference Abstracts, Apr 1, 2013
Elaborado por Maurício Amormino Júnior-CRB6/2422 O conteúdo dos artigos e seus dados em sua forma... more Elaborado por Maurício Amormino Júnior-CRB6/2422 O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores. 2019 Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais. www.atenaeditora.com.br conforme observado na tabela 3.
Ecological Informatics, Dec 1, 2021
This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) supe... more This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from highresolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Jun 1, 2013
Natural tropical peat swamp forests are important for their rich biodiversity and serve as a huge... more Natural tropical peat swamp forests are important for their rich biodiversity and serve as a huge carbon pool. However, peat swamp forests are decreasing due to deforestation, conversion into farm land, excessive draining, the use of shifting cultivation on a large scale, illegal logging, forest fire and palm oil plantation. Airborne laser scanning (ALS) also termed airborne Light Detection and Ranging (LiDAR) data is nowadays a good single sensor to investigate bio-geophysical parameters in remote tropical rain forest areas (e.g. tree canopy height which is strongly correlated with above ground biomass). Bi-temporal airborne LiDAR data acquired in August 2007 and August 2011 were used to characterize peat swamp forest changes located in Central Kalimantan, Indonesia. We measured the tree height and Canopy Height Model (CHM) with LiDAR, segmented the canopies and then compared the tree height with the field measurements. Additionally, we collected ground field measurements at Sabangau forest transect in order to characterize some biophysical properties of different peat swamp forest physiognomies such as diameter at breast height (DBH), tree-height, leaf area index (LAI), crown coverage and above ground biomass (AGB). From the bi-temporal LiDAR Data we analyzed the forest regrowth and the peat subsidence. This work can be promising in the REDD+ (Reducing Emissions from Deforestation and forest Degradation) framework of knowledge of tropical PSF. The LiDAR technology supports the MRV (Monitoring, Reporting, and Verification) aspect of REDD+.
Giscience & Remote Sensing, Jan 12, 2020
The classification of tree species can significantly benefit from high spatial and spectral infor... more The classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced classification methods. This study investigated the following topics concerning the classification of 16 tree species in two subtropical forest fragments of Southern Brazil: i) the potential integration of UAV-borne hyperspectral images with 3D information derived from their photogrammetric point cloud (PPC); ii) the performance of two machine learning methods (support vector machine-SVM and random forest-RF) when employing different datasets at a pixel and individual tree crown (ITC) levels; iii) the potential of two methods for dealing with the imbalanced sample set problem: a new weighted SVM (wSVM) approach, which attributes different weights to each sample and class, and a deep learning classifier (convolutional neural network-CNN), associated with a previous step to balance the sample set; and finally, iv) the potential of this last classifier for tree species classification as compared to the above mentioned machine learning methods. Results showed that the inclusion of the PPC features to the hyperspectral data provided a great accuracy increase in tree species classification results when conventional machine learning methods were applied, between 13 and 17% depending on the classifier and the study area characteristics. When using the PPC features and the canopy height model (CHM), associated with the majority vote (MV) rule, the SVM, wSVM and RF classifiers reached accuracies similar to the CNN, which outperformed these classifiers for both areas when considering the pixel-based classifications (overall accuracy of 84.4% in Area 1, and 74.95% in Area 2). The CNN was between 22% and 26% more accurate than the SVM and RF when only the hyperspectral bands were employed. The wSVM provided a slight increase in accuracy not only for some lesser represented classes, but also some major classes in Area 2. While conventional machine learning methods are faster, they demonstrated to be less stable to changes in datasets, depending on prior segmentation and hand-engineered features to reach similar accuracies to those attained by the CNN. To date, CNNs have been barely explored for the classification of tree species, and CNNbased classifications in the literature have not dealt with hyperspectral data specifically focusing on tropical environments. This paper thus presents innovative strategies for classifying tree species in subtropical forest areas at a refined legend level, integrating UAV-borne 2D hyperspectral and 3D photogrammetric data and relying on both deep and conventional machine learning approaches.
Geografia (Rio Claro), 2006
Em 18 de dezembro de 1999, a National Aeronautics and Space Administration (NASA) lançou o Satéli... more Em 18 de dezembro de 1999, a National Aeronautics and Space Administration (NASA) lançou o Satélite TERRA como marco principal do programa Earth Observing System (EOS), visando iniciar a mais abrangente missão científica até então tentada, destinada a gerar uma ...
Remote Sensing in Ecology and Conservation, Aug 11, 2022
Remote Sensing, Jan 31, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Giscience & Remote Sensing, Oct 1, 2010
In this study, we examine seasonal aspects and the potential of multi-angle CHRIS/PROBA data, acq... more In this study, we examine seasonal aspects and the potential of multi-angle CHRIS/PROBA data, acquired at two different dates, to improve forest classification. The test site is a typical peat swamp landscape located in Central Kalimantan, Indonesia. We focus on eight specific land use/cover categories from a single view angle and from a multi-angular perspective. We show that:(1) reflectance changes from the end of the monsoon to the beginning of the dry season in the visible were small and slightly positive for the forestry classes, whereas ...
Remote Sensing, Aug 4, 2021
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Forests, Jun 21, 2023
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Jul 17, 2022
Episodes, Jun 1, 2010
important to understand the full context of sedimentary basins, their evolution, and their hydroc... more important to understand the full context of sedimentary basins, their evolution, and their hydrocarbon reserves and potential. This will help to solve puzzle for pre-Pangean time back to 2600 Ma and provide constraints on pre-Pangean supercontinents. Considering this important aspect of study of dykes, the Organizing Committee of the 6th International Dyke Conference (IDC-6), which was held at the
Journal of Forestry Research, Jan 2, 2020
about silvicultural operations. Growth continuity and forest production indicate that any interve... more about silvicultural operations. Growth continuity and forest production indicate that any intervention should be directed at younger trees of smaller sizes, and that one of the main management factors for stand stability and growth is the formation of the stand and its capture of light.
International journal of applied earth observation and geoinformation, Apr 1, 2013
Abstract The classification of tropical fragmented landscapes and moist forested areas is a chall... more Abstract The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced ...
Journal of agricultural science, Jul 15, 2021
The form of distribution found for the dendro/morphometric variables determines the structure, st... more The form of distribution found for the dendro/morphometric variables determines the structure, stability, productivity of forest stands, being a tool to propose silvicultural interventions, management, conservation of species, and dynamics of this environment. Thus, this study evaluates, using probability density functions (pdf), the form of distribution of these variables for araucaria in five sites in southern Brazil, aiming to establish the dynamics and identify the existence of a standard-or the lack thereof-to propose the need for silvicultural interventions to conserve the species and the future forest structure. The Normal, Log-Normal, Weibull and Gamma probability density functions were tested. Results show no significant changes in the shape and dimension in the forest structure dynamics, but a period of stability in the pattern of dendro/morphometric values, resulting from the stagnation of the values of the variables, non-intervention in the forest, relationship with the site, density, competition, and position of the tree in the forest stratum, which compromises the future structure of this forest typology. The study proves that the distribution probability of the variables can be used in management for species conservation and future structure development, as this influences the growth dynamics and processes, resource availability, and the stability, diversity, vitality, and productivity of the species.
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Papers by Veraldo Liesenberg