Traditional forest restoration (FR) monitoring methods employ spreadsheets and photos taken at the ground level. Since remotely piloted aircraft (RPA) generate a panoramic high resolution and georeferenced view of the entire area of interest, this technology has high potential to improve the traditional FR monitoring methods. This study evaluates how low-cost RPA data may contribute to FR monitoring of the Brazilian Atlantic Forest by the automatic remote measurement of Tree Density, Tree Height, Vegetation Cover (area covered by trees), and Grass Infestation. The point cloud data was processed to map the Tree Density, Tree Height, and Vegetation Cover parameters. The orthomosaic was used for a Random Forest classification that considered trees and grasses as a single land cover class. The Grass Infestation parameter was mapped by the difference between this land cover class (which considered trees and grasses) and the Vegetation Cover results (obtained by the point cloud data processing). Tree Density, Vegetation Cover, and Grass Infestation parameters presented F_scores of 0.92, 0.85, and 0.64, respectively. Tree Height accuracy was indicated by the Error Percentage considering the traditional fieldwork and the RPA results. The Error Percentage was equal to 0.13 and was considered accurate because it estimated a 13% shorter height for trees that averaged 1.93 m tall. Thus, this study showed that the FR structural parameters were accurately measured by the low-cost RPA, a technology that contributes to FR monitoring. Despite accurately measuring the structural parameters, this study reinforced the challenge of measuring the Biodiversity parameter via remote sensing because the classification of tree species was not possible. After all, the Brazilian Atlantic Forest is a biodiversity hotspot, and thus different species have similar spectral responses in the visible spectrum and similar geometric forms. Therefore, until improved automatic classification methods become available for tree species, traditional fieldwork remains necessary for a complete FR monitoring diagnostic.
Eixo: Uso e ocupação das terras e legislação ambiental ResumoA erosão Chitolina, destaca-se em Goiás por apresentar dinâmica evolutiva complexa e ser a maior erosão no lado goiano na emblemática bacia do Araguaia. Em regiões agrícolas, o monitoramento da evolução desses processos é fundamental para as estratégias de projetos de recuperação. O objetivo do trabalho foi avaliar à dinâmica erosiva evolutiva da erosão Chitolina, considerando os dados do cadastro do processo erosivo realizado em 1998 e a realização de campo com uso de veículo aéreo não tripulado (VANT) em 2016. Os resultados constaram que o processo encontra-se estabilizado com ausência de desenvolvimento remontante, abertura de novas ramificações, o leito da erosão está sendo entulhado com material que se rompe dos taludes e não há curso definido de drenagem; alguns piping na porção média da feição. A utilização do VANT no mapeamento e monitoramento desses processos otimiza recursos físicos e humanos no cadastro e dados automatizados facilmente validados em campo.Palavras chave: Voçoroca Chitolina; Degradação do solo; mapeamento; VANT. IntroduçãoDentre os diversos impactos ambientais associados às atividades agropecuárias, a análise e a avaliação dos processos erosivos antrópicos comumente ganha destaque, por implicar no comprometimento da qualidade hídrica, perda de solo agricultável e ameaças ao equilíbrio ecossistêmico (HELFER et al., 2003;CARVALHO et al., 2006). Segundo a FAO (2015), os processos erosivos eliminam por ano entre 25 e 40 bilhões de toneladas de solo no mundo, o que implica diretamente na produtividade das culturas e em outras funções do solo, além de comprometimento dos cursos hidrográficos.A erosão é um processo evolutivo das paisagens, mas em regiões tropicais ela se intensifica, dada a concentração e intensidade das chuvas em áreas submetidas a ações de desmatamentos e com parâmetros morfográficos e morfológicos favoráveis. O processo evolui nos estágios laminares (escoamento difuso da água) e lineares (concentração de linhas de fluxo das águas superficiais e
Remotely Piloted Aircraft Systems (RPAS) are already a reality in Brazil. They are used in different fields of knowledge to obtain digital products that contribute to the identification, monitoring, control, and precision of the agricultural decision process. However, the cost-benefit of applying this technology to produce seedlings in commercial tomato nurseries needs better evaluation. This research analyzes the use of an RPAS with RGB camera over areas of table tomato seedlings and compares the cartographic products with the information obtained through semi-structured interviews with rural property owners in the States of Goiás, Minas Gerais, and the Federal District (Brasília). The results were not divided into external area applications and internal (greenhouse) mapping, as adopting technology for monitoring the seedling production process inside greenhouses is still economically unfeasible compared to human identification, thus not justifying the investment. Nonetheless, images of the external area provide crucial information for planning a nursery, considering its structural aspects and adequate disposal. Keywords: Seedling production; Digital Image Processing; UAV; Drones.
Woody plant encroachment in grassy ecosystems is a widely reported phenomenon associated with negative impacts on ecosystem functions. Most studies of this phenomenon have been carried out in arid and semi-arid grasslands. Therefore, studies in tropical regions, particularly savannas, which are composed of grassland and woodland mosaics, are needed. Our objective was to evaluate the accuracy of woody encroachment classification in the Brazilian Cerrado, a tropical savanna. We acquired dry and wet season unmanned aerial vehicle (UAV) images using RGB and multispectral cameras that were processed by the support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers. We also compared two validation methods: the orthomosaic and in situ methods. We targeted two native woody species: Baccharis retusa and Trembleya parviflora. Identification of these two species was statistically (p < 0.05) most accurate in the wet season RGB images classified by the RF algorithm, with an overall accuracy (OA) of 92.7%. Relating to validation assessments, the in situ method was more susceptible to underfitting scenarios, especially using an RF classifier. The OA was higher in grassland than in woodland formations. Our results show that woody encroachment classification in a tropical savanna is possible using UAV images and field surveys and is suggested to be conducted during the wet season. It is challenging to classify UAV images in highly diverse ecosystems such as the Cerrado; therefore, whenever possible, researchers should use multiple accuracy assessment methods. In the case of using in situ accuracy assessment, we suggest a minimum of 40 training samples per class and to use multiple classifiers (e.g., RF and DT). Our findings contribute to the generation of tools that optimize time and cost for the monitoring and management of woody encroachment in tropical savannas.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.