scholarly journals Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thais Lourençoni ◽  
Carlos Antonio da Silva Junior ◽  
Mendelson Lima ◽  
Paulo Eduardo Teodoro ◽  
Tatiane Deoti Pelissari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Kernel Density ◽  
Cloud Platform ◽  
Mato Grosso ◽  
Monitoring Programs ◽  
Data Variability ◽  
Southern Amazonia ◽  
Remote Sensing Techniques ◽  
Crop Enhancement

AbstractThe guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo.

scholarly journals Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 273 ◽  
Author(s):  
Peter Surový ◽  
Karel Kuželka
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Forest Cover ◽  
Forest Health ◽  
Basal Area ◽  
Tree Height ◽  
Radar Sensors ◽  
Tree Number ◽  
Remote Sensing Techniques ◽  
New Sensors

In recent decades, remote sensing techniques and the associated hardware and software have made substantial improvements. With satellite images that can obtain sub-meter spatial resolution, and new hardware, particularly unmanned aerial vehicles and systems, there are many emerging opportunities for improved data acquisition, including variable temporal and spectral resolutions. Combined with the evolution of techniques for aerial remote sensing, such as full wave laser scanners, hyperspectral scanners, and aerial radar sensors, the potential to incorporate this new data in forest management is enormous. Here we provide an overview of the current state-of-the-art remote sensing techniques for large forest areas thousands or tens of thousands of hectares. We examined modern remote sensing techniques used to obtain forest data that are directly applicable to decision making issues, and we provided a general overview of the types of data that can be obtained using remote sensing. The most easily accessible forest variable described in many works is stand or tree height, followed by other inventory variables like basal area, tree number, diameters, and volume, which are crucial in decision making process, especially for thinning and harvest planning, and timber transport optimization. Information about zonation and species composition are often described as more difficult to assess; however, this information usually is not required on annual basis. Counts of studies on forest health show an increasing trend in the last years, mostly in context of availability of new sensors as well as increased forest vulnerability caused by climate change; by virtue to modern sensors interesting methods were developed for detection of stressed or damaged trees. Unexpectedly few works focus on regeneration and seedlings evaluation; though regenerated stands should be regularly monitored in order to maintain forest cover sustainability.

scholarly journals EMPREGO DO SENSORIAMENTO REMOTO PARA ANÁLISE DO USO E OCUPAÇÃO DO SOLO NO PERÍMETRO IRRIGADO VÁRZEAS DE SOUSA-PB

Irriga ◽  
2022 ◽  
Vol 1 (4) ◽  
pp. 722-729
Author(s):  
LEONCIO GONÇALVES RODRIGUES ◽  
ANA CÉLIA MAIA MEIRELES ◽  
CARLOS WAGNER OLIVEIRA
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Sustainable Development ◽  
Water Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ideal Tool ◽  
Major Producer ◽  
Irrigated Perimeter ◽  
Remote Sensing Techniques

EMPREGO DO SENSORIAMENTO REMOTO PARA ANÁLISE DO USO E OCUPAÇÃO DO SOLO NO PERÍMETRO IRRIGADO VÁRZEAS DE SOUSA-PB     LEONCIO GONÇALVES RODRIGUES1; ANA CÉLIA MAIA MEIRELES2 E CARLOS WAGNER OLIVEIRA3   1Mestrando em Desenvolvimento Regional Sustentável, Universidade Federal do Cariri-UFCA, Rua Ícaro Moreira de Sousa, nº 126, Muriti, 63130-025, Crato, Ceará, Brasil, [email protected]. 2 Professora titular do Programa de pós graduação em Desenvolvimento Regional Sustentável, Universidade Federal do Cariri-UFCA, Rua Ícaro Moreira de Sousa, nº 126, Muriti, 63130-025, Crato, Ceará, Brasil, [email protected]  3 Professor titular do Programa de pós graduação em Desenvolvimento Regional Sustentável, Universidade Federal do Cariri-UFCA, Rua Ícaro Moreira de Sousa, nº 126, Muriti, 63130-025, Crato, Ceará, Brasil, [email protected]     1 RESUMO   O perímetro irrigado várzeas de Sousa (PIVAS) é um grande produtor de culturas como coco, banana, sorgo, algodão dentre outras. Tem grande importância para o desenvolvimento econômico da região do alto sertão da Paraíba. Possui características impares como a distribuição de água para todos os lotes por potencial gravitacional. Para a sustentabilidade do perímetro é necessário o monitoramento constante de suas áreas, para se poder desenvolver estratégias que auxiliam no desenvolvimento sustentável. Nesse sentido, o sensoriamento remoto é uma ferramenta ideal por permitir a obtenção rápida e precisa de informações sobre uma área, o que pode auxiliar na tomada de decisão. Partindo desse pressuposto, o objetivo deste trabalho é apresentar um conjunto de técnicas de sensoriamento que possibilitem o monitoramento de áreas irrigadas ou ambientais. Para tanto foi determinado do uso e ocupação do solo, o índice de vegetação por diferença normalizada (NDVI) e o índice de vegetação ajustado ao solo (SAVI) para o PIVAS. Onde se observou que as técnicas de sensoriamento remoto auxiliam na compreensão de áreas no espaço e tempo.   Palavras-chave: monitoramento, manejo, satélite.     RODRIGUES, L. G.; MEIRELES, A. C. M.; OLIVEIRA, C, W. USE OF REMOTE SENSING TO ANALYZE THE USE AND OCCUPANCY OF THE SOIL IN THE PERIMETER IRRIGATED VÁRZEAS DE SOUSA-PB.     2 ABSTRACT   The floodplain-irrigated perimeter of Sousa (PIVAS) is a major producer of crops such as coconut, banana, sorghum, cotton, among others. It is of great importance for the economic development of the upper wilderness region of Paraiba. It has unique characteristics such as water distribution to all lots by gravitational potential. For the sustainability of the perimeter, constant monitoring of its areas is necessary, to be able to develop strategies that help in sustainable development. In this sense, remote sensing is an ideal tool as it allows for quick and accurate obtaining information about an area, which can help in decision making. Based on this assumption, this work aims to present a set of sensing techniques that enable monitoring of irrigated or environmental areas. For this purpose, the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) were determined for the PIVAS. Where it was observed that remote sensing techniques help understand areas in space and time.   Keywords: monitoring, management, satellite.

scholarly journals Análisis del efecto de variables ambientales en la estimación de la erosionabilidad (Factor K) = Analysis of the Effect of Environmental Variables on the Soil Erodibility Estimation (K Factor)

2018 ◽  
pp. 195
Author(s):  
Pedro Perez Cutillas ◽  
Gonzalo G. Barberá ◽  
Carmelo Conesa García
Keyword(s):  
Remote Sensing ◽  
Environmental Variables ◽  
Input Data ◽  
Vegetation Index ◽  
Mineralogical Composition ◽  
Soil Erodibility ◽  
Estimation Algorithms ◽  
K Factor ◽  
Remote Sensing Techniques

El objetivo principal de este trabajo se centra en la determinación y análisis de las variables ambientales que influyen en las divergencias de las estimaciones de erosionabilidad a partir de dos métodos, aplicando tres algoritmos de estimación del Factor K. La exploración de esta información permite conocer el peso que ejerce el origen de los datos de entrada a los modelos en el cómputo de erosionabilidad y qué importancia tiene en función del algoritmo elegido para la estimación del Factor K. Los resultados muestran que las pendientes, así como los índices de vegetación (NDVI) y de composición mineralógico (IOI) obtenidos mediantes técnicas de teledetección han   mostrado los valores de asociación más elevados entre ambos métodos.The main goal of this work is to determine and analyze the influence of environmental variables on the changes of two erodibility methods, through the application of three estimation algorithms of K Factor. The analysis of this information allows knowing the significance of the input data to the models in the erodibility estimation, and likewise the consequence of the algorithm selected for the estimation of K Factor. The results show that the slopes, as well as the vegetation index (NDVI) and the mineralogical composition index (IOI), generated both by remote sensing techniques, have shown the highest values of association between methods.

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