scholarly journals Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data

2014 ◽  
Vol 30 ◽  
pp. 203-216 ◽  
Author(s):  
Christopher E. Churches ◽  
Peter J. Wampler ◽  
Wanxiao Sun ◽  
Andrew J. Smith
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Landsat Data

scholarly journals ­ Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing

2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Single Leaf ◽  
Sentinel 2A ◽  
Sentinel 2

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

scholarly journals ­ Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing

2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Single Leaf ◽  
Sentinel 2A ◽  
Sentinel 2

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 46 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

scholarly journals USO DE IMAGENS LANDSAT PARA O MONITORAMENTO DA COBERTURA FLORESTAL DE TRÊS RPPNs EM RONDÔNIA

Nativa ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 205
Author(s):  
Micheli Leite Zanchetta ◽  
Diogo Martins Rosa
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Sustainable Use ◽  
Conservation Units ◽  
Landsat Images ◽  
Natural Heritage ◽  
Area Of Interest ◽  
Forest Coverage ◽  
Classification Of Images

O desmatamento ilegal na região amazônica vem crescendo muito nas últimas décadas, os maiores avanços e mais preocupantes estão dentro de Unidades de Conservação (UCs). Nesse contexto, esse estudo teve o objetivo de avaliar a eficiência de três Reservas Particulares de Patrimônio Natural (RPPN) para a conversação da cobertura florestal. Para isso, foi realizado o monitoramento da cobertura do solo de três RPPNs (Seringal Assunção, Vale das Antas e Água boa) com uso da classificação supervisionada das imagens Landsat 5 e 8, referentes aos anos de criação de cada RPPN e o ano de 2018. Para realizar a classificação foram coletados ~60 pixels por área de interesse (ROI), as classes selecionadas foram: água, solo exposto e floresta. Com o monitoramento das três RPPNs foi observado um aumento entre 2% até 35% de cobertura florestal nas RPPNs. O monitoramento das RPPNs com o uso de imagens Landsat possibilitou detectar a eficiência da regeneração natural da cobertura florestal, bem como a preservação da vegetação nativa. Portanto, conclui-se que as RPPNs são eficientes para conter o desmatamento, porém são necessárias mais pesquisas nesse sentido, visto que há poucos trabalhos de monitoramento de unidades de conservação em Rondônia e no Brasil.Palavras-chave: monitoramento; unidade de conservação; uso sustentável. USE OF LANDSAT IMAGES FOR THE MONITORING OF THE FOREST COVERAGE OF THREE PRIVATE NATURAL HERITAGE RESERVE (RPPNs) IN RONDÔNIA ABSTRACT: Illegal deforestation in the amazon region has been growing a lot in recent years, the biggest and most worrying advances are within Conservation Units (CUs). This research aims to evaluate the efficiency of three Private Natural Heritage Reserve (RPPN) for the conservation of the forest cover. Therefore, for this research, three RPPNs (Seringal Assunção, Vale das Antas and Água Boa) were monitored using the supervised classification of images Landsat 5 and 8, corresponding to the years of creation of each RPPN and 2018. To perform the classification were collected ~60 pixels per area of interest (ROI), the classes selected were water, exposed soil and forest. After monitoring the three RPPNs, it was possible to observe an increase between 2% up to 35% of forest cover in the RPPNs. With the monitoring of the RPPNs using the images Landsat it was possible to detect the efficiency of the natural regeneration of the forest cover, as well as the preservation of the native vegetation. Therefore, it is possible to conclude that RPPNs are efficient to contain deforestation. However, further research is still needed in this area, since there are few researches on the monitoring of conservation units in Rondônia as well as in Brazil.Keywords: monitoring; conservation unit; sustainable use.

scholarly journals Predicting Pinus monophylla forest cover in the Baja California Desert by remote sensing

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4603 ◽  
Author(s):  
Jonathan G. Escobar-Flores ◽  
Carlos A. Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A. Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Probability Of Occurrence ◽  
Single Leaf ◽  
Sentinel 2

The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world’s only one-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This tree is distributed as a relict subspecies, at elevations of between 1,010 and 1,631 m in the geographically isolated arid Sierra La Asamblea, an area characterized by mean annual precipitation levels of between 184 and 288 mm. The aim of this research was (i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea by using Sentinel-2 images, and (ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that (i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to the finer resolution (×3) and greater number of bands (×2) relative to Landsat-8 data, which is publically available free of charge and has been demonstrated to be useful for estimating forest cover, and (ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine the sites where conifers can become established and persist. An atmospherically corrected a 12-bit Sentinel-2A MSI image with 10 spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index (NDVI). Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multiple linear binominal logistical regression and Random Forest classification including cross validation were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Using supervised classification of Sentinel-2 satellite images, we estimated that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed most to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). Ruggedness was the most influential environmental predictor variable, indicating that the probability of occurrence of P. monophylla was greater than 50% when the degree of ruggedness terrain ruggedness index was greater than 17.5 m. The probability of occurrence of the species decreased when the mean temperature in the warmest month increased from 23.5 to 25.2 °C. Ruggedness is known to create microclimates and provides shade that minimizes evapotranspiration from pines in desert environments. Identification of the P. monophylla stands in Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

scholarly journals Analysis of Deforested Area Using Google Earth Engine in The Period 2001-2020 In the Apurimac Region

2022 ◽  
pp. 1-14
Author(s):  
Walquer Huacani ◽  
Nelson P. Meza ◽  
Franklin Aguirre ◽  
Darío D. Sanchez ◽  
Evelyn N. Luque
Keyword(s):  
Decision Tree ◽  
Supervised Classification ◽  
Forest Cover ◽  
Google Earth ◽  
Environmental Data ◽  
University Of Maryland ◽  
Planetary Scale ◽  
Google Earth Engine ◽  
The University

The objective of this study is to analyze the deforestation of forest cover in the Apurimac region between 2001 and 2020 using the Google Earth Engine (GEE) platform, a planetary-scale platform for the analysis of environmental data. The methodology used in the analysis of the deforested area is based on the classification of cover, using a supervised classification method developed by the University of Maryland, based on a "decision tree".

scholarly journals ­ Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing

2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Single Leaf ◽  
Sentinel 2A ◽  
Sentinel 2

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

scholarly journals Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize

2019 ◽  
Vol 11 (7) ◽  
pp. 823 ◽  
Author(s):  
Carly Voight ◽  
Karla Hernandez-Aguilar ◽  
Christina Garcia ◽  
Said Gutierrez
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Agricultural Practices ◽  
Google Earth ◽  
Training Data ◽  
Forest Loss ◽  
Landsat 8 ◽  
Forest Cover Change ◽  
Change Analysis

Tropical forests and the biodiversity they contain are declining at an alarming rate throughout the world. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened by unsustainable agricultural practices. Deforestation data allow forest managers to efficiently allocate resources and inform decisions for proper conservation and management. This study utilized satellite imagery to analyze recent forest cover and deforestation in southern Belize to model vulnerability and identify the areas that are the most susceptible to future forest loss. A forest cover change analysis was conducted in Google Earth Engine using a supervised classification of Landsat 8 imagery with ground-truthed land cover points as training data. A multi-layer perceptron neural network model was performed to predict the potential spatial patterns and magnitude of forest loss based on the regional drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests, predicting a decrease from 75.0% mature forest cover in 2016 to 71.9% in 2026. This study represents the most up-to-date assessment of forest cover and the first vulnerability and prediction assessment in southern Belize with immediate applications in conservation planning, monitoring, and management.

scholarly journals ­ Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing

2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Single Leaf ◽  
Sentinel 2A ◽  
Sentinel 2

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

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