scholarly journals ANÁLISE ESPAÇO-TEMPORAL DA COBERTURA VEGETAL EM UMA BACIA HIDROGRÁFICA NA REGIÃO DO MATOPIBA, BRASIL

Nativa ◽  
2018 ◽  
Vol 6 ◽  
pp. 737
Author(s):  
Luciano Cavalcante de Jesus França ◽  
João Batista Lopes Da Silva ◽  
Gerson Dos Santos Lisboa ◽  
Danielle Piuzana Mucida ◽  
Clebson Lima Cerqueira ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Environmental Planning ◽  
Vegetation Index ◽  
Temporal Analysis ◽  
Hydrographic Basin ◽  
Sparse Vegetation ◽  
Dense Vegetation ◽  
Original Vegetation ◽  
Environmental Agencies ◽  
Basin Area

A bacia hidrográfica do rio Uruçuí-Preto, Piauí, com área de 15.777 Km², vem caracterizando-se ao longo dos últimos 30 anos pela expansão do agronegócio, integrando a fronteira agrícola do MATOPIBA, composta por Maranhão, Tocantins, Piauí e Bahia.  Este estudo objetivou avaliar a mudança da cobertura vegetal nesta bacia hidrográfica entre 1984 a 2015. Utilizou-se imagens dos sensores TM e OLI dos satélites Landsat 5 e 8, respectivamente, para elaboração de mosaicos e realce da vegetação por meio do Índice de Vegetação da Diferença Normalizada (IVDN), para obtenção da evolução da mudança na cobertura da terra. Procedeu-se a classificação supervisionada (Máxima Verossimilhança), em cinco classes: solo exposto, área antropizada, vegetação rala, vegetação esparsa e vegetação densa. Os resultados atestaram intensa antropização na área analisada. Em 1984, a classe solo exposto correspondia a 390,3 km² do total da área da bacia, com aumento em 2015 para 1.498,20 km². Em 1984 existiam 7.743,2 km² de cobertura vegetal original, reduzida em 2015, para 3.487,40 km², com redução de 45,03% da classe de vegetação densa. Este estudo pode auxiliar em estratégias de atuação dos órgãos ambientais e planejamento ambiental para o desenvolvimento do agronegócio em consonância com a conservação dos recursos naturais, sobretudo no MATOPIBA.Palavras-chave: IVDN, desmatamento, Cerrado, sensoriamento remoto, Uruçuí-preto. SPACE-TEMPORAL ANALYSIS OF VEGETABLE COVERAGE IN A HYDROGRAPHIC BASIN OF THE REGION OF MATOPIBA, BRAZIL ABSTRACT:The hydrographic basin of the Uruçuí-Preto river, Piauí, with an area of 15.777 km², has been characterized over the last 30 years by agricultural expansion, integrating the agricultural frontier of MATOPIBA, composed of the states of Maranhão, Tocantins, Piauí and Bahia. The objective of this study was to evaluate the change in the vegetation cover in this basin from 1984 to 2015. Images of the TM and OLI sensors of the Landsat 5 and 8 satellites, respectively, were used to elaborate mosaics and vegetation enhancement through the Vegetation Index (IVDN), to obtain the evolution of the change in land cover. The supervised classification (Maximum Likelihood) was carried out in five classes: exposed soil, anthropic area, sparse vegetation, sparse vegetation and dense vegetation. The results showed intense anthropization in the analyzed area. In 1984, the exposed soil class corresponded to 390.3 km² of the total basin area, with an increase in 2015 to 1,498.20 km². In 1984 there were 7,743.2 km² of original vegetation cover, reduced in 2015, to 3,487.40 km², with a reduction of 45.03% of the class of dense vegetation. This study can aid in strategies for the performance of state environmental agencies and environmental planning in order to reconcile the development of agribusiness with the conservation of natural resources.Keywords: NDVI, deforestation, Cerrado, remote sensing, Uruçuí-preto.

scholarly journals Annually Urban Fractional Vegetation Cover Dynamic Mapping in Hefei, China (1999–2018)

2021 ◽  
Vol 13 (11) ◽  
pp. 2126
Author(s):  
Yuliang Wang ◽  
Mingshi Li
Keyword(s):  
Standard Deviation ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Dynamic Range ◽  
Dynamic Change ◽  
Estimation Model ◽  
Sparse Vegetation ◽  
Fractional Vegetation Cover ◽  
Dynamic Mapping ◽  
Multi Temporal

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.

scholarly journals SPATIO-TEMPORAL ANALYSIS OF LANDSCAPE PATTERNS IN THE CATOLÉ WATERSHED, NORTHERN MINAS GERAIS1

2018 ◽  
Vol 42 (4) ◽  
Author(s):  
Rodrigo Nogueira Martins ◽  
Selma Alves Abrahão ◽  
Danilo Pereira Ribeiro ◽  
Ana Paula Ferreira Colares ◽  
Marco Antonio Zanella
Keyword(s):  
Landscape Metrics ◽  
Riparian Vegetation ◽  
Temporal Analysis ◽  
Bare Soil ◽  
Landscape Patterns ◽  
Core Area ◽  
Sparse Vegetation ◽  
Dense Vegetation ◽  
Processing Level ◽  
Spatio Temporal

ABSTRACT The aim of this study was to quantify the spatio-temporal changes in land use/ cover (LULC), as well as analyze landscape patterns over a 20-year period (1995 - 2015) in the Catolé watershed, northern Minas Gerais State, using landscape metrics. The LULC maps were obtained using Landsat 5 and 8 data (Processing level 1) through supervised classification using the maximum likelihood classifier. Seven thematic classes were identified: dense vegetation, sparse vegetation, riparian vegetation, cropland, planted forest, bare soil, and water. From the LULC maps, classes related to the natural landscape (dense, sparse, and riparian vegetation) were grouped into forest patches, which was then ordered by size: very small (< 5 ha); small (5 - 10 ha); medium (10 - 100 ha); large (100 ha); and a general class (no distinction of patch size). Then, metrics of area, size and density, edge, shape, proximity and core area were calculated. The dense vegetation portion of the study area decreased considerably within a given time, while the portion of cropland and bare soil increased. Overall, in the Catolé river basin, the total area of natural vegetation decreased by 3,273 hectares (4.62%). Landscape metrics analysis exhibited a reduction in the number of very small patches, although the study area was still considered as fragmented. Moreover, a maximum edge distance of 50 m is suggested for conducting studies involving core area metrics in the Catolé watershed, as values above this distance would eliminate the very small patches.

scholarly journals Impact of resettlement scheme on vegetation cover and its implications on conservation in Chewaka district of Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Alemayehu Abera ◽  
Teshome Yirgu ◽  
Abera Uncha
Keyword(s):  
Rural Communities ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Management Practices ◽  
Normalized Difference Vegetation Index ◽  
Average Rate ◽  
Sampling Procedure ◽  
Rapid Reduction ◽  
Sparse Vegetation ◽  
Major Factors

Abstract Background Resettlement has been conceived as a viable solution to the continual impoverishment and destitution of Ethiopian rural communities. However, it has considerable impacts on natural resources of the environment at destination areas. This study was carried out to evaluate impact of resettlement scheme on vegetation cover and its implications on conservation in Chewaka district of Ethiopia. Methods The study utilized ArcGIS10.3, ERDAS Imagine 9.1, Landsat imageries of 2000, 2009, 2018 and socio-economic data to analyze the LULC of the district. Normalized Difference Vegetation Index was employed to detect vegetation cover changes of the area. The study was conducted on the seven kebeles of Chewaka district and the total households of the sample kebeles are 3340. Through multistage sampling procedure a total of 384 households were selected from sample kebeles. Data were collected using questionnaires, GPS, interviews, focus group discussions and field observations. The collected data were analyzed both quantitatively and qualitatively. Results The results showed that Chewaka district has undergone substantial LULC change since population resettlement in the area. A rapid reduction of woodland (34.6%), forest (59.9%), grassland (50.5%) and bareland (46.8%) took place between 2000 and 2018, while built-up areas and cultivated lands have expanded at an average rate of 90.7 and 1515.7 ha/year respectively. The results of NDVI revealed that the extent of dense and sparse vegetation cover have decreased by 26.1% and 20.6% respectively, whereas non-vegetation cover has increased by 14,340.2 ha during the study period. It was found that rapid population growth following resettlement program, farmland and settlement expansion, deforestation, human-induced forest fire, lack of land use plan, unwise utilization and low management practices were the major factors that underpin the observed changes in the area. Conclusions Resettlement scheme has resulted in the depletion and dynamics of vegetation cover in Chewaka district. Therefore, the study suggests urgent attention on conservation of the remaining vegetation resources for sustainable utilization.

EFFECT OF INDIAN OCEAN CYCLONE ON COASTAL REGION USING REMOTE SENSING AND GIS

2020 ◽  
pp. 2050005
Author(s):  
KHUSHBOO KUMARI ◽  
ASMITA A. DEO
Keyword(s):  
Remote Sensing ◽  
Tamil Nadu ◽  
Andhra Pradesh ◽  
Vegetation Index ◽  
Coastal Region ◽  
Risk Zone ◽  
Sparse Vegetation ◽  
Dense Vegetation ◽  
Risk Zonation ◽  
Before And After

The effect of four different cyclones making land fall on four different coastal regions is studied viz., Nisha (2008, Tamil Nadu), Laila (2010, Andhra Pradesh), Sidr (2007, Bangladesh) and land depression BOB 03 (2008, Orissa). Remote sensing and Geographic Information System (GIS) technique are used to detect change in Land use and Land cover (LU/LC). Change in vegetation cover by Normalized Vegetation Index (NDVI) is also investigated. Further, preparation of slope map, processing of buffer zoning map is exercised. These parameters are analyzed to find the impression of cyclones after hitting the coastal boundaries by considering the images before and after the cyclone has passed. Change detection assessment of LU/LC features provides information for monitoring the trend of change in an area. In almost every considered region, it is found that dense vegetation is changed to sparse vegetation. Also, decrease in the irrigated cropland due to heavy rainfall caused by cyclone is noted. Risk zone is created by buffer ring of cyclone track to spot the area under risk zone. The area calculation suggests the effect of cyclone at the distance of 20–50[Formula: see text]km from the cyclone path which is validated from the slope effect on LU/LC, also. Some of the common features such as dense vegetation, show decrease in the area by 71%, 17%, 67% and 60%, or settlement area also shows decrease by 38%, 15%, 57% and 17% due to Laila, BOB 03, Nisha and Sidr cyclones, respectively. Increase in shrubland mix with rangeland by 18%, 113% and 98% is also seen due to Laila, Nisha and Sidr cyclones. Other LU/LC shows changes such as, water bodies increasing by 6%, 189% due to BOB 03 and Nisha cyclones. Changes are also seen in sparsed vegetation, which is decreased in Orissa and Tamil Nadu and increased in Andhra Pradesh and Bangladesh. It is demonstrated that by preparing risk zonation map, risk assessment can be done.

scholarly journals Mapeamento e Quantificação da Cobertura Vegetal no Agreste Central de Pernambuco Utilizando Técnicas de Empilhamento e o NDVI (Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique)

2011 ◽  
Vol 3 (3) ◽  
pp. 157
Author(s):  
Daniel Rodrigues Lira ◽  
Maria do Socorro Bezerra de Araújo ◽  
Everardo Valadares De Sá Barretto Sampaio ◽  
Hewerton Alves da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Digital Processing ◽  
Sparse Vegetation ◽  
New Sensors

O mapeamento e monitoramento da cobertura vegetal receberam consideráveis impulsos nas últimas décadas, com o advento do sensoriamento remoto, processamento digital de imagens e políticas de combate ao desmatamento, além dos avanços nas pesquisas e gerações de novos sensores orbitais e sua distribuição de forma mais acessível aos usuários, tornam as imagens de satélite um dos produtos do sensoriamento remoto mais utilizado para análises da cobertura vegetal das terras. Os índices de cobertura vegetal deste trabalho foram obtidos usando o NDVI - Normalized Difference Vegetation Index para o Agreste central de Pernambuco indicou 39,7% de vegetação densa, 13,6% de vegetação esparsa, 14,3% de vegetação rala e 10,5% de solo exposto. O NDVI apresentou uma caracterização satisfatória para a classificação do estado da vegetação do ano de 2007 para o Agreste Central pernambucano, porém ocorreu uma confusão com os índices de nuvens, sombras e solos exposto, necessitando de uma adaptação na técnica para um melhor aprimoramento da diferenciação desses elementos, constituindo numa recombinação de bandas após a elaboração e calculo do NDVI.Palavras-chave: Geoprocessamento; sensoriamento remoto; índice de vegetação. Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique ABSTRACTIn recent decades, advanced techniques for mapping and monitoring vegetation cover have been developed with the advent of remote sensing. New tools for digital processing, the generation of new sensors and their orbital distribution more accessible have facilitated the acquisition and use of satellite images, making them one of the products of remote sensing more used for analysis of the vegetation cover. The aim of this study was to assess the vegetation cover from Central Agreste region of Pernambuco State, using satellite images TM / LANDSAT-5. The images were processed using the NDVI (Normalized Difference Vegetation Index) technique, generating indexes used for classification of vegetation in dense, sparse and scattered. There was a proportion of 39.7% of dense vegetation, 13.6% of sparse vegetation, 14.3% of scattered vegetation and 10.5% of exposed soil. NDVI technique has been used as a useful tool in the classification of vegetation on a regional scale, however, needs improvement to a more precise differentiation among levels of clouds, shadow, exposed soils and vegetation. Keywords: Geoprocessing, remote sensing, vegetation index

scholarly journals A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine

2021 ◽  
Vol 936 (1) ◽  
pp. 012038
Author(s):  
Benedict ◽  
Lalu Muhamad Jaelani
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Data ◽  
Temporal Analysis ◽  
Google Earth ◽  
Vegetation Density ◽  
Angle Sensor ◽  
Study Results ◽  
Google Earth Engine

Abstract Java is Indonesia’s and the world’s most populous island. The increase in population on the island of Java reduces the area of forest and other vegetation covers. Landslides, floods, and other natural disasters are caused by reduced vegetation cover. Furthermore, it has the potential to lead to the extinction of flora and fauna. The Normalized Difference Vegetation Index (NDVI) can be used to monitor the vegetation cover. This study analyzes the NDVI changes value from 2005 to 2020 using Terra and Aqua MODIS image data processed using Google Earth Engine. Processing was carried out in some stages: down-setting, performing NDVI processing, calculating monthly average NDVI, calculating annual average NDVI, and analyzing. From the study results, the NDVI value of Terra and Aqua MODIS data has a solid but imperfect correlation coefficient due to differences in orbital time which causes differences in solar zenith angle, sensor viewing angle, and azimuth angle. Then from this study, it was found that overall, changes in vegetation density cover on the island of Java decreased, which was indicated by the NDVI decline rate of -0.00047/year. The most significant decrease in NDVI value occurred in the period 2015–2016, covering an area of 13994.630 km2, and the most significant increase in NDVI occurred in the period 2010–2011, covering an area of 2256.101 km2.

ESTIMATIVA DE MUDANÇAS TEMPORAIS COM O CÁLCULO DE ÍNDICES DE VEGETAÇÃO DO MUNICÍPIO DE ITABERÁ (SP)

2017 ◽  
Vol 32 (2) ◽  
pp. 195
Author(s):  
Ana Clara De Barros ◽  
Amanda Aparecida De Lima ◽  
Felipe De Souza Nogueira Tagliarini ◽  
Zacarias Xavier de Barros
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Near Infrared ◽  
Temporal Analysis ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Chlorophyll Contents ◽  
Spectral Bands ◽  
Vegetation Water ◽  
Images Processing

O presente trabalho teve como objetivo realizar a análise temporal da cobertura vegetal, num período de 10 anos do município de Itaberá-SP, utilizando os índices de vegetação NDVI e NDWI por meio de imagens de satélite. Do ano de 2005 foram utilizadas duas imagens do Landsat 5 de órbita/ponto 221/76 e 221/77 e uma imagem de 2015 do Landsat 8, órbita/ponto 221/76. As bandas espectrais utilizadas foram: 3,4 e 5 do Landsat 5 e 4,5 e 6 do Landsat 8 que correspondem aos comprimentos de ondas do vermelho (RED), infravermelho próximo (NIR) e infravermelho médio (SWIR1), respectivamente. Através das análises dos índices, constatou que as áreas que possuem baixos valores de NDVI também possuem baixos valores de NDWI, o que indica uma vegetação que sofre estresse hídrico e com baixo teor de clorofila. Os valores mais altos indicam vegetação fotossinteticamente ativa, que contêm maior teor de umidade.PALAVRAS-CHAVE: Sensoriamento remoto, processamento de imagens, cobertura vegetal. TEMPORAL ANALYSISUSING VEGETATION INDEX OF VEGETATION COVER IN ITABERA (SP)ABSTRACT: The objective of this work was to carry out the temporal analysis of the vegetation cover, in a period of 10 years of Itaberá-SP county, making use of the vegetation index NDVI and NDWI of satellites images. Two Landsat 5’s images of 2005 with path/row 221/76 and 221/77 and one Landsat 8’s image, path/row 221/76 were used. The spectral bands used ware: 3, 4 and 5 of the Landsat 5 and 4, 5 and 6 of the Landsat 8 that correspond to red waves lengths (RED), near infrared (NIR) and medium infrared (SWIR1), respectively. It was found that areas with low NDVI values also have low NDWI values, indicating vegetation water stress and low chlorophyll contents. The highest values indicate Photosynthetically active vegetation, which contain higher moisture contents.KEYWORDS: Remote sensing, images processing, vegetal cover.

scholarly journals Assessment of Vegetation Cover Degradation Using Remote Sensing and GIS Techniques along Sudanese Red Sea Coast (Suakin to Ashad)

2016 ◽  
Vol 8 (1) ◽  
pp. 55
Author(s):  
Atiyat Abdalla Fadoul Nuri ◽  
Amna Ahmed Hamid ◽  
El Abbas Doka M. Ali ◽  
Eltegani Mohamed Salih
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Cover ◽  
Field Survey ◽  
Remote Sensing And Gis ◽  
Sparse Vegetation ◽  
Dense Vegetation ◽  
Red Sea Coast ◽  
Vegetation Species ◽  
Sea Coast

This study aimed to assess the vegetation cover degradation in the Sudanese Red Sea coast (from Suakin to Ashad) after the drought during the period from 2000 - 2011. Remote Sensing and GIS techniques were used beside field survey to conduct the study. Moderate Resolution Imaging Spectrometer (MODIS) terra 2000 -2001, 2005-2006 and 2010-2011 time-Series images mainly the 16 days Normalized Difference Vegetation Index (NDVI) product and Enhanced Thematic Mapper plus (ETM+) images dated 2005 and 2010 were used. Unsupervised classification methods were used to detect vegetation cover of the study area. Based on field survey investigations, beside the data collected on the study area and image interpretation, it was evident that season 2005-2006 and season 2006-2010 are good seasons in the vegetation cover compared to season 2000-2001. Five land cover classes were detected; wet land, bare land and three classes of vegetation cover (dense vegetation, moderately dense vegetation and sparse vegetation cover). Spectral signatures of the three dominant land cover vegetation species were detected.  Areas of the three classes of vegetation cover area (dense vegetation, moderately dense vegetation and sparse vegetation cover) were calculated per km2. The study concluded that MODIS could be used as a cost effective tool in assessing land cover changes and monitoring vegetation cover degradation.As well, it could also be used to detect fairly the different vegetation species in arid and semiarid regions.

scholarly journals Change of Vegetation Cover at Rohingya Refugee Occupied Areas in Cox’s Bazar District of Bangladesh: Evidence from Remotely Sensed Data

2019 ◽  
Vol 11 (1-2) ◽  
pp. 9-16
Author(s):  
M Rahman ◽  
MS Islam ◽  
TA Chowdhury
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remotely Sensed ◽  
Refugee Camps ◽  
Remotely Sensed Data ◽  
Index Method ◽  
Sparse Vegetation ◽  
Dense Vegetation ◽  
Before And After ◽  
One Year

Nearly one million Rohingya Refugees are living in Cox’s Bazar—a south-eastern district of Bangladesh; among them, more than half a million have fled Myanmar since August 2017. There are always some impacts of refugee settlements on the host environment. Hence, this study has made an initiative to investigate the changes of vegetation covers in four refugee occupied Unions of Teknaf and Ukhia Upazila. Analysing the remotely sensed Landsat imageries using Normalized Difference Vegetation Index method, the spatial extent of sparse vegetation, moderate vegetation, and dense vegetation before and after the occurrence of 2017 Influx have been quantified. The result reveals that nearly 21,000 acres of dense vegetation and more than 1700 acres of moderate vegetation have been reduced within the period of one year in-between 2017 and 2018. On the other hand, during the same period, the refugee sites have been expanded by almost 6000 acres. The main reasons for this drastic reduction of vegetation include the construction of refugee camps by felling the forest and consumption of firewood by refugees from the surrounding forest of their camps. Arrangement of alternative cooking fuel, relocation of refugees, reforestation, and accelerating the repatriation process may reduce the further degradation of vegetation. J. Environ. Sci. & Natural Resources, 11(1-2): 9-16 2018

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