Analisa NDVI Citra Satelit Landsat Multi Temporal Untuk Pemantauan Deforestasi Hutan Kabupaten Aceh Utara

One of the functions of the forest is natural disaster such as flood control and the landslide that is how these forests absorb water into the root of the tree. Most forests in North Aceh Regency is protected forest which has undergone deforestation due to the presence of illegal logging and opening of new land like planting oil palm that impact against water infiltration. This research was conducted to identify deforestation forests in 2000, 2003 and 2015 using the techniques of remote sensing by satellite images landsat landsat 7 and 8. The method used was algorithm NDVI to get the classification of forest distribution and the level of deforestation forests based on the density of the vegetation from the Forestry Department 2003. Analysis of the rate of deforestation and loss of vast forests is done by leveraging the value of NDVI and other supporting data.The results showed that the NDVI value for forest distribution based on vegetation density in 2000 was -0,620438 � 0,628743, in 2003 between -0,364238 � 0,530055 and in 2015 between -0,274592 � 0,642049. The rate of deforestation in the district of North Aceh based on the value of the vegetation index (NDVI) yields 3 classes of deforestation are severe deforestation, light deforestation and not deforested, in 2000 there was deforestation of 25,62%, in 2003 it was 99,91% and in 2015 amounted to 15,89%, most deforestation occurs in production forests.

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Analisis Perubahan Penggunaan Lahan Menggunakan Metode NDVI (Normalized Difference Vegetation Index) pada Kecamatan Natar, Kabupaten Lampung Selatan

Unram Journal of Community Service ◽

10.29303/ujcs.v2i1.22 ◽

2021 ◽

Vol 2 (1) ◽

pp. 17-22

Author(s):

Fattur Rachman

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Sector ◽

Land Conversion ◽

Field Observations ◽

Land Uses ◽

Vegetation Density ◽

Index Method ◽

Landsat 7 ◽

Open Land

Natar District is one of the districts in South Lampung Regency which has an area of 213.77 km2 or around 21,377 HA. In the agricultural sector, most of the land in Natar District is dominated by maize and paddy fields. This study aims to determine changes in land use in 2002, 2009 and 2019 in Natar District, South Lampung Regency. This study uses imagery from Landsat 7 and 8 processed in the NDVI (Normalized Difference Vegetation Index) method with the formula "NDVI = (NIR-RED) / (NIR + RED)". After processing the data, field observations were made to 30 sample points which were spread evenly throughout the Natar District. In this study, the results showed that land conversion to open land increased every year, on the other hand the area of land with low to moderate vegetation density decreased every year. In field observations, it was found that various land uses ranging from settlements, markets, and various uses for agricultural and plantation land.

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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)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v3i3.232664 ◽

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

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DETECTION OF ACID SLUDGE CONTAMINATED AREA BASED ON NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) VALUE

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2014.v11.a2598 ◽

2017 ◽

Vol 11 (1) ◽

pp. 21

Author(s):

Nanik Suryo Haryani ◽

Sayidah Sulma ◽

Junita Monika Pasaribu

Keyword(s):

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Dry Season ◽

Wet Season ◽

Multi Temporal ◽

Acid Sludge ◽

Landsat Satellite ◽

Solid Form

The solid form of oil heavy metal waste is known as acid sludge. The aim of this research is to exercise the correlation between acid sludge concentration in soil and NDVI value, and further studying the Normalized Difference Vegetation Index (NDVI) anomaly by multi-temporal Landsat satellite images. The implemented method is NDVI. In this research, NDVI is analyzed using the remote sensing data on dry season and wet season. Between 1997 to 2012, NDVI value in dry season is around – 0.007 (July 2001) to 0.386 (May 1997), meanwhile in wet season NDVI value is around – 0.005 (November 2006) to 0.381 (December 1995). The high NDVI value shows the leaf health or thickness, where the low NDVI indicates the vegetation stress and rareness which can be concluded as the evidence of contamination. The rehabilitation has been executed in the acid sludge contaminated location, where the high value of NDVI indicates the successfull land rehabilitation effort.

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Analysis of the Influence of Vegetation Index Choice on the Classification of Satellite Images for Monitoring Forest Pathology

10.51130/graphicon-2020-2-3-49 ◽

2020 ◽

pp. paper49-1-paper49-12

Author(s):

Evgeniy Trubakov ◽

Olga Trubakova

Keyword(s):

Satellite Images ◽

Vegetation Index ◽

Vegetation Indices ◽

Multispectral Images ◽

Classification Methods ◽

Planted Forests ◽

The Earth ◽

Evaluation Algorithm ◽

And Control

Rational use of natural resources and control over their recovery, as well as over destruction due to natural and technogenic causes, is currently one of the most urgent problems of the humanity. Forests are no exception. Multispectral images from Earth’s satellites are most often used for monitoring changes in forest planting. This is due to the fact that merging images taken in certain spectra makes it possible to recognize vegetation containing chlorophyll quite well. It also allows to detect changes in the level of chlorophyll, which shows the differences between healthy and damaged plants. Large areas of planted forests create the need to process huge amounts of data, which is difficult to do manually. One of the most important stages of image processing is the classification of objects in these images. This paper deals with various classification methods used to solve the problem of classifying images of remote sensing of the Earth. As a result, it was decided to evaluate the accuracy of classification methods on various vegetation indices. In the course of the study, the evaluation algorithm was determined, as well as one of the options for analyzing the results obtained. Conclusions were made about the work of classification methods on different vegetation indices.

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Classification of Naked Barley Cultivation Area Using Multi-temporal Satellite Images and Relationship between Naked Barley Yield and Climate Data

Journal of the Association of Korean Geographers ◽

10.25202/jakg.7.2.1 ◽

2018 ◽

Vol 7 (2) ◽

pp. 129-139

Author(s):

Hyun-Su Park ◽

Dong-Ho Jang

Keyword(s):

Satellite Images ◽

Climate Data ◽

Naked Barley ◽

Multi Temporal

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Investigations of the Yenisei River delta dynamics in multi-temporal satellite images

Geodesy and Cartography ◽

10.22389/0016-7126-2020-955-1-34-47 ◽

2020 ◽

Vol 955 (1) ◽

pp. 34-47

Author(s):

V.I. Kravtsovа ◽

O.V. Vakhnina

Keyword(s):

Twentieth Century ◽

21St Century ◽

Satellite Images ◽

Sediment Accumulation ◽

Yenisei River ◽

Small Areas ◽

Landsat 7 ◽

Multi Temporal ◽

The Yenisei River ◽

Erosion Intensity

Previous studies of the Yenisei delta dynamics in the second half of the twentieth century showed a slight increase in its area, prevailing over erosion. In this work, images from Landsat-7 satellites of 1999 and Sentinel-2B in 2017 were used to study the changes in the first decades of the 21st century. A pre-map was obtained for them showing the land formation areas at the water site (sediment accumulation) and appearing water instead of land (erosion of the coast). Small areas of these changes can’t be displayed on observing maps, but the characteristics of the accumulation and erosion intensity is possible, corresponding to the average annual velocity of the coastline moving. Such maps, compiled in the scale of 1

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Use of Savitzky - Golay Filters to Minimize Multi-temporal Data Anomaly in Land use Land cover mapping

Indian Journal of Forestry ◽

10.54207/bsmps1000-2019-650st3 ◽

2019 ◽

Vol 42 (4) ◽

pp. 362-368

Author(s):

Ram Kumar Singh ◽

◽

Vinay Shankar Prasad Sinha ◽

Pawan Kumar Joshi ◽

Manoj Kumar ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Vegetation Index ◽

Spectral Signature ◽

Atmospheric Effects ◽

Temporal Data ◽

Land Use Land Cover ◽

Distance Method ◽

Multi Temporal

Land use land cover characterization and mapping have become a prerequisite in all environmental Planaing. The array of satellites deployed in the space provides multi-temporal images that can be used for the land use land cover classification. But, much often these multi-temporal images have data noise and anomaly owing to the cloud and atmospheric effects. This brings pseudo hikes and lows in data adding classification with possible errors. We present a method for the removal of data anomaly where monthly data of MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (MODIS 13Q1) was used for the classification of images over a large area encompassing the SAARC nations. MODIS multi-temporal data were filtered usinga Savitzky-Golay (S-G) algorithm which provided smoothened data and the seasonality (start, end of the season) were identified. Phenology profile curves were created for the characterization of the agriculture and forestry feature classes. The S-G filtered images and raw MODIS data phenology profile curves were compared for the eleven classes of land cover, viz., ever green needle forest, ever green broad leave, deciduous broad leave, shrub, savannas, grass, agriculture, built-up, water, snow (ice), and barren. Spectral signature separability was also compared using Euclidean spectral distance method. In conclusion, it was observed that multi-spectral S-G filtered data were more useful for the classification of agriculture and forestry classes for a larger coverage.

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Analysis of change in vegetation cover linked to public policies, case study: Tenosique, Tabasco, Mexico.

10.5194/egusphere-egu21-6708 ◽

2021 ◽

Author(s):

Jacob Nieto ◽

Gabriela Vidal García ◽

Mariana Patricia Jácome Paz ◽

Tania Ximena Ruiz Santos ◽

Juan Manuel Nuñez ◽

...

Keyword(s):

Vegetation Cover ◽

Satellite Images ◽

Vegetation Index ◽

Agricultural Sector ◽

Public Policies ◽

Anthropogenic Activity ◽

Time Range ◽

Free Access ◽

Ecological Processes ◽

Landsat 7

<p>Currently, natural areas are being devastated by anthropogenic activity. Activities such as agriculture, illegal logging, non-organic farms, and livestock exploitation, disrupt an ecosystem that has been in balance for many years. Therefore, regulations implemented by governments are required for their preservation. However, these regulations are not always the most used in terms of conservation. Such is the case of the town "Tenosique", in this area is one of the most important rivers in Mesoamerica, the Usumacinta River, which is a great regulator of ecological processes and is connected to Mexico with Guatemala. This site has been under the influence of regulations applied to the economic impulse of the area, whether for agricultural and livestock activities, which has affected the apparent vegetation cover, unlike Guatemala that has opted for regulations with a forest conservation approach. These policies sought to boost the agricultural sector, but many deforested areas to carry out this activity turned out not to be suitable due to the type of soil. With the change of regime, financing ends and with it economic activity decreases, leaving the area quite affected and the communities with financial problems. Recently, conservation and protection actions were implemented in the area together with support for these communities. The proximity between Mexico and Guatemala visually shows the results of the application of different public policies. The objective of this study is to quantify the loss and gain of vegetation over time from satellite images of the area, in order to compare this statistic with the different government programs of each era. For this, at least 10 multispectral satellite images of free access will be used, from the Landsat 7 satellite, which has 30 meters of resolution but visually adjustable to 15 meters with the union of its panchromatic channel, and that cover a time range from 1999 to 2020. On these, two processes will be carried out: 1) a normalized vegetation index calculation and 2) a supervised classification. With which it is intended to measure the area and the greenness of a mask of the vegetation cover. The results will serve to update the projects carried out on the site and detect areas of priority interest resolution for larger projects, as well as the future estimation of the critical state of the site regarding the loss of vegetation cover and quantify the conservation efforts that have been carried out. carried out from 2008 to the present.</p>

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ANALISIS VEGETASI DI TAMAN WISATA ALAM (TWA) SUNGAI DUMAI, RIAU

Wahana Forestra Jurnal Kehutanan ◽

10.31849/forestra.v16i1.5720 ◽

2021 ◽

Vol 16 (1) ◽

pp. 25-36

Author(s):

Hanifah Ikhsani

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Vegetation Density ◽

Vegetation Analysis ◽

Landsat 8 Oli ◽

Density Class ◽

Vegetation Class ◽

Do So

TWA Sungai Dumai is a tourist forest area and ensuring the preservation of natural potential. However, there are problems that can disrupt the sustainability of it, including forest and land fires and conversion of land use to agriculture and oil palm plantations. Until now, there is no vegetation analysis using satellite imagery in TWA Sungai Dumai, so it is important to do so that can be managed sustainably. This study classification of vegetation density classes which are presented in the form of a vegetation density class map in it. This research uses Landsat-8 OLI / TIRS images from October 2017 and October 2020 which are processed to determine density class using Normalized Difference Vegetation Index algorithm. The vegetation density class with the highest area in 2017 was the vegetation density class (2380,832 ha or 66,819% of the total area), while the lowest area was the non-vegetation class (75,737 ha or 2,126% of the total area). The vegetation density class with the highest area in 2020 in TWA Sungai Dumai is dense vegetation density class (3205,039 ha or 89,950% of the total area), while the lowest area is non-vegetation class (1,637 ha or 0.046% of the total area)

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