PEMANFAATAN DATA PENGINDERAAN JAUH DAN TEKNOLOGI SIG UNTUK MENGIDENTIFIKASI BUILT UP AREA DAN KAITANNYA DENGAN RTRW DI KABUPATEN KENDAL

2019 ◽  
Vol 3 ◽  
pp. 521
Author(s):  
Mailendra Mailendra
Keyword(s):  
Supervised Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Integrasi data penginderaan jauh dengan sistem informasi geografis telah banyak dikembangkan, dan salah satunya dalam melihat perkembangan lahan terbangun. Tujuan penelitian ini adalah untuk melihat perkembangan lahan terbangun dan kesesuaiannya dengan Rencana Pola Ruang Kabupaten Kendal. Kemudian metode yang digunakan yaitu metode supervised classification dengan memanfaatkan data citra landsat 5 TM dan landsat 8 OLI yang selanjutnya dihitung luas dari masing lahan terbangun berdasarkan data temporal tahun 1990, tahun 2015 dan tahun 2017. Setelah diketahui luas lahan terbangun selanjutnya dioverlay dengan peta rencana pola ruang Kabupaten Kendal untuk melihat sesuai atau tidaknya penempatan lahan terbangun tersebut. Adapun hasil penelitiannya yaitu setiap tahunnya lahan terbangun terus meningkat di Kabupaten Kendal, terjadi peningkatan yang cukup signifikan dalam dua tahun terakhir yaitu tahun 2015 hingga tahun 2017. Selanjutnya diperkirakan 88 % lahan terbangun tersebut telah sesuai dengan RTRW karena sudah berada pada kawasan budidaya.

scholarly journals Deteksi Perubahan Luasan Mangrove Teluk Youtefa Kota Jayapura Menggunakan Citra Landsat Multitemporal

2018 ◽  
Vol 32 (2) ◽  
pp. 115
Author(s):  
Baigo Hamuna ◽  
Rosye H.R. Tanjung
Keyword(s):  
Satellite Imagery ◽  
Supervised Classification ◽  
Anthropogenic Factors ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
The Road ◽  
Mangrove Area ◽  
Landsat 5 Tm

Kondisi mangrove di kawasan Teluk Youtefa, baik dari aspek kualitas maupun kuantitasnya terus mengalami penurunan dari tahun ke tahun. Penelitian ini dilakukan untuk mengetahui seberapa besar perubahan luasan mangrove yang terjadi di kawasan Teluk Youtefa, Kota Jayapura dari tahun 1994 sampai tahun 2017 dengan menggunakan citra satelit Landsat 5 TM dan Landsat 8 OLI. Pengamatan kondisi mangrove di lapangan dilakukan dengan menggunakan GPS dan pengolahan citra menggunakan algoritma NDVI dengan klasifikasi supervised. Tumpang susun peta hasil interpretasi citra satelit untuk mengetahui sebaran dan perubahan luasan kawasan mangrove. Hasil penelitian menunjukan bahwa luasan mangrove pada tahun 1994 sebesar 392,45 ha dan luasan mangrove pada tahun 2017 mengalami penurunan menjadi 233,12 ha. Perubahan luasan mangrove dalam kurun waktu 23 tahun sebesar 159,34 ha atau sebesar 40,59%. Perubahan kawasan mangrove pada umumnya disebabkan oleh faktor antropogenik seperti penebangan, perubahan fungsi kawasan mangrove menjadi jalan, jembatan, pemukiman dan perubahan secara alami. ABSTRACTThe condition of mangrove in Youtefa Bay both qualitatively and quantitatively has decreased from year to year. This research was conducted to determine how much of the change occurring mangrove area in Youtefa Bay, Jayapura City from 1994 to 2017 by using Landsat 5 TM images and Landsat 8 OLI. Monitoring of mangrove condition in the field used GPS, and processing of images used NDVI algorithm with supervised classification. The map was overlaying satellite imagery interpretation to determine the distribution and changes of mangrove area. The result of research showed that the mangrove area in 1994 was about 392.45 hectares, mangrove area in 2017 have decreased becoming was 233.12 hectares. Changing of mangrove area for 23 years was about 159.34 hectares or 40.59%. Changes in mangrove were generally caused by anthropogenic factors such as logging, changes over the function of mangroves into the road, bridge, settlement, and change naturally.

scholarly journals ALTERAÇÃO NA COBERTURA FLORESTAL DA REGIÃO NOROESTE DA AMAZÔNIA MATOGROSSENSE

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 520
Author(s):  
Luani Rosa de Oliveira Piva ◽  
Rorai Pereira Martins Neto
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Forest Conversion ◽  
Landsat 8 ◽  
Amazon Forest ◽  
Landsat 8 Oli ◽  
Mato Grosso ◽  
The Past ◽  
Landsat 5 Tm

Nos últimos anos, a intensificação das atividades antrópicas modificadoras da cobertura vegetal do solo em território brasileiro vem ocorrendo em larga escala. Para fins de monitoramento das alterações da cobertura florestal, as técnicas de Sensoriamento Remoto da vegetação são ferramentas imprescindíveis, principalmente em áreas extensas e de difícil acesso, como é o caso da Amazônia brasileira. Neste sentido, objetivou-se com este trabalho identificar as mudanças no uso e cobertura do solo no período de 20 anos nos municípios de Aripuanã e Rondolândia, Noroeste do Mato Grosso, visando quantificar as áreas efetivas que sofreram alterações. Para tal, foram utilizadas técnicas de classificação digital de imagens Landsat 5 TM e Landsat 8 OLI em três diferentes datas (1995, 2005 e 2015) e, posteriormente, realizada a detecção de mudanças para o uso e cobertura do solo. A classificação digital apresentou resultados excelentes, com índice Kappa acima de 0,80 para os mapas gerados, indicando ser uma ferramenta potencial para o uso e cobertura do solo. Os resultados denotaram uma conversão de áreas florestais principalmente para atividades antrópicas agrícolas, na ordem de 472 km², o que representa uma perda de 1,3% de superfície de floresta amazônica na região de estudo.Palavras-chave: conversão de áreas florestais; uso e cobertura do solo; classificação digital; análise multitemporal. CHANGE IN FOREST COVER OF THE NORTHWEST REGION OF AMAZON IN MATO GROSSO STATE ABSTRACT: In the past few years, the intensification of anthropic activities that modify the soil-vegetation cover in Brazil’s land has been occurring on a large scale. To monitor the forest cover changes, the techniques of Remote Sensing of vegetation are essential tools, especially in large areas and with difficult access, as is the case of the Brazilian Amazon. The aim of this work was to identify the changes in land use and land cover, over the past 20 years, in the municipalities of Aripuanã and Rondolândia, Northwest of Mato Grosso State, in order to quantify the effective altered areas. Landsat 5 TM and Landsat 8 OLI digital classification images techniques were used in three different dates (1995, 2005 and 2015) and, later, the detection to the land use and land cover changes. The digital classification showed excellent results, with kappa index above 0.80 for the generated maps, indicating the digital classification as a potential tool for land use and land cover. Results reflect the conversion of forest areas mainly for agricultural activities, in the order of 472 km², representing a loss of 1.3% of Amazon forest surface in the study region.Keywords: forest conversion; land use and land cover; digital classification; multitemporal analysis.

scholarly journals NDVI e SAVI como ferramentas de monitoramento das modificações no uso e ocupação do solo no sudoeste paraense

2022 ◽  
Vol 11 (1) ◽  
pp. e47611122583
Author(s):  
Hellem Cristina Teixeira Rodrigues ◽  
Rayssa Soares da Silva ◽  
Francimary da Silva Carneiro ◽  
Charles Benedito Gemaque Souza ◽  
Tamires Borges de Oliveira ◽  
...  
Keyword(s):  
United States ◽  
De Novo ◽  
Geological Survey ◽  
United States Geological Survey ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Sensoriamento Remoto é um uma tecnologia que permite aquisição de informações sobre áreas ou objetos sem manter contato físico. Esse trabalho objetivou utilizar imagens de satélites passivos, por meio dos índices de cobertura vegetal, como o Índice de Vegetação por Diferença Normalizada (NVDI) e Índice de Vegetação Ajustado para o Solo (SAVI), nos anos de 2008 e 2018, para identificar as modificações sofridas em 10 anos da comunidade Comunidade Linha Gaúcha localizada no município de Novo progresso no estado do Pará. Para este trabalho, foram utilizados dados provenientes do IBAMA, como a localização espacial da Comunidade e imagens da plataforma United States Geological Survey (USGS), para os anos de 2008(Landsat 5 – TM) e 2018 (Landsat 8 – OLI). Por meio do método de NDVI e SAVI foi possível analisar a expansão urbana em torno da comunidade num raio de 50 km, assim como observar a intensa modificação no uso e ocupação do solo, estando este fato intimamente ligado à presença da rodovia Transamazônica, importante agente de crescimento na Amazônia.

scholarly journals Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1430
Author(s):  
V. M. Fernández-Pacheco ◽  
C. A. López-Sánchez ◽  
E. Álvarez-Álvarez ◽  
M. J. Suárez López ◽  
L. García-Expósito ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollutants ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Regional Studies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm ◽  
Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

scholarly journals ANALISIS PERUBAHAN CADANGAN KARBON DI KAWASAN GUNUNG PADANG KOTA PADANG

JURNAL BUANA ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 94
Author(s):  
Rina Suksesi ◽  
Dedi Hermon ◽  
Endah Purwaningsih
Keyword(s):  
Land Cover ◽  
Supervised Classification ◽  
Sampling Method ◽  
Forest Cover ◽  
Sampling Technique ◽  
Residential Area ◽  
Carbon Stocks ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Quantitative Descriptive

This study aims to determine (1) changes in land cover in the Mount Padang Region in 1996, 2006 and 2016, (2) changes in carbon stocks as a result of changes in land cover in the Mount Padang Region of Padang City. The type of research is quantitative descriptive. Changes in land cover isanalyzed based on Landsat TM 5 of 1996 and 2006, as well as Landsat 8 OLI of 2016, using ENVI 4.5 and ArcGIS 10.1 and supervised classification method. Value of carbon stocks is obtained from the equation C = B ×% C (0.47), by predicting biomass on each type of carbon pool using allometric equations, which D2,62 ρ B = 0.11, B = exp {-2.134 + 2.530 × ln (D)}, B = 0.281 D2,06, and B = 0.030 D2,13, where D (diameter at breast height of trees, cm) and ρ (wood density). The sampling technique used is stratified random sampling method which refers to the technique of each plot on land cover classes which are then converted to thehectares area. The results of the analysis show that (1) the land cover in the Mount Padang Region of Padang City in 1996 has forest area of 744.23 Ha (92.6%), mixed garden area of 39.44 Ha (4.9%), shrubs of 17, 92 Ha (2.2%), and the settlement area of 2.35 Ha (0.3%). 2006 forest cover an area of 696.84 Ha (87%), mixed garden area of 18.84 Ha (2%), shrubs covering 37.55 Ha (5%), and residential area of 50.71 ha (6%). 2016 forest cover an area of 533.50 Ha (66%), mixed garden covering an area of 69.14 Ha (9%),shrubs covering 119.81 Ha (15%), and residential area of 81.49 Ha (10%). (2) the carbon stock in 1996 amounted to 495,800.03 tons, in 2006 a number of 458,165.73 tons, and in 2016 a number of 369,223.00 tons. Over the last 20 years, as a result of land cover changes in carbon stocks in Padang Mountain Region has been reduced as much as 126,577.03 tons.

scholarly journals ANÁLISE DA DINÂMICA DE USO E COBERTURA DA TERRA NA REGIÃO METROPOLITANA DE NATAL NO PERÍODO DE 1984 A 2018

2021 ◽  
Vol 33 (1) ◽  
pp. 31-53
Author(s):  
Ana Paula Frazão ◽  
Venerando Eustáquio Amaro ◽  
Silvio Braz de Sousa
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Mapas de cobertura e uso da terra são importantes ferramentas de ordenamento territorial. Este estudo buscou compreender e quantificar o uso e a cobertura da terra na Região Metropolitana de Natal nos anos de 1984 e 2018, a partir de um processo de classificação supervisionada (algoritmo de Bhattacharya) de um compositie anual de imagens LANDSAT 5-TM e LANDSAT 8-OLI. Os resultados apontam que entre 1984 e 2018 (34 anos) ocorreu intensa e rápida conversão das áreas de vegetação (redução de ~1.402 km²) e sua apropriação para atividades antrópicas, predominantemente agropecuária. Também se constatou um processo de urbanização bastante expressivo, registrando uma expansão urbana de ~243%. Nossos resultados corroboram com a observada sinergia entre dados orbitais da série LANDSAT e Rapideye e colabora para o entendimento que a metropolização de Natal foi institucional.

scholarly journals Dynamique De L’occupation Du Sol Dans Les Zones Humides De La Commune D’allada Au Sud-Benin (Sites Ramsar 1017 Et 1018)

2018 ◽  
Vol 14 (12) ◽  
pp. 59
Author(s):  
L. Estelle Brun ◽  
Djego J. Gaudence ◽  
Moussa Gibigaye ◽  
Brice Tente
Keyword(s):  
Sustainable Management ◽  
Supervised Classification ◽  
Landsat 8 ◽  
Maximum Likelihood Algorithm ◽  
Landsat 8 Oli ◽  
Human Pressure ◽  
Integral Element ◽  
Benin Republic ◽  
Anthropic Pressure

The wetlands are the integral element of the natural resource of Benin Republic. However, anthropic pressure on those “fragil” environments, contribute to the reducing of their surface and accordingly, to a loss their biodiversity. The target objective is to make cartography of land units from 1990 to 2014 in order to identify the various pressures upon the wet ecosystems. A 2014 Landsat 8 OLI-TIRS image and a 1990 map of Benin land cover were used to establish the cartography. We used the Maximum likelihood algorithm to execute the supervised classification of the landsat image in ERDAS. The mapping of the land’s units in the wetlands was then carried out in ArcGIS. The results revealed that the tree savana have completely disappeared. It represents 11.47 % of the landscape in 1990 against 0 % in 2014. The mosaics of fields and fallows under palm plantations have reduced to -30.42 % in 2014. They represent 66.63 % of the landscape. The land units which progressed are the mosaic of fields and fallow (12.06 %), the swamps (10.47 %), the plantations (5.26 %) and the agglomerations (2.71 %). This shows strong human pressure exerted on the natural vegetation of the wetlands in the Allada district. These results will provide the local authorities with a tool for decision support, for an efficient use and a sustainable management of these natural wet ecosystems.

scholarly journals Utilização da ferramenta lcm do idrisi /taiga para análise de perdas e ganhos no uso da terra na zona de amortecimento da estação ecológica de Assis (SP)

2021 ◽  
Vol 21 (2) ◽  
pp. 249-263
Author(s):  
Layla Cristina de Freitas Assalve ◽  
Daniela Fernanda Da Silva Fuzzo
Keyword(s):  
Landsat 8 ◽  
Land Change ◽  
Landsat 8 Oli ◽  
Land Change Modeler ◽  
Landsat 5 Tm

Ao longo do tempo foram criados diversos tipos de manejos adequados para cada Unidade de Conservação (UCs), dentre eles, a Zona de Amortecimento (ZA) surge com o papel de filtrar os impactos negativos das atividades que ocorrem ao seu redor. Desta forma, o objetivo deste trabalho foi analisar as modificações do uso da terra na ZA da Estação Ecológica de Assis – SP, nos anos 2000 e 2017. Para a realização do trabalho foram utilizadas imagens dos satélites Landsat 5/TM e Landsat 8/OLI, as imagens foram processadas em ambiente SIG e geradas os mapeamentos de perdas e ganhos utilizando o módulo Land Change Modeler (LCM) do software Idrisi-Taiga, o qual apresenta grande importância para a realização de estudos ambientais. Foi possível observar, a redução da pastagem e o aumento das culturas temporárias na porção norte e leste da área, nesses dezessete anos analisados, obteve um ganho de área de 12,29 %. Destaca-se que o entorno se encontra tomado integralmente por cultura temporária e silvicultura, que afetam a conservação, podendo gerar a perda da biodiversidade e o desmatamento neste local, sendo assim o conhecimento dos parâmetros analisados permite a utilização de técnicas adequadas para a preservação efetiva. Portanto, a utilização de dados orbitais pode contribuir ao monitoramento das UCs evitando a degradação ambiental.

scholarly journals CENÁRIOS AMBIENTAIS E O INCENTIVO À PROTEÇÃO DE ÁREAS DE PRESERVAÇÃO PERMANENTE NA MICROBACIA DO RIO FAGUNDES, PARAÍBA DO SUL/RJ

Geo UERJ ◽  
2020 ◽  
Author(s):  
Cintia de Andrade Corrêa ◽  
Leandro Andrei Beser de Deus ◽  
Nadja Maria Castilho da Costa
Keyword(s):  
Rio De Janeiro ◽  
Landsat 8 ◽  
Land Change ◽  
Landsat 8 Oli ◽  
Land Change Modeler ◽  
Landsat 5 Tm

Para incentivar os agricultores a preservar e/ou restaurar a vegetação nativa como previsto na Lei 12.651/2012, um programa de desenvolvimento rural sustentável do Rio de Janeiro investe na proteção e recuperação de Áreas de Preservação Permanente, especialmente as de nascente. O objetivo deste trabalho, relacionado a este programa, é construir cenários ambientais que subsidiem o planejamento e a gestão de APPs. A metodologia é a quantificação das classes de cobertura da terra: "Floresta", "Não Floresta", "Água" e "Não Classificada", usando geotecnologias em duas cenas comparativas, 2011 e 2016, e um cenário futuro, 2046. Foram utilizados o programa ArcGis 10.3 para processamento de imagem digital, os sensores Landsat 5 TM e Landsat 8 OLI e o programa Idrisi Selva 17.0/Land Change Modeler para a geração de modelos preditivos e análise comparativa. Os resultados indicam que, entre 2011 e 2016, houve um aumento de 160,4 hectares da classe “Não Floresta” em relação à “Floresta”: a classe “Floresta” diminuiu 7,4%, enquanto a classe “Não Floresta” aumentou 5,33%. Para o cenário futuro 2046, o modelo probabilístico markoviano apresenta 52% de chance de conversão da classe "Floresta" em "Não Floresta", portanto, há uma predição de cenário futuro tendencial pessimista de desmatamento. Recomenda-se o monitoramento da microbacia conjugado a mudanças na cobertura da terra e a ações de preservação de "Floresta”.

scholarly journals URBANIZATION AND IT IMPACTS TO LAND SURFACE TEMPERATURE ON SMALL MEDIUM SIZE CITY FOR YEAR 1991, 2011 AND 2018: CASE STUDY KOTA KINABALU

2020 ◽  
Vol 6 (1) ◽  
pp. 58-76
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hot Spot ◽  
Medium Size ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Urban Heat ◽  
Landsat 5 Tm

Transformation of land cover vegetation toward urban areas causes the temperature at urban higher to compare to suburban and rural areas, namely urban heat island (UHI) effect. The UHI has a negative impact, such a stroke heat, air pollution, green gasses emission, and electric consumption. UHI studies at a tropical country still limited due to the containment of cloud cover. Besides that, studies only focus on big cities which have residents above than 2 million. The outcome this studied important to enhance our knowledge of urban heat effect at small-medium cities and guidelines to policymaker and urban planner to discover there has effectively taken to decrease the effect of urban heat at the hot spot area. The main goal of this research about to discovered influence of urban growth and selected urban index, namely the Normalized Difference Built Index (NDBI) to LST. NDBI is an index which denotes intensity of urban built up. In the first step, we generate the LST and NDBI from Landsat 8 OLI at year 2018 and Landsat 5 TM for the year 2011 and 1991. Second, we applied the unsupervised classification of Landsat 8 OLI and Landsat 5 TM to generate the land cover maps for the years 1991, 2011, and 2018. Third of our method to examine the relationship between Land surface temperature (LST) and NDBI.  The higher value NDBI is a hot spot, and the low value is a cold spot. In the last step, we applied for Change Detection analysis using GIS to examine the land cover change between 1991 and 2018.  Our results show the higher the value of NDBI and LST at the centre of the city and the lowest value at vegetation land cover. The transformation of land cover vegetation to urban increase at countryside area and out-of-town and significantly increase of distribution of UHI. On another hand, the shows positive relationships between LST and NDBI. The output of the study provides a guideline for policymakers and town designers to develop to toward city zero carbon, sustainable and health.

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