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.

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 MAPPING OF MANGROVE VEGETATION USING LANDSAT SATELLITE IMAGERYIN THE ESTUARY OF GANGSAL RIVER INDRAGIRI HILIR REGENCY RIAU PROVINCE

2020 ◽  
Vol 2 (3) ◽  
pp. 181-189
Author(s):  
Hendri Susilo ◽  
Musrifin Ghalib ◽  
Aras Mulyadi
Keyword(s):  
Community Structure ◽  
Satellite Imagery ◽  
Landsat 8 ◽  
Sampling Point ◽  
Landsat 8 Oli ◽  
Transect Line ◽  
Solid Density ◽  
Mangrove Vegetation ◽  
Landsat 5 Tm ◽  
Landsat Satellite

The research was conducted in January - March 2019. This study aims to map and analyze changes in the area and density of mangrove vegetation based on NDVI values and community structure in the Muara Sungai Gangsal, Indragiri Hilir Regency. To analyze the area and density of NDVI using Landsat 5 TM satellite imagery in 2008 and Landsat 8 OLI/TIRS in 2018. Analysis using ArcGis 10.3 software. The calculation of mangroves based on community structure used the Transect Line Plot method at 6 stations for community structure sampling. The area of mangrove vegetation in 2008 was 2,706 ha and in 2018 it was 2,693 ha. The results of the analysis of mangrove vegetation area from 2008 to 2018 there was a reduction of 13 ha. The NDVI value for 2008 criteria is rarely 133 ha, while 2.009 ha are wide and 564 ha is dense. The NDVI value of the 2018 mangrove vegetation is rarely 16 hectares, while 2,135 hectares are in the area and 542 hectares are dense. Based on the analysis of mangrove density in 2018 at 6 sampling point stations ranging from 866 ind/ha to 1,522 ind/ha. Density criteria are rarely detected at station I with a density of 922 ind/ha and station II with a density of 866 ind/ha. The criterion of moderate density was detected at station V with a density of 1,255 ind/ha and station VI with a density of 1,044 ind/ha. Criteria for solid density were detected at station III with a density of 1,522 ind/ha and station IV with a density of 1,511 ind/ha.

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 Analysis of Changes in Area and Density of Mangroves through Landsat 8 Satellite Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 16-22
Author(s):  
Nadya Fiqi Nurcahyani
Keyword(s):  
Image Processing ◽  
Satellite Imagery ◽  
Good Accuracy ◽  
Social Values ◽  
Satellite Image ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Android Application ◽  
Mangrove Area ◽  
Satellite Image Processing

Mangrove forests have high ecological, economic and social values ??which function to maintain shoreline stability, protect beaches and riverbanks, filter and remediate waste, and to withstand floods and waves. The facts show that mangrove damage is everywhere, even the intensity of damage and its area tends to increase significantly. Many roles of mangroves require proper management to maintain the existence of mangroves. One way to determine the area of ??mangroves is by processing Landsat 8 satellite imagery. The stages of mangrove identification are carried out by using 564 RGB band merger, then separating the mangrove and non-mangrove objects. Next step is to analyze the density of mangroves using NDVI formula. To maximize monitoring of mangrove area, an android application was created that provides information on the area and density of mangroves at several locations, namely Clungup, Bangsong Teluk Asmara and Cengkrong from 2015 to 2018.The results showed that Landsat 8 satellite imagery can be used to identify changes in the area of ??mangrove forests with good accuracy, namely in the Clungup area of ??90% and Cengkrong of 86.67%. From processing results, the mangrove area in the Clungup area has also decreased from 2015 to 2017 but has increased in 2018 so that the application provides recommendations for embroidering mangroves in 2016 to 2017 and mangrove recommendations are maintained in 2018. As for Bangsong Teluk area Asmara and Cengkrong have increased the area of ??mangroves every year so that the application provides recommendations to be maintained from 2016 to 2018.

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.

Sign in / Sign up

Export Citation Format

Share Document