Estimation of salt pond area in Madura based on satellite imagery

Abstract Salt is one of the essential commodities in Madura. Still, this commodity is often a problem related to the volume of production that cannot be determined with certainty. Sometimes, the estimation and actual production in the field is much different. The satellite image is a picture of an area photographed by satellite remote sensing of an area according to conditions in the field. Satellite imagery can be used to estimate the area of production of a commodity at a specific location. This study aimed to estimate the total area of salt pond in the Madura Island, specifically Sampang district, using a Landsat 8 satellite image. The method used spectral analysis that extracts multispectral data Landsat 8 to result from different areas. Field observations were conducted to validate the area. The results show that the accuracy of satellite image interpretation of salt ponds and non-salt ponds was 67.5%. Based on the result, it is possible to estimate salt pond area production in the Sampang district using Landsat 8. However, classification results must be improved by using other classification methods.

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Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

Journal of Science Natural Science ◽

10.18173/2354-1059.2021-0021 ◽

2021 ◽

Vol 66 (1) ◽

pp. 175-187

Author(s):

Duong Phung Thai ◽

Son Ton

Keyword(s):

Satellite Images ◽

Satellite Image ◽

Image Interpretation ◽

Image Data ◽

Vegetation Coverage ◽

Landsat 8 ◽

Mangrove Forests ◽

Forest Change ◽

Shrimp Ponds ◽

Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

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

Jurnal Jartel Jurnal Jaringan Telekomunikasi ◽

10.33795/jartel.v9i2.175 ◽

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.

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Change in tropical forest cover of Southeast Asia from 1990 to 2010

Biogeosciences Discussions ◽

10.5194/bgd-10-12625-2013 ◽

2013 ◽

Vol 10 (8) ◽

pp. 12625-12653 ◽

Cited By ~ 7

Author(s):

H.-J. Stibig ◽

F. Achard ◽

S. Carboni ◽

R. Raši ◽

J. Miettinen

Keyword(s):

Southeast Asia ◽

Satellite Imagery ◽

Forest Cover ◽

Satellite Image ◽

Image Interpretation ◽

Selective Logging ◽

Automated Classification ◽

Forest Cover Change ◽

Forest Change ◽

The One

Abstract. The study assesses the extent and trends of forest cover in Southeast Asia for the period 1990–2000–2010 and provides an overview on the main drivers of forest cover change. A systematic sample of 418 sites (10 km × 10 km size) located at the one-degree geographical confluence points and covered with satellite imagery of 30 m resolution is used for the assessment. Techniques of image segmentation and automated classification are combined with visual satellite image interpretation and quality control, involving forestry experts from Southeast Asian countries. The accuracy of our results is assessed through an independent consistency assessment, performed from a subsample of 1572 mapping units and resulting in an overall agreement of > 85% for the general differentiation of forest cover vs. non-forest cover. The total forest cover of Southeast Asia is estimated at 268 Mha in 1990, dropping to 236 Mha in 2010, with annual change rates of 1.75 Mha (~0.67% and 1.45 Mha (~0.59%) for the periods 1990–2000 and 2000–2010, respectively. The vast majority of forest cover loss (~2/3 for 2000–2010) occurred in insular Southeast Asia. Combining the change patterns visible from satellite imagery with the output of an expert consultation on the main drivers of forest change highlights the high pressure on the region's remaining forests. The conversion of forest cover to cash crop plantations (e.g. oil palm) is ranked as the dominant driver of forest change in Southeast Asia, followed by selective logging and the establishment of tree plantations.

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Kajian Metode Klasifikasi Citra Landsat-8 untuk Pemetaan Habitat Bentik di Kepulauan Padaido, Papua

Oseanologi dan Limnologi di Indonesia ◽

10.14203/oldi.2017.v2i1.69 ◽

2017 ◽

Vol 2 (1) ◽

pp. 1 ◽

Cited By ~ 2

Author(s):

Muhammad Hafizt ◽

Marindah Yulia Iswari ◽

Bayu Prayudha

Keyword(s):

Coral Reefs ◽

Satellite Remote Sensing ◽

Satellite Images ◽

Habitat Mapping ◽

Benthic Habitat ◽

Landsat 8 ◽

Classification Methods ◽

Landsat 8 Oli ◽

Remote Sensing Technique ◽

Object Based

Assessment of Landsat-8 Classification Method for Benthic Habitat Mapping in Padaido Islands, Papua. Indonesia is the biggest archipelagic country in the world with an area of coral reefs of 39,583 km.This area has to be managed effectively and efficiently utilizing satellite remote sensing technique capable of mapping of benthic habitat coverage, such as coral reefs, seagrasses, macroalgae, and bare substrates. The technique is supported by the availability of Landsat-8 OLI satellite images that have been recording the regions of Indonesia continuously every 16 days. This research was carried out in June 2015 in parts of Padaido Islands, Papua. This area was selected due to high coral reef damages. This study utilized Landsat-8 OLI to compare two classification methods, namely pixel based and object based methods using ‘maximum 2 likelihood’ (ML) and ‘example based feature extraction’ classifications, respectively, after water column correction (Lyzenga method). The results showed that both methods produced benthic habitat maps with 7 class covers. The pixel-based classification resulted in a better overall accuracy (47.57%) in the mapping of benthic habitats than object-based classification approach (36.17%). Thus, the ML classification is applicable for benthic habitat mapping in Padaido Islands. However, the consistency of this method must be analyzed in many diffrent locations of Indonesian waters.

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Change in tropical forest cover of Southeast Asia from 1990 to 2010

Biogeosciences ◽

10.5194/bg-11-247-2014 ◽

2014 ◽

Vol 11 (2) ◽

pp. 247-258 ◽

Cited By ~ 106

Author(s):

H.-J. Stibig ◽

F. Achard ◽

S. Carboni ◽

R. Raši ◽

J. Miettinen

Keyword(s):

Southeast Asia ◽

Satellite Imagery ◽

Forest Cover ◽

Satellite Image ◽

Image Interpretation ◽

Important Change ◽

Automated Classification ◽

Natural Forests ◽

Forest Cover Change ◽

Forest Change

Abstract. The study assesses the extent and trends of forest cover in Southeast Asia for the periods 1990–2000 and 2000–2010 and provides an overview on the main causes of forest cover change. A systematic sample of 418 sites (10 km × 10 km size) located at the one-degree geographical confluence points and covered with satellite imagery of 30 m resolution is used for the assessment. Techniques of image segmentation and automated classification are combined with visual satellite image interpretation and quality control, involving forestry experts from Southeast Asian countries. The accuracy of our results is assessed through an independent consistency assessment, performed from a subsample of 1572 mapping units and resulting in an overall agreement of >85% for the general differentiation of forest cover versus non-forest cover. The total forest cover of Southeast Asia is estimated at 268 Mha in 1990, dropping to 236 Mha in 2010, with annual change rates of 1.75 Mha (∼0.67%) and 1.45 Mha (∼0.59%) for the periods 1990–2000 and 2000–2010, respectively. The vast majority of forest cover loss (∼2 / 3 for 2000–2010) occurred in insular Southeast Asia. Complementing our quantitative results by indicative information on patterns and on processes of forest change, obtained from the screening of satellite imagery and through expert consultation, respectively, confirms the conversion of forest to cash crops plantations (including oil palm) as the main cause of forest loss in Southeast Asia. Logging and the replacement of natural forests by forest plantations are two further important change processes in the region.

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PERUBAHAN TUTUPAN LAHAN HUTAN LINDUNG GUNUNG GUNTUR TAHUN 2014 SAMPAI DENGAN TAHUN 2017

Wanamukti: Jurnal Penelitian Kehutanan ◽

10.35138/wanamukti.v21i1.153 ◽

2020 ◽

Vol 21 (1) ◽

pp. 30

Author(s):

Prasetyo Widodo ◽

Abdul Japar Sidik

Keyword(s):

Land Use ◽

Land Cover ◽

Satellite Image ◽

Image Interpretation ◽

Landsat 8 ◽

Land Protection ◽

Community Activities ◽

Protected Forests ◽

Open Land

High pressure by community activities on the existence of forests, especially protected forests that affect the quality of the environment that can cause a disaster, such as the occurrence of flash floods that occurred in 2016 in Garut regency, cannot be separated from damage to the upstream cover of cimanuk-citanduy. This prompted investigators to analyze the three year change of land protection prevailing in Mt. Guntur RPH Simpang BKPH Bayongbong. The objective of research is to calculate how large changes land cover area in Mt. Guntur Protected Area (MGPA), RPH Simpang BKPH Bayongbong KPH Garut in three years. The data collected on July to August 2017 by geographic information system (GIS) and satellite image. The results of land cover interpretation by landsat 8 OLI image 2014 and 2017 describe the condition of land use and land cover change in MGPA. Land cover of MGPA dominated by shrub (B) is 287.58 Ha (57.52%) at 2014 and 202.89 Ha (40.58%) at 2017, so deforestation as three years is 31.24 Ha or 32.13%. The results of ground check there is a land use change to open land and farming dryland. According to data of image interpretation at 2017, the open land is 20.03 Ha but after ground checking is 20.51 Ha. The reduction of it based on data of image interpretation at 2017 is 200.33 Ha to 201.85 Ha after ground checking.

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TELEDETECCIÓN APLICADA AL RECONOCIMIENTO DE PRADERAS DE PASTOS MARINOS EN AMBIENTES DE BAJA VISIBILIDAD: LA GUAJIRA, COLOMBIA

Bulletin of Marine and Coastal Research ◽

10.25268/bimc.invemar.2016.45.2.687 ◽

2016 ◽

Vol 45 (2) ◽

Cited By ~ 1

Author(s):

Santiago Millán ◽

Jiner Antonio Bolaños ◽

Carolina García Valencia ◽

Diana Isabel Gómez López

Keyword(s):

Spatial Scales ◽

Satellite Image ◽

Image Interpretation ◽

High Water ◽

Landsat 8 ◽

Visual Interpretation ◽

Ecological Value ◽

High Productivity ◽

Seagrass Meadows ◽

Definition Of

Seagrass meadows are important ecosystems due to their high productivity and ecological value among tropical ecosystems, because of their high species diversity. In Colombia seagrasses are located around some islands, oceanic coral banks and along the Caribbean shelf, mainly in La Guajira Department, where more than 80% of the seagrass meadows of the country are present. In the world, the delimitation of this ecosystem has been successfully mapped during years, with assistance of remote sensing, using satellite image of different spatial scales. Nevertheless, the specific environmental conditions in La Guajira, such as high water turbidity and reduced light penetration restrict the use of traditional satellite images employed for those seascapes. With the aim of delimiting and establishing the extension of seagrass meadows in La Guajira, based on analyses between July 2013 and February 2014, a methodology of massive image interpretation that included fieldwork fast verification was applied, generating as a result one layer of seagrass habitats in Cabo de La Vela – Dibulla area at 1:100000 scale. Methodology included geometric correction, image fusion, fieldwork information, definition of thematic classes, determining of criteria for spatial delimitation, visual interpretation of images, thematic uncertainty qualification, and final cartography production. The process of cartographic production showed that Landsat 8 OLI satellite sensor images made easier the identification of seagrass meadows in deep areas (>10m). In total, 53621 ha of seagrass meadows were identified, and the largest meadows of Colombia were delimitated, which reach dimensions of up to 6018 ha.

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INTEGRASI DATA INDERAJA DAN DATA GEOLOGI UNTUK MENDUKUNG EKSPLORASI TAMBANG EMAS, Studi Kasus di Kabupaten Paniai Provinsi Papua

MAJALAH ILMIAH GLOBE ◽

10.24895/mig.2017.19-1.559 ◽

2017 ◽

Vol 19 (1) ◽

pp. 75 ◽

Cited By ~ 1

Author(s):

Iksal Yanuarsyah ◽

Yatin Suwarno

Keyword(s):

Remote Sensing ◽

Satellite Imagery ◽

Spatial Data ◽

Satellite Image ◽

Image Interpretation ◽

Geological Mapping ◽

Alteration Zone ◽

Mineralized Zone ◽

Landsat Satellite ◽

Mining Exploration

ABSTRAKPemetaan potensi sumberdaya geologi pertambangan khususnya potensi mineral perlu dilakukan sebagai awal dalam pengelolaan sumberdaya pertambangan terlebih dalam tahapan eksplorasi pendahuluan. Penginderaan jauh (Inderaja) merupakan alat bantu yang merekam rona lingkungan bumi yang mampu menginterpretasi potensi eksplorasi mineral logam seperti emas. Dengan menggunakan data citra satelit, biaya eksplorasi akan lebih rendah, termasuk efisiensi dalam melakukan pemboran. Tujuan dari studi ini yaitu mampu mendeliniasi Jalur Alterasi dengan interpretasi citra satelit agar untuk mendukung kegiatan eksplorasi tambang lebih efektif dan efisien. Lokasi kajian berada di Distrik Bogobaida, Kabupaten Paniai, Propinsi Papua seluas 40.116 Ha yang merupakan lokasi Izin Usaha Pertambangan (IUP) Eksplorasi PT. Kotabara Mitratama (izin berdasarkan Keputusan Bupati Paniai No. 017 Tahun 2010). Metode yang digunakan dalam kajian ini yaitu metode konseptual dengan memanfaatkan faktor geologi yang berpengaruh pada terbentuknya endapan minera). Tahapan analisa dimulai dari pengumpulan data spasial (peta) dan non spasial (tabular), analisa interpretasi citra Landsat dan identifikasi kelurusan zona lemah (lineament) untuk menentukan zona mineralisasi. Berdasarkan hasil interpretasi citra Landsat dengan didukung analisa geologi untuk daerah IUP PT. Kotabara Mitratama berprospek Tembaga (Cu) dan Emas (Au) yang terbagi dalam 9 Zona Mineralisasi dengan luas mencapai 2.922,48 Ha (yang terdiri dari 8 zona mineralisasi primer seluas 2.208,83 Ha dan 1 zona mineralisasi aluvial seluas 713,65 Ha). Kata kunci: data inderaja, data geologi, eksplorasi emas ABSTRACT Geological mapping of the mineral potential has to be done as the preliminary stages of mining exploration. Remote sensing is a common tool that used to records the earth's environment through image interpretation such for gold mine potential exploration. By using satellite imagery data, will be lower exploration costs, including efficiency in drilling The aim of this study is to delineate alteration zone with satellite image interpretation to support mining exploration activities more effectively and efficiently. The study Located in Bogobaida District, Paniai Regency, Papua Province, covering an area of 40 116 hectares, in site case of Legal Mining Exploration Permit (IUP) PT. Kotabara Mitratama (Paniai Regent Decree No. 017 of 2010). The method used is utilizing conceptual geological factors that alleged the formation of mineral deposits. Stages of analysis starting from spatial data (maps) and non-spatial (tabular) collection, then Landsat satellite imagery interpretation and identification of weak zones straightness (lineament) due to define the mineralized zones. Based on the results of image interpretation with geological analysis in IUP PT. Kotabara Mitratama was prospected Copper (Cu) and gold (Au) which is divided into 9 Mineralization Zone with an area of 2,922.48 ha (consisting of 8 primary mineralized zone covering an area of 2,208.83 ha and 1 alluvial mineralized zone measuring 713.65 ha). Keywords: Remote sensing, geological data, gold exploration

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ASSESSMENT OF MANGROVE ECOSYSTEM POTENCY AND ITS UTILIZATION IN DOMPU REGENCY COASTAL WATERS, WEST NUSA TENGGARA PROVINCE USING SATELLITE IMAGERY APPLICATION

Jurnal Segara ◽

10.15578/segara.v15i2.7461 ◽

2019 ◽

Vol 15 (2) ◽

Author(s):

Yulius Yulius ◽

Syahrial Nur Amri ◽

August Daulat ◽

Sari Indriani Putri

Keyword(s):

Satellite Imagery ◽

Coastal Waters ◽

Mangrove Forest ◽

Satellite Image ◽

Mangrove Ecosystem ◽

Coastal Vegetation ◽

Coastal Protection ◽

Landsat 8 ◽

Mangrove Forests ◽

Mangrove Species

Mangrove forests are tropical coastal vegetation communities, which has the ability to grow in coastal area with tidal and muddy environment. Several functions of mangrove forest such as ecological functions can be used for coastal protection, trapping sediment and strengthen the coastal ecosystems. Coastal waters in Dompu Regency, West Nusa Tenggara have natural mangrove ecosystem with a huge potency and advantages to the region. This study aimed to understand the condition of mangrove ecosystem based on satellite image analysis of Landsat 8 Operational Land Imager (OLI) in 2014 and assess the potency, information related to the utilization by community. Data collection in this study were combined from satellite imagery interpretation with interview and questionnaires. The results showed that the mangrove forest extent in Dompu Regency Coastal Waters were about 90,631 ha with uniformity index 0.68 (medium uniformity). Two mangrove species were found in the region namely Rhizopora stylosa and Rhizopora apiculata and used by the community for several purposes such as firewood, natural coastal protection from tidal, waves and abrasion, also for crabs and fish spawning ground.

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THE USAGE OF RUSBOOST BOOSTING METHOD FOR CLASSIFICATION OF IMPERVIOUS SURFACES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-981-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 981-985 ◽

Cited By ~ 2

Author(s):

M. H. Kesikoglu ◽

U. H. Atasever ◽

C. Ozkan ◽

E. Besdok

Keyword(s):

Decision Tree ◽

Classification Tree ◽

Satellite Image ◽

Impervious Surface ◽

Landsat 8 ◽

Impervious Surfaces ◽

Classification Methods ◽

Surface Areas ◽

Tree Algorithms ◽

Boosting Method

Impervious surface areas are artificial structures covered by materials such as asphalt, stone, brick, rooftops and concrete. Buildings, parking lots, roads, driveways and sidewalks are shown as impervious surfaces. They increase depending on the population growth. The spatial development of impervious surface expansion is necessary for better understanding of the urbanization status and its effect on environment. There are different impervious surface determining approaches met in literature. In this paper, it is aimed to extract the impervious surface areas of Kayseri city, Turkey by using remote sensing techniques. It is possible to group these techniques under a few main topics as V-I-S (vegetation-impervious surface-soil) model, based on spectral mixture analysis or decision tree algorithms or impervious surface indices. According to these techniques, we proposed a new technique by using RUSBoost algorithm based on decision tree in this study. In this scope, Landsat 8 LDCM image belonging to July, 2013 was used. Determining of impervious surface areas accurately depends on accuracy of image classification methods. Therefore, satellite image was classified separately by using Classification Tree and RUSBoost boosting method which increases accuracy of the classification method based on decision tree. Classification accuracies of these supervised classification methods were compared and it was observed that the best overall accuracy was obtained with RUSBoost method. For this reason, RUSBoost method was preferred to determine impervious surface areas. The overall accuracies were obtained 95&thinsp;% with Classification Tree and 97&thinsp;% with RUSBoost boosting method.

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