scholarly journals Valuasi Dampak Banjir Di Kabupaten Landak, Kalimantan Barat

2021 ◽  
Vol 20 (1) ◽  
pp. 65-75
Author(s):  
Gusti Rachmad Rabsanjani ◽  
Aji Ali Akbar ◽  
Henny Herawati
Keyword(s):  
Land Cover ◽  
Rainy Season ◽  
Economic Valuation ◽  
Economic Losses ◽  
Land Cover Class ◽  
Attribute Analysis ◽  
Flood Disasters ◽  
Open Land ◽  
Cover Class ◽  
Slope Class

Banjir merupakan becana yang kerap sekali terjadi pada musim hujan, banjir dapat menyebabkan kehilangan harta benda maupun korban jiwa. Valuasi ekonomi akibat terjadinya banjir adalah salah satu cara yang dapat digunakan untuk menghitung kerugian akibat terjadinya bencana banjir. Tidak adanya kajian mengenai kerentanan dan kerugian ekonomi akibat banjir pada tiga desa di Kecamatan Ngabang yaitu Desa Raja, Hilir Tengah dan Hilir Kantor adalah alasan dilakukannya penelitian ini. Tujuan dilakakukan penelitian ini adalah untuk mengidentifikasi dan menginventarisasi besarnya tingkat kerentanan terhadap banjir yang terjadi dan menghitung valuasi kerugian ekonomi akibat terjadinya bencana banjir. Metode yang digunakan dalam menganalisis kerentanan banjir menggunakan software ArcMap 10.3 adalah Analisa atribut meliputi pemberian skor kelas curah hujan, pemberian skor kelas tutupan lahan, pemberian skor kelas kemiringan lahan, pembobotan dan Analisa AHP. Metode yang digunakan untuk menghitung estimasi kerugian akibat banjir menggunakan metode ECLAC. Hasil yang didapat dalam penelitian ini adalah perubahan tutupan lahan mengalami penurunan dan peningkatan luasan permukiman, pertanian/sawah, dan lahan terbuka/semak, Curah hujan yang tinggi dan kelerengan daerah yang landai menjadi parameter penyebab terjadinya banjir. Pada estimasi nilai kerugian akibat banjir dengan nilai kerugian menggunakan USD dan Emas pada tahun yang ditentukan dengan hasil total kerugian pada tahun 1973 adalah 73,7 Juta dollar, tahun 1989 180 juta dollar, tahun 1994 261 juta dollar, tahun 2000 261juta dollar, tahun 2010 1,1 miliar dollar, dan tahun 2020 1,9 miliar dollar.ABSTRACTFlood is a plan that often occurs in the rainy season, floods can cause loss of property and fatalities. Economic valuation due to flooding is one way that can be used to calculate losses due to flood disasters. The absence of studies on vulnerability and economic losses due to flooding in three villages in Ngabang Subdistrict namely Desa Raja, Hilir Tengah and Hilir Kantor is the reason for this research. The purpose of this study is to identify and inventory the level of vulnerability to floods that occur and calculate the valuation of economic losses due to flood disasters. The methods used in analyzing flood vulnerabilities using ArcMap 10.3 software are attribute analysis including rainfall class scoring, giving land cover class scores, awarding land slope class scores, weighting and AHP Analysis. The method used to calculate the estimated loss due to flooding uses the ECLAC method. The results obtained in this study are changes in land cover experiencing a decrease and increase in the area of settlements, agriculture / rice fields, and open land / bush, high rainfall and marbles of sloping areas become parameters of the cause of flooding. In the estimated value of losses due to floods with the value of losses using USD and Gold in the specified year with the total loss in 1973 was 73.7 million dollars, in 1989 180 million dollars, in 1994 261 million dollars, in 2000 261 million dollars, in 2010 1.1 billion dollars, and in 2020 1.9 billion dollars.

scholarly journals The Aerosol Index and Land Cover Class Based Atmospheric Correction Aerosol Optical Depth Time Series 1982–2014 for the SMAC Algorithm

2017 ◽  
Vol 9 (11) ◽  
pp. 1095 ◽  
Author(s):  
Emmihenna Jääskeläinen ◽  
Terhikki Manninen ◽  
Johanna Tamminen ◽  
Marko Laine
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Correction ◽  
Land Cover Class ◽  
Cover Class ◽  
Aerosol Index

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

scholarly journals Analisis Perubahan Penutupan Lahan dan Potensi Karbon di Taman Hutan Raya Pocut Meurah Intan, Aceh Indonesia

2021 ◽  
Vol 19 (2) ◽  
pp. 450-458
Author(s):  
Rahmat Fadhli ◽  
Sugianto Sugianto ◽  
Syakur Syakur
Keyword(s):  
Land Cover ◽  
Carbon Potential ◽  
Land Use Land Cover ◽  
Plantation Forest ◽  
Dry Land ◽  
Forest Park ◽  
Carbon Absorption ◽  
Open Land ◽  
Cover Class ◽  
Object Area

Perubahan penutupan lahan merupakan sektor penyumbang emisi gas rumah kaca terbesar, termasuk di dalamnya adalah pemanfaatan lahan. Analisis tutupan lahan menjadi bagian penting dalam menentukan jumlah potensi karbon yang tersedia. Penelitian bertujuan untuk menganalisis perubahan tutupan lahan dari tahun 2003 hingga 2018 dan menghitung potensi karbon di Taman Hutan Raya Pocut Meurah Intan dengan luas objek penelitian 6.215 ha. Penelitian dilaksanakan selama 5 (lima) bulan. Penelitian ini menggunakan metode stock difference, yaitu metode perhitungan luas tutupan lahan dan stok karbon pada dua titik waktu. Hasil penelitian menunjukkan bahwa perubahan luas tertinggi tahun 2018 seluas 263 ha dan terendah tahun 2009 seluas 108 ha. Lahan terbuka meningkat seluas 100 ha, pemukiman 81 ha, semak belukar 65 ha, pertanian lahan kering campur semak 32 ha. Sementara hutan lahan kering sekunder menurun 79 ha, hutan tanaman 76 ha, savanna 21 ha dan pertanian lahan kering 103 ha. Selama kurun waktu 15 tahun berdasarkan kelas penutupan lahan, cadangan karbon tertinggi pada tahun 2003 sebesar 656.053 ton, terendah tahun 2012 sebesar 620.992 ton. Laju serapan karbon tertinggi pada periode tahun 2015-2018 sebesar 94.615 ton CO2 dan terendah pada periode tahun 2009-2012 sebesar 1.981 ton CO2. Laju emisi tertinggi pada periode tahun 2003-2006 sebesar 79.559 ton CO2 dan terendah periode tahun 2006-2009 sebesar 9.069 ton CO2. Peningkatan serapan karbon diakibatkan oleh meningkatnya luas tutupan lahan pada hutan lahan kering sekunder dan adanya pemanfaatan lahan untuk pertanian lahan kering campur semak.ABSTRACTChanges in land cover are the largest contributor to greenhouse gas emissions, including land use. Land cover analysis is an important part in determining the potential amount of carbon available. The study aims to analyze changes in land cover from 2003 to 2018 and calculating the carbon potential in the Pocut Meurah Intan Forest Park with a research object area of 6,215 ha. The research was conducted for 5 (five) months. This research uses the stock difference method, namely the method of calculating land cover area dan stok karbon pada dua titik waktu. The results showed that the highest area change in 2018 was 263 ha and the lowest was in 2009 at 108 ha. Open land increased by 100 ha, settlement 81 ha, scrub 65 ha, dry land agriculture mixed with shrubs 32 ha. Meanwhile, secondary dry land forest decreased by 79 ha, plantation forest 76 ha, savanna 21 ha and dry land agriculture 103 ha. Over a 15 year period based on land cover class, the highest carbon stock in 2003 was 656,053 tons, the lowest was in 2012 at 620,992 tons. The highest carbon absorption rate in the 2015-2018 period was 94,615 tons of CO2 and the lowest was in the 2009-2012 period of 1,981 tons of CO2. The highest emission rate in the 2003-2006 period was 79,559 tonnes of CO2 and the lowest for the 2006-2009 period was 9,069 tonnes of CO2. The increase in carbon sequestration is caused by the increase in land cover in secondary dryland forest and the use of land for mixed dry land agriculture.

scholarly journals THE USE OF ONLINE WEB-BASED SOFTWARE I-TREE CANOPY AS AN ALTERNATIVE IN SUPPORTING COMMUNITY BASED ZERO DELTA Q PLANNING

2018 ◽  
Vol 2 (2) ◽  
pp. 120
Author(s):  
Akhmadi Puguh Raharjo
Keyword(s):  
Land Cover ◽  
Planning Process ◽  
River System ◽  
Tree Canopy ◽  
Land Cover Class ◽  
Community Based ◽  
Web Based ◽  
The Difference ◽  
Cover Class ◽  
Online Web

Zero Delta Q is a policy to ensure that any additional surface runoff due to development does not further burden the drainage or river system. In case of Zero Delta Q application planning at the community level, a software is needed that can help classify and quantify the existing land cover class in area where the community is located. The purpose of this study is to look at the time needed and reliability of the i-Tree Canopy web-based software online in classifying and quantifying land cover classes on one of the sub-catchments in the Pesanggrahan River Basin. The land cover class is divided into six: trees, grasses / undergrowth plants, open area, water bodies, pavement / road and roof of the building. For comparison, an RBI map is used from the same area to see the extent of each class of land cover. Observation of each point requires an average time of 5.2 ± 1.0 seconds. The difference between direct sub-basin measurements using i-Tree Canopy and detailed analysis results from the RBI map is within the range of 0.41% or 0.36 Ha for each individual class of land cover. For a relatively small study area (under 100 ha) and when supported with reliable internet access, this web-based online software is sufficiently reliable in assisting the application planning process to support Zero Delta Q policy.

scholarly journals Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 35
Author(s):  
Dingfan Xing ◽  
Stephen V. Stehman ◽  
Giles M. Foody ◽  
Bruce W. Pengra
Keyword(s):  
Land Cover ◽  
Latent Class ◽  
Reference Data ◽  
Land Cover Class ◽  
Reference Class ◽  
Study Region ◽  
Latent Class Modeling ◽  
Class Labels ◽  
Cover Class ◽  
Percent Area

Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM). This study compares agreement between area estimates obtained from SA and LCM using reference data obtained by seven trained, professional interpreters who independently interpreted an annual time series of land cover reference class labels for 300 sampled Landsat pixels. We also compare the variability of the LCM and SA area estimates over different numbers of interpreters and different subsets of interpreters within each interpreter group size, and examine area estimates of three land cover classes (forest, developed, and wetland) and three change types (forest gain, forest loss, and developed gain). Differences between the area estimates obtained from SA and LCM are most pronounced for the estimates of wetland and the three change types. The percent area estimates of these rare classes were usually greater for LCM compared to SA, with the differences between LCM and SA increasing as the number of interpreters providing the reference data increased. The LCM area estimates generally had larger standard deviations and greater ranges over different subsets of interpreters, indicating greater sensitivity to the selection of the individual interpreters who carried out the reference class labeling.

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