EVALUATION OF LAND USE WITH LAND CAPABILITY CLASSIFICATION USING SATELLITE DATA AND GIS IN BATUR UNESCO GLOBAL GEOPARK

Development in ??Batur UNESCO Global Geopark has increased significantly in recent years. The land use changes in Bangli regency that the plantation, built-up/residential and tourism support facilities development increased quickly, especially in Kintamani district. To know the suitability of land use in Batur UNESCO Global Geopark area, it is necessary to evaluate land use with land capability classification. Landsat 8 remote sensing data on 27 September 2017 was used to create land use maps. Land use maps obtained through the process of image classification using supervised classification method and verified by ground check, this technique result 10 classes of land use. Land capability class map generated from improvisation overlay methods, reclassification of differentiator classes into the differentiator value of a raster data pixel (differentiator pixel value method). Furthermore, to evaluate of land use suitability was conducted by comparing land use with land capability class. In this process, there is an overlay between the land use maps with land capability class map using geographic information system (GIS). The results of evaluation land use in Batur UNESCO Global Geopark with land capability class overall has suitable area 15,764.78 ha (88.49%), not suitable area 1,767.48 ha (9.92%) and not detected/cloud interference 283.67 ha (1.59%).

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EVALUATION OF LAND USE WITH REGIONAL SPATIAL PLAN (RTRW) USING SATELLITE DATA AND GIS IN BATUR UNESCO GLOBAL GEOPARK

ECOTROPHIC Jurnal Ilmu Lingkungan (Journal of Environmental Science) ◽

10.24843/ejes.2018.v12.i01.p08 ◽

2018 ◽

Vol 12 (1) ◽

pp. 57

Author(s):

Putu Wira Utama ◽

Takahiro Osawa ◽

I Wayan Sandi Adnyana

Keyword(s):

Remote Sensing ◽

Land Use ◽

Satellite Remote Sensing ◽

Supervised Classification ◽

Remote Sensing Data ◽

Landsat 8 ◽

Land Use Suitability ◽

Suitable Area ◽

Land Use Maps ◽

Spatial Plan

Development in ??Batur UNESCO Global Geopark which has an area of ??19,422.39 ha has increased significantly in recent years. The existence of limited land and to know the suitability of land use, it is necessary to evaluate of land use with regional spatial plan (RTRW). Landsat 8 satellite remote sensing data on 27 September 2017 is used to create land use maps. Land use maps obtained through the process of image classification using supervised classification method and verified by ground check. From this technique result 11 classes of land use. Furthermore, to evaluate of land use suitability was conducted by comparing land use with regional spatial plan (RTRW). In this process, there is an overlay between the land use maps with regional spatial plan (RTRW) map using geographic information system (GIS). The results of evaluation land use in Batur UNESCO Global Geopark with regional spatial plan (RTRW) overall has suitable area 10,863.14 ha (55.93%), not suitable area 8,275.58 ha (42.61%) and not detected/cloud interference 283.67 ha (1.46%).

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Landuse Changes Refer to Spatial Planning Regulations at Kelara Watershed Area: An Analysis Using Geospatial Information Technology

Forum Geografi ◽

10.23917/forgeo.v29i1.795 ◽

2015 ◽

Vol 29 (1) ◽

Author(s):

Andi Ramlan ◽

Risma Neswati ◽

Sumbangan Baja ◽

Muhammad Nathan

Keyword(s):

Remote Sensing ◽

Land Use ◽

Spatial Data ◽

Spatial Planning ◽

Land Use Changes ◽

Remote Sensing Data ◽

Landsat 8 ◽

Geospatial Information ◽

Survey Techniques ◽

Information System Technology

The purpose of this study is to analyze land use changes in the Kelara watershed and to assess the suitability of current land use changes with the spatial planning regulation of Jeneponto within Kelara basin. This study integrates various survey techniques, remote sensing, and geographic information system technology analysis. Geospatial information used in this study consists of Landsat ETM 7+ satellite imagery (2009) and Landsat 8 (2014) as well as a number of spatial data based on vector data which is compiled by the Jeneponto Government. Remote sensing data using two time series (2009 and 2014) are analyzed by means of supervised classification and visual classification. The analysis indicated that land use type for the paddy fields and forests (including mangroves) converted become a current land use which is inconsistent with the spatial planning regulation of Jeneponto.The use of land for settlement tends to increase through conversion of wetlands (rice fields). These conditions provide an insight that this condition will occur in the future, so that providing the direction of land use change can be better prepared and anticipated earlier.

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Changes in Coastal Agricultural Land Use in Response to Climate Change: An Assessment Using Satellite Remote Sensing and Household Survey Data in Tien Hai District, Thai Binh Province, Vietnam

Land ◽

10.3390/land10060627 ◽

2021 ◽

Vol 10 (6) ◽

pp. 627

Author(s):

Duong H. Nong ◽

An T. Ngo ◽

Hoa P. T. Nguyen ◽

Thuy T. Nguyen ◽

Lan T. Nguyen ◽

...

Keyword(s):

Climate Change ◽

Land Use ◽

Agricultural Land ◽

Land Use Changes ◽

Management Strategies ◽

Household Survey ◽

Extreme Weather Events ◽

Agricultural Land Use ◽

Landsat 8 ◽

Land Uses

We analyzed the agricultural land-use changes in the coastal areas of Tien Hai district, Thai Binh province, in 2005, 2010, 2015, and 2020, using Landsat 5 and Landsat 8 data. We used the object-oriented classification method with the maximum likelihood algorithm to classify six types of land uses. The series of land-use maps we produced had an overall accuracy of more than 80%. We then conducted a spatial analysis of the 5-year land-use change using ArcGIS software. In addition, we surveyed 150 farm households using a structured questionnaire regarding the impacts of climate change on agricultural productivity and land uses, as well as farmers’ adaptation and responses. The results showed that from 2005 to 2020, cropland decreased, while aquaculture land and forest land increased. We observed that the most remarkable decreases were in the area of rice (485.58 ha), the area of perennial crops (109.7 ha), and the area of non-agricultural land (747.35 ha). The area of land used for aquaculture and forest increased by 566.88 ha and 772.60 ha, respectively. We found that the manifestations of climate change, such as extreme weather events, saltwater intrusion, drought, and floods, have had a profound impact on agricultural production and land uses in the district, especially for annual crops and aquaculture. The results provide useful information for state authorities to design land-management strategies and solutions that are economic and effective in adapting to climate change.

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Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Sustainability ◽

10.3390/su10103421 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3421 ◽

Cited By ~ 43

Author(s):

Rahel Hamad ◽

Heiko Balzter ◽

Kamal Kolo

Keyword(s):

Land Use ◽

Markov Chain ◽

Land Cover ◽

Land Use Changes ◽

Landsat 8 ◽

Chain Model ◽

Land Use Land Cover ◽

Kurdistan Region ◽

The Past ◽

Past Trend

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

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Research on Land Use Changes in Panjin City Basing on Remote Sensing Data

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/153/6/062050 ◽

2018 ◽

Vol 153 ◽

pp. 062050

Author(s):

Hua Ding ◽

Ru Ren Li ◽

Li Shuang Sun ◽

Xin Wang ◽

Yu Mei Liu

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Use Changes ◽

Remote Sensing Data ◽

Sensing Data

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Prediksi Perubahan Lahan Terbangun Dengan OBIA LCM Pada Citra Terfusi di Kota Kendari

JAGAT (Jurnal Geografi Aplikasi dan Teknologi) ◽

10.33772/jagat.v5i2.21621 ◽

2021 ◽

Vol 5 (2) ◽

pp. 170

Author(s):

Adnan Adnan ◽

Fitra Saleh ◽

Iradat Salihin

Keyword(s):

Land Use ◽

Landsat Imagery ◽

Land Use Changes ◽

Landsat 8 ◽

Land Change ◽

Land Use Classification ◽

Landsat 8 Oli ◽

Land Change Modeler ◽

Two Factors ◽

Land Changes

Abstrak: Penggunaan lahan disetiap tahunnya akan mengalami perubahan. Perkembangan tersebut bisa jadi tidak terkendali, sehingga perencanaan prediksi perubahan lahan penting untuk dikaji. Dalam memprediksi dapat dilakukan dengan menggunakan citra, khususnya citra Landsat. Penelitian ini bertujuan untuk: (1) distribusi penggunaan lahan terbangun di Kota Kendari pada tahun 2014 dan 2019 dengan metode OBIA pada citra terfusi; (2) melihat arah perubahan penggunaan lahan terbangun di Kota Kendari pada tahun 2024 dan 2029 dengan metode Land Change Modeler (LCM). Metode yang digunakan dalam penelitian ini yaitu metode klasifikasi penggunaan lahan berbasis piksel OBIA dan pemodelan prediksi perubahan penggunaan lahan Land Change Modeler (LCM). Hasil penelitian ini antara lain: (1) luas lahan terbangun pada tahun 2014 di Kota Kendari seluas 6.061,85 hektar dan luas penggunaan lahan terbangun di Kota Kendari pada tahun 2019 seluas 6.716,96 hektar dengan perubahan penggunaan lahan terbangun tahun 2014 sampai dengan tahun 2019 dengan pertambahan luas 2,43%; (2) Arah perubahan penggunaan lahan terbangun di Kota Kendari diprediksikan cenderung berkembang ke arah Kecamatan Baruga karena dipengaruhi oleh dua faktor yaitu kemiringan lereng dan jaringan jalan. Kata Kunci : Penggunaan Lahan, Landsat 8 OLI, Penajaman Citra, OBIA, LCM Abstract: Land use will change every year. The development may be uncontrollable, so predictive planning of land changes is important to review. In predicting can be done using imagery, especially Landsat imagery. This study aims to:(1) the distribution of land use built in Kendari City in 2014 and 2019 with OBIA method on diffusion imagery; (2) see the direction of land use changes built in Kendari City in 2024 and 2029 with land change modeler (LCM) method. The methods used in this study are OBIA pixel-based land use classification method and land use change prediction modeling land change modeler (LCM). The results of this study include: (1) land area built in 2014 in Kendari City aswide as 6,061.85 hectars and land use area built in Kendari City in 2019 aswide as 6,716.96 hectars with land use changes built in 2014 to 2019 with an increase of 2.43%; (2) The direction of land use changes built in Kendari City is predicted to tend to develop towards Baruga Subdistrict because it is influenced by two factors, namely slope and road network. Keywords: Land Use, Landsat 8 OLI, Image Sharpening, OBIA, LCM

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Study on Coastline Changes of Xiamen City Based on Remote Sensing Images

E3S Web of Conferences ◽

10.1051/e3sconf/201913605003 ◽

2019 ◽

Vol 136 ◽

pp. 05003

Author(s):

Yanfang Qin ◽

Lin Ye ◽

Siming Chen

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Use Changes ◽

Remote Sensing Data ◽

Length Change ◽

Vector Data ◽

Remote Sensing Images ◽

Change Rate ◽

Xiamen City ◽

Spatio Temporal

Based on the Landsat remote sensing data, this paper had monitored the coastline changes of Xiamen city in recent 20 years. By extracting the coastline vector data of 1999, 2005, 2011 and 2017 respectively, the spatio-temporal characteristics of coastline changes on coastline length, change rate and land change area were analyzed, and the main driving factors were analyzed combined with the land use changes in the coastal swing area. The results show that: the total length of Xiamen's coastline increased from 235.16 km to 264.98 km during 1999-2017, and the land area increased from 1558.84 km2 to 1594.29 km2. The most significant changes occurred in Xiang'an district and Huli district with the coastline length increased by 16.38% during 2011-2017 and 22.14% during 1999-2005 respectively, while the changes were not very conspicuous in other areas. According to the land use changes in the coastal areas, the coastline changes in Xiamen City were mainly related to the expansion of construction land and port constructions in Haicang district, Xiang'an district and Huli district, as well as the expansion of aquaculture in the Xiang'an district.

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