scholarly journals Impact of land use change on hydrological conditions in the Karajae watershed, South Sulawesi Province

2021 ◽  
Vol 886 (1) ◽  
pp. 012079
Author(s):  
Chairil A ◽  
Syamsu Rijal ◽  
Munajat Nursaputra ◽  
Muh. Faisal Mappiase
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Image Data ◽  
Water Source ◽  
Assessment Tools ◽  
Landsat 8 ◽  
Dryland Agriculture ◽  
Land Use Patterns ◽  
Hydrological Conditions ◽  
Landsat 7

Abstract Land use is a representation of activities and utilization of land resources by the community. Land use has a big influence on the hydrological condition of a watershed. One of the small watersheds, in general, is the Karajae watershed, but it has a very large impact on the City of Pare-Pare, and the surrounding community. The Karajae watershed is the main water source for the people of Pare-Pare and agriculture. This study aims to analyze land use patterns that have a major impact on hydrological conditions in the Karajae watershed. The analysis begins with remote sensing methods to interpret land use using Landsat 7 image data in 2010 and Landsat 8 imagery in 2020. Next, analyze the pattern of land use change in detail in each forest area with a geographic information system approach. Analysis of hydrological conditions using the Soil and Water Assessment Tools approach with the input of the land use data. Land use Change 2010-2020 in the Karajae watershed shows additional land use in the form of settlements, rice fields, and dryland agriculture as a form of community activity. There are two forest areas in the Karajae watershed, namely production forest and protected forest. Production forest is dominated by dryland agriculture in the form of corn, beans, and horticulture, while the protected forest is dominated by and secondary dryland forest. This has an impact on hydrological conditions that there are fluctuations in discharge and an increase in sediment a decade ago. Optimal application of forest functions reduces discharge and sediment. Different forest planning for each forest function and land use within. Production forest with many activities directed towards community-based forest management such as community forest and village forest. As for the Protected Forest, which is dominated by grassland and shrubs, forest rehabilitation is carried out.

scholarly journals Estimation of Green Land to Urban Change Based on Cellular Automata (CA) Method in Singaraja City and its Surrounding Areas

2018 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Nyoman Arto Suprapto ◽  
Takahiro Osawa ◽  
I Dewa Nyoman Nurweda Putra
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Areas ◽  
Negative Impact ◽  
Positive Impact ◽  
Landsat 8 ◽  
Land Conversion ◽  
Landsat 7 ◽  
Surrounding Areas ◽  
Green Land

Singaraja city is the second largest city in Bali which have a fairly rapid growth. Growth and development of the region in urban areas of Singaraja give the positive impact on the economy of the community but also give the negative impact on the environment. Land use change and land conversion into one of the negative issues of the development of urban areas in Singaraja. This study intends to calculate the amount of land conversion occur on the green land into urban areas within 14 years (2001-2015) and predict land use change in 2020 and 2025 in Singaraja City and Its Sorrounding Areas. Landsat 7 and Landsat 8 imageries were used to determine the land use map. Land use map obtained through the process of image classification using supervised method then verified using data field. Land use maps in 2015 and 2001 used to obtain the amount of change of urban areas and green land during the period of 14 years. This results show increasing amount of urban areas as 11,37% (3.153,74 ha) whereas green land decreased by 11,17% (3.097,68 ha). Land use change was predicted by Markov method. The projection results show the amount of urban areas in 2020 was 27,40% (7.598,45 ha) and 35,97% (9.974,55 ha) in 2025. The results obtained with this prediction accuracy rate of 0.91.

scholarly journals Current and Future Land Use Characters of a National Central City in Eco-Fragile Region—A Case Study in Xi’an City Based on FLUS Model

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 286
Author(s):  
Dingrao Feng ◽  
Wenkai Bao ◽  
Meichen Fu ◽  
Min Zhang ◽  
Yiyu Sun
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Development ◽  
Urban Expansion ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Pattern Change ◽  
Land Use Dynamics ◽  
The Future ◽  
Xi’An City

Land use change plays a key role in terrestrial systems and drives the process of ecological pattern change. It is important to investigate the process of land use change, predict land use patterns, and reveal the characteristics of land use dynamics. In this study, we adopted the Markov model and future land use (FLUS) model to predict the future land use conditions in Xi’an city. Furthermore, we investigated the characteristics of land use change from a novel perspective, i.e., via establishment of a complex network model. This model captured the characteristics of the land use system during different periods. The results indicated that urban expansion and cropland loss played an important role in land use pattern change. The future gravity center of urban development moved along the opposite direction to that from 2000 to 2015 in Xi’an city. Although the rate of urban expansion declined in the future, urban expansion remained the primary driver of land use change. The primary urban development directions were east-southeast (ENE), north-northeast (NNE) and west-southwest (WSW) from 1990 to 2000, 2000 to 2015, and 2015 to 2030, respectively. In fact, cropland played a vital role in land use dynamics regarding all land use types, and the stability of the land use system decreased in the future. Our study provides future land use patterns and a novel perspective to better understand land use change.

scholarly journals Estimating historical land use patterns in Amazonia for hydrological studies

2017 ◽  
Vol 8 (4) ◽  
pp. 189-197
Author(s):  
Christiane Cavalcante Leite ◽  
Marcos Heil Costa ◽  
Ranieri Carlos Ferreira de Amorim
Keyword(s):  
Land Use ◽  
Water Resources ◽  
Land Use Change ◽  
Census Data ◽  
Land Use Patterns ◽  
Use Patterns ◽  
The Past ◽  
The World ◽  
Historical Land Use ◽  
Past Land Use

The evaluation of the impacts of land-use change on the water resources has been, many times, limited by the knowledge of past land use conditions. Most publications on this field present only a vague description of the past land use, which is usually insufficient for more comprehensive studies. This study presents the first reconstruction of the historical land use patterns in Amazonia, that includes both croplands and pasturelands, for the period 1940-1995. During this period, Amazonia experienced the fastest rates of land use change in the world, growing 4-fold from 193,269 km2 in 1940 to 724,899 km2 in 1995. This reconstruction is based on a merging of satellite imagery and census data, and provides a 5'x5' yearly dataset of land use in three different categories (cropland, natural pastureland and planted pastureland) for Amazonia. This dataset will be an important step towards understanding the impacts of changes in land use on the water resources in Amazonia.

scholarly journals Impacts of land use change scenarios on hydrology and land use patterns in the Wu-Tu watershed in Northern Taiwan

2007 ◽  
Vol 80 (1-2) ◽  
pp. 111-126 ◽  
Author(s):  
Yu-Pin Lin ◽  
Nien-Ming Hong ◽  
Pei-Jung Wu ◽  
Chen-Fa Wu ◽  
Peter H. Verburg
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Northern Taiwan

Penentuan Luas Area Sawah Kabupaten Sleman Pada Citra Landsat 8 Operational Land Imager (OLI)

Respati ◽  
2018 ◽  
Vol 13 (3) ◽  
Author(s):  
Sulidar Fitri ◽  
Novi Nurjanah
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Support Vector Machine ◽  
Rice Field ◽  
Image Data ◽  
Rice Fields ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Learning Machine

INTISARITeknologi penginderaan jauh sangat baik dijadikan data pembuatan peta penggunaan lahan, karena kebutuhan pemetaan semakin tinggi terutama untuk mendeteksi perubahan penggunaan lahan terutama untuk penentuan luas area khususnya sawah di kabupaten Sleman. Untuk mendapatkan informasi luasan area sawah dari interpretasi citra landsat-8 OLI (Operational Land Imager) diperlukan metode khusus, terutama untuk pengolahan data citra penginderaan jauh secara digital. Salah satu metode pengolahan citra penginderaan jauh adalah metode Support Vector Machine (SVM). Metode SVM merupakan metode learning machine (Pembelajaran mesin) yang dapat mengklasifikasikan pola serta mengenali pola dari inputan atau contoh data yang diberikan dan juga termasuk ke dalam supervised learning. Hasil area sawah yang didapati dari citra Landsat 8 OLI dengan pengolahan metode SVM didapati berada di 18 kecamatan dala Kabupaten Sleman. Luasan tertinggi ada di kecamatan Ngaglik dengan 19,78 KM2 dan terendah di kecamatan Turi seluas 2,14 KM2. Nilai keseluruhan akurasi yang didapat untuk kelas lahan sawah dan area non sawah adalah adalah 53%.Kata kunci— Landsat-8 OLI, SVM, Data Citra, Geospasial, Luas Area Sawah ABSTRACTRemote sensing technology is very well used as a data for making land use maps, because mapping needs are increasingly high especially for detecting land use changes, especially for determining the area, especially rice fields in Sleman district. To get information about the area of the rice fields from the interpretation of Landsat-8 OLI (Operational Land Imager), special methods are needed, especially for processing remote sensing image data digitally. One method of processing remote sensing images is the Support Vector Machine (SVM) method. The SVM method is a learning machine method that can classify patterns and recognize patterns from input or sample data provided and also includes supervised learning. The results of the rice field that were found from the Landsat 8 OLI image by processing the SVM method were found in 18 sub-districts in Sleman Regency. The highest area is in Ngaglik sub-district with 19.78 KM2 and the lowest in Turi sub-district is 2.14 KM2. The overall value of the accuracy obtained for the class of rice field and non-rice field is 53%.Kata kunci—  Landsat-8 OLI, SVM, Image Data, Geospatial, Area of Rice Fields

scholarly journals Study of Land Use Change from 1997 To 2014 Using Landsat Data In Bangli Regency

2018 ◽  
Vol 2 (2) ◽  
pp. 89
Author(s):  
Nuranita Naningsi ◽  
Takahiro Osawa ◽  
I Nyoman Merit
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Fresh Water ◽  
Paddy Field ◽  
Rapid Development ◽  
Landsat 8 ◽  
Landsat Data ◽  
Data Set ◽  
Dry Land ◽  
Bare Land

Bangli Regency is one of Regency in the Bali Province. The total area of  Bangli Regency is 52,081 hectares (9.24%) of total area of Bali Province (563,666 ha). The Growth and the development of the region Bangli Regency the positive impacts on the economy of the community, and the negative impacts on the environment. Land use change is one of the negative issue of development Bangli Regency. This study conduted the calculation of land use change from 1997 to 2014 using Landsat data in Bangli Regency. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI/TIRS imageries were used to determine the land use map based, on using supervised classification method. The field data set the nine classes were classtuded based, on the classification were fresh water, bare land, forest, residential, bushes, irrigated paddy field, non irrigated paddy field, dry land and plantation. There results showed in land use changes from 1997 to 2014 that plantation increased (19,486.33 ha (36.89%)), and residential increased (1,872.00 ha (3.47%)), there is also a vast to reduction in dry land  (-10,868.90 ha (-21.21%)), forest (-6,333.34 ha (-12.24%)), irrigated paddy field (-1,619.50 ha (-3.17%)), bushes (-1,637.30 ha (-3.27%)), bare land (-63.00 ha (-0.17%)), non irrigated paddy field (-113.59 ha ( -0.26%)) and fresh water (-2.70 ha (-0.05%). The results accuracy rate was 89.45%. Anslyse of land use showed that the significant decrease of plantation area in Bangli Regency hill due to rapid development of infrastrusture of tourism and extensive residential area has increased particularly in sub district of the Kintamani District.

CLIMATE CHANGE AND FUTURE LAND USE IN THE UNITED STATES: AN ECONOMIC APPROACH

2011 ◽  
Vol 02 (01) ◽  
pp. 27-51 ◽  
Author(s):  
DAVID HAIM ◽  
RALPH J. ALIG ◽  
ANDREW J. PLANTINGA ◽  
BRENT SOHNGEN
Keyword(s):  
Climate Change ◽  
United States ◽  
Land Use ◽  
Land Use Change ◽  
Land Use Changes ◽  
The United States ◽  
Land Use Patterns ◽  
Cropland Area ◽  
Climate Change Effects ◽  
Net Returns

An econometric land-use model is used to project regional and national land-use changes in the United States under two IPCC emissions scenarios. The key driver of land-use change in the model is county-level measures of net returns to five major land uses. The net returns are modified for the IPCC scenarios according to assumed trends in population and income and projections from integrated assessment models of agricultural prices and agricultural and forestry yields. For both scenarios, we project large increases in urban land by the middle of the century, while the largest declines are in cropland area. Significant differences among regions in the projected patterns of land-use change are evident, including an expansion of forests in the Mountain and Plains regions with declines elsewhere. Comparisons to projections with no climate change effects on prices and yields reveal relatively small differences. Thus, our findings suggest that future land-use patterns in the U.S. will be shaped largely by urbanization, with climate change having a relatively small influence.

scholarly journals Adequacy Analysis of Green Open Space and its Development Direction as an Attributes of Green City in the Capital of Jember Regency, Indonesia

2018 ◽  
Vol 2 (1) ◽  
pp. 53
Author(s):  
Santun R.P. Sitorus ◽  
Imelda Kusuma Wardani ◽  
Setyardi Pratika Mulya
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Open Space ◽  
Paddy Fields ◽  
Sustainable Urban Development ◽  
Dryland Agriculture ◽  
Development Direction ◽  
Potential Land ◽  
Green City

The development of an urban area needs to pay attention to the environmental carrying capacity. One of the way to achieve sustainable urban development is to apply one of the attributes of green city namely green open space (GOS). The purpose of the research are to analyze the types of land use in the years of 2010 and 2017, to analysis land use changes from 2010 to 2017, predicting land use change, analyzing the adequacy of GOS by area acreage and population number, and to determine the direction of GOS development in the Jember City.The research was conducted in the Capital of Jember Regency, namely Jember City with the total area of 9,900 ha. Methods of data analysis are the spatial analysis, analysis of population growth with quadratic growth model, Cellular Automata-Markov, and synthesis of green open space (GOS) development direction based on potential land and the value of the land. The results showed that there are ten types of land use in the Jember City, those are forest, mixed gardens, dryland agriculture, open land, cemetery, plantation, settlements and buildings, paddy fields, shrubs and grasses, and river. A relatively large land use changed in the period of 2010-2017 were dryland agriculture and paddy fields into settlements and buildings. The results of land use prediction with Cellular Automata-Markov described the trend of land use change becomes settlements (buildings) and plantations. The adequacy of public GOS by area as well as population still lacking whereas the adequacy of private GOS has been exceeded. The GOS acreage based on number of population is lower than those GOS based on an area. The GOS development planning is required to fulfill the needs. The consideration used to draw up the directives is the existing land use, regional spatial plan (RTRW), prediction of land use in the year of 2024, distribution of GOS, and land values. Development plans of GOS consist of two stages namely stage 1 and stage 2 with three priorities, namely priority 1, priority 2, and priority 3 with the total area 1,052 ha and funding require approximately two trillion rupiahs. The acreage of potential land for development of GOS has already enough to fulfill the needs of GOS based on population, however, not yet sufficient to fulfill the needs of GOS based on regency area.

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