scholarly journals Land-Use and Land Cover Changes on the Slopes of Mount Meru-Tanzania

2018 ◽  
Vol 13 (3) ◽  
pp. 331-352
Author(s):  
ALDO. J. KITALIKA ◽  
REVOCATUS. L. MACHUNDA ◽  
HANS. C. KOMAKECH ◽  
KAROLI. N. NJAU
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Land Use Changes ◽  
Mixed Forest ◽  
Spatial Filter ◽  
Software Environment ◽  
Agriculture Land ◽  
The Future ◽  
Environmental Transitions

The study of spatial land use and land change is inevitable for sustainable development of land use plans. Environmental transitions analysis was done in part of the land on the slopes of the foothills of Mount Meru in thirty (30) years’ time from 1986 to 2016 using satellite-derived land use/cover maps and a Cellular Automata (CA) spatial filter under IDRISI software environment and assessed the important land use changes. Also, the future land use for 2026 which is the next ten (10) years was simulated based on Cellular-Automata Markov model. The results showed significant land use transitions whereby there is a huge land use change of bush land (BL) and agriculture land (AG) into human settlement (ST) which resulted into conversion of Arusha town into a City. In addition, the changes have caused slight changes in water bodies into mixed forest. Moreover, the future land use/land cover (LULC) simulations indicated that there will be unsustainable LULC changes in the next ten years since most of bush land and part of agriculture land will be used for building different structures thus interfering with fresh water and food availability in the City. These changes call upon the relevant planning authorities to put in place the best strategies for good urban development.

scholarly journals Modeling Major Rural Land-Use Changes Using the GIS-Based Cellular Automata Metronamica Model: The Case of Andalusia (Southern Spain)

2020 ◽  
Vol 9 (7) ◽  
pp. 458 ◽  
Author(s):  
Rafael M. Navarro Cerrillo ◽  
Guillermo Palacios Rodríguez ◽  
Inmaculada Clavero Rumbao ◽  
Miguel Ángel Lara ◽  
Francisco Javier Bonet ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Land Use Changes ◽  
Growth Patterns ◽  
Kappa Index ◽  
Rural Land ◽  
Dry Land ◽  
Rural Land Use ◽  
Clustered Network

The effective and efficient planning of rural land-use changes and their impact on the environment is critical for land-use managers. Many land-use growth models have been proposed for forecasting growth patterns in the last few years. In this work; a cellular automata (CA)-based land-use model (Metronamica) was tested to simulate (1999–2007) and predict (2007–2035) land-use dynamics and land-use changes in Andalucía (Spain). The model was calibrated using temporal changes in land-use covers and was evaluated by the Kappa index. GIS-based maps were generated to study major rural land-use changes (agriculture and forests). The change matrix for 1999–2007 showed an overall area change of 674971 ha. The dominant land uses in 2007 were shrubs (30.7%), woody crops on dry land (17.3%), and herbaceous crops on dry land (12.7%). The comparison between the reference and the simulated land-use maps of 2007 showed a Kappa index of 0.91. The land-cover map for the projected PRELUDE scenarios provided the land-cover characteristics of 2035 in Andalusia; developed within the Metronamica model scenarios (Great Escape; Evolved Society; Clustered Network; Lettuce Surprise U; and Big Crisis). The greatest differences were found between Great Escape and Clustered Network and Lettuce Surprise U. The observed trend (1999–2007–2035) showed the greatest similarity with the Big Crisis scenario. Land-use projections facilitate the understanding of the future dynamics of land-use change in rural areas; and hence the development of more appropriate plans and policies

scholarly journals PREDIKSI SPASIAL DINAMIKA AREAL TERBANGUN KOTA SEMARANG DENGAN MENGGUNAKAN MODEL REGRESI LOGISTIK

2016 ◽  
Vol 18 (2) ◽  
pp. 95 ◽  
Author(s):  
Irmadi Nahib
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Cellular Automata ◽  
Land Cover ◽  
Open Space ◽  
Land Use Changes ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Rate Of Increase ◽  
The City

<p class="JudulABSInd"><strong>ABSTRAK</strong></p><p class="abstrak">Salah satu indikator perkembangan fisik wilayah kota dapat diidentifikasi melalui fenomena perubahan tutupan lahan bervegetasi menjadi lahan terbangun. Perubahan lahan tersebut dapat berdampak terhadap penurunan kualitas lingkungan, akibat berkurangnya ruang terbuka hijau. Kota Semarang dengan visi terwujudnya Semarang sebagai kota perdagangan dan jasa yang berbudaya menuju masyarakat sejahtera, merupakan  wilayah yang rentan mengalami perubahan penggunaan lahan yang cenderung kearah lahan terbangun. Penelitian ini mengintegrasikan model <em>Cellular Automata</em> (CA) dan regresi logistik biner untuk memprediksi dinamika lahan terbangun di Kota Semarang. Citra yang digunakan adalah Citra Ikonos 2002, Ikonos 2006 dan <em>Quic</em><em>kbird</em> 2012. Model CA pada penelitian ini digunakan untuk memprediksi sebaran penutup lahan tahun 2022 dan 2032 dengan mempertimbangkan jarak terhadap jalan, jarak terhadap sungai, jarak terhadap lahan terbangun, ketinggian, kepadatan penduduk, <em>evidence likelihood </em>perubahan lahan dan indeks pengembangan kelurahan yang diakomodasi dalam peta sub-model transisi hasil model regresi logistik biner. Hasil penyusunan model ini adalah peta prediksi penutup lahan dengan akurasi 78,21 % validitas model yang dihasilkan dapat dikategorikan “<em>moderate</em>” mengindikasikan bahwa peta yang dihasilkan dapat digunakan. Hasil pemodelan menunjukkan bahwa Kota Semarang pada tahun 2022 terjadi pertambahan luas lahan terbangun rata-rata 284 ha/tahun dan pada tahun 2032 rata-rata 226 ha/tahun.</p><p><strong><em>Kata </em></strong><strong><em>k</em></strong><strong><em>unci</em></strong><em>: </em><em>cellular automata, pemodelan, regresi logistik biner, lahan terbangun</em></p><p class="judulABS"><em><strong>ABSTRACT</strong></em></p><p class="Abstrakeng">One indicator of the physical development of the city can be identified by phenomenon of land expansion, vegetated land cover changes to be built-up area. The land use changes can impact to environmental degradation, due to reduced green open space. Semarang as a city of trade and services cultured toward a prosperous community, a region that is vulnerable to changes in land use tends toward small plots. This research integrates the model of Cellular Automata (CA) and binary logistic regression to predict the dynamics of builtup area in the city of Semarang. The image used is a Ikonos imagery (2002), Ikonos imagery (2006) and Quickbird (2012). Model CA in this research use to predict the distribution of land cover 2022 and 2032 with respect to: distance to roads, the distance to the river, the distance to the built-up area, elevation, population density, evidence likelihood of land use change and development villages index were accommodated in the map sub-model transition binary logistic regression model results. The results of this study are predictive maps of built-up area  with an accuracy of 78,21 % so that the validity of the resulting model can be categorized as "moderate", indicates that the probability map is valid. Modeling results showed that Semarang City in 2022 predicted rate of increase of  built-up area an average 284  ha / year and in 2032 rate of increase of built-up area an average 226 ha / year.</p><p><strong><em>Keywords</em></strong><em>: cellular automata, modelling, binary logistic regression, built-up area</em></p>

scholarly journals Land Use/Land Cover Change of Rabi Season of Rahuri Taluka of Ahmednagar District Maharashtra

2020 ◽  
pp. 20-27
Author(s):  
Payal A. Mahadule ◽  
A. A. Atre ◽  
Ankita P. Kamble ◽  
C. Pande ◽  
S. D. Gorantiwar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
United States Geological Survey ◽  
Fallow Land ◽  
Land Use Land Cover ◽  
Rabi Season ◽  
Agriculture Land ◽  
Spatio Temporal ◽  
Sentinel 2A

Advanced change location procedures by utilizing multi-temporal satellite symbolism helps in understanding landscape dynamics. The present examination shows the spatio- temporal elements of land use of Rahuri Taluka, Ahmednagar District, Maharashtra, India.  Sentinel 2A satellite imageries of four different months of Rabi season (2019-2020) were acquired by United States Geological Survey (USGS) earth explorer site and quantify the changes in the Rahuri Taluka from October 2019 to January 2020 over a period of 3 months.This study applied supervised classification-maximum likelihood algorithm by using Arc GIS 10.1 Map envision to distinguish land use changes of Rahuri. Land Use/Land Cover (LULC) in the Rahuri has experienced a progression of changes in the course of the last three months. Four significant LULC classes viz; Water body, Built-up Land, Waste/Fallow land, Agriculture land have been distinguished and demonstrate that significant land use in the Rahuri Taluka. Results appears, water bodies was highest in month of October 15.68% (166.48 km2), Agriculture land was highest in month of November 59.77% (634.56 km2) and Waste/Fallow land was significantly higher in month of October 41.1% (437.47 km2) and December 41.7% (442.77 km2) than November 30.54% (324.28 km2). The examination and discoveries of the investigation features significant approach suggestions for the maintainable Land Use/Land Cover the board in the Rahuri.

scholarly journals IMPACT OF SPATIAL FILTER ON LAND-USE CHANGES MODELLING USING URBAN CELLULAR AUTOMATA

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 429-433 ◽  
Author(s):  
M. Omidipoor ◽  
N. N. Samani
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Changes ◽  
Spatial Filter ◽  
Kappa Index ◽  
Spatial Filters ◽  
Size And Shape ◽  
Neighbourhood Size ◽  
Different Shapes ◽  
The Impact

Urban cellular automata is used vastly in simulating of urban evolutions and dynamics. Finding an appropriate neighbourhood size in urban cellular automata modelling is important because the outputs are strongly influenced by input parameters. This paper investigates the impact of spatial filters on behaviour and outcome of urban cellular automata models. In this study different spatial filters in various sizes including 3*3, 5*5, 7*7, 9*9, 11*11, 13*13, 15*15 and 17*17 cells are used in a scenario of land-use changes. The proposed method is examined changes in size and shape of spatial filter whereas the resolution was kept fixed. The implementation results in Ahvaz city demonstrated that KAPPA index is changed in different shapes and types at the time when different spatial filters are used. However, circular shape with size of 5*5 offers better accuracy.

scholarly journals Long-Term Perspective Changes in Crop Irrigation Requirement Caused by Climate and Agriculture Land Use Changes in Rechna Doab, Pakistan

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1567 ◽  
Author(s):  
Arfan Arshad ◽  
Zhijie Zhang ◽  
Wanchang Zhang ◽  
Ishfaq Gujree
Keyword(s):  
Land Use ◽  
Meteorological Parameters ◽  
Land Use Changes ◽  
Crop Water ◽  
Irrigation Requirement ◽  
Cropping Patterns ◽  
Agriculture Land ◽  
The Future ◽  
Sown Area ◽  
Crop Irrigation

Climate change and agriculture land use changes in the form of cropping patterns are closely linked with crop water use. In this study the SDSM (statistical downscaling model) was used to downscale and simulate changes in meteorological parameters from 1961 to 2099 using HadCM3 General Circulation Model (GCM) data under two selected scenarios i.e., H3A2 and H3B2. Results indicated that Tmax, Tmin, and wind speed may increase while relative humidity and precipitation may decrease in the future under both H3A2 and H3B2 scenarios. Downscaled meteorological parameters were used as input in the CROPWAT model to simulate crop irrigation requirement (CIR) in the baseline (1961–1990) and the future (2020s, 2050s and 2080s). Data related to agriculture crop sown area of five major crops were collected from Punjab statistical reports for the period of 1981–2015 and forecasted using linear exponential smoothing based on the historical rate. Results indicated that the cropping patterns in the study area will vary with time and proportion of area of which sugarcane, wheat, and rice, may exhibit increasing trend, while decreasing trend with respect to the baseline scenario was found in maize and cotton. Crop sown area is then multiplied with CIR of individual crops derived from CROPWAT to simulate Net-CIR (m3) in three sub-scenarios S1, S2, and S3. Under the H3A2 scenario, total CIR in S1, S2, and S3 may increase by 3.26 BCM, 12.13 BCM, and 17.20 BCM in the 2080s compared to the baseline, while under the H3B2 scenario, Net-CIR in S1, S2, and S3 may increase by 2.98 BCM, 12.04 BCM, and 16.62 BCM in the 2080s with respect to the baseline. It was observed that under the S2 sub-scenario (with changing agriculture land-use), total CIR may increase by 12.13 BCM (H3A2) and 12.04 BCM (H3B2) in the 2080s with respect to the baseline (1961–1990) which is greater as compared to S1 (with changing climate). This study might be valuable in describing the negative effects of climate and agriculture land use changes on annual crop water supply in Rechna Doab.

scholarly journals Simulating Land-Use Changes and Predicting Maize Potential Yields in Northeast China for 2050

2021 ◽  
Vol 18 (3) ◽  
pp. 938
Author(s):  
Luoman Pu ◽  
Jiuchun Yang ◽  
Lingxue Yu ◽  
Changsheng Xiong ◽  
Fengqin Yan ◽  
...  
Keyword(s):  
Land Use ◽  
Food Security ◽  
Northeast China ◽  
Land Use Changes ◽  
Sanjiang Plain ◽  
Potential Production ◽  
Potential Yield ◽  
The Sanjiang Plain ◽  
Ecological Zones ◽  
The Future

Crop potential yields in cropland are the essential reflection of the utilization of cropland resources. The changes of the quantity, quality, and spatial distribution of cropland will directly affect the crop potential yields, so it is very crucial to simulate future cropland distribution and predict crop potential yields to ensure the future food security. In the present study, the Cellular Automata (CA)-Markov model was employed to simulate land-use changes in Northeast China during 2015–2050. Then, the Global Agro-ecological Zones (GAEZ) model was used to predict maize potential yields in Northeast China in 2050, and the spatio-temporal changes of maize potential yields during 2015–2050 were explored. The results were the following. (1) The woodland and grassland decreased by 5.13 million ha and 1.74 million ha respectively in Northeast China from 2015 to 2050, which were mainly converted into unused land. Most of the dryland was converted to paddy field and built-up land. (2) In 2050, the total maize potential production and average potential yield in Northeast China were 218.09 million tonnes and 6880.59 kg/ha. Thirteen prefecture-level cities had maize potential production of more than 7 million tonnes, and 11 cities had maize potential yields of more than 8000 kg/ha. (3) During 2015–2050, the total maize potential production and average yield decreased by around 23 million tonnes and 700 kg/ha in Northeast China, respectively. (4) The maize potential production increased in 15 cities located in the plain areas over the 35 years. The potential yields increased in only nine cities, which were mainly located in the Sanjiang Plain and the southeastern regions. The results highlight the importance of coping with the future land-use changes actively, maintaining the balance of farmland occupation and compensation, improving the cropland quality, and ensuring food security in Northeast China.

scholarly journals Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

2020 ◽  
Vol 30 (1) ◽  
pp. 273-286
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar
Keyword(s):  
Land Use ◽  
Image Segmentation ◽  
Cellular Automata ◽  
Land Cover ◽  
Satellite Imagery ◽  
Current Model ◽  
Discrete Dynamical System ◽  
Land Use Land Cover ◽  
Data Set ◽  
Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

scholarly journals Impacts of land use changes on soil erosion in Pa Deng sub-district, adjacent area of Kaeng Krachan National Park, Thailand

2012 ◽  
Vol 7 (No. 1) ◽  
pp. 10-17 ◽  
Author(s):  
S. Wijitkosum
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
National Park ◽  
Land Use Changes ◽  
Influential Factors ◽  
Geographical Information ◽  
Adjacent Area ◽  
Erosion Risk ◽  
Study Results

Soil erosion has been considered as the primary cause of soil degradation since soil erosion leads to the loss of topsoil and soil organic matters which are essential for the growing of plants. Land use, which relates to land cover, is one of the influential factors that affect soil erosion. In this study, impacts of land use changes on soil erosion in Pa Deng sub-district, adjacent area of Kaeng Krachan National Park, Thailand, were investigated by applying remote sensing technique, geographical information system (GIS) and the Universal Soil Loss Equation (USLE). The study results revealed that land use changes in terms of area size and pattern influenced the soil erosion risk in Pa Deng in the 1990&ndash;2010 period. The area with smaller land cover obviously showed the high risk of soil erosion than the larger land cover did.

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

2018 ◽  
Vol 10 (10) ◽  
pp. 3421 ◽  
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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