Balancing Urban Growth and Food Autonomy: An Integrated Machine Learning and Agricultural Statistics Framework for Local Rice Self-Sufficiency in the PNAR of Purwokerto, Indonesia

Serang Raya is developing rapidly, characterized by the growing population, and increasing socioeconomic activities would threaten the sustainability as one of the food storage areas and guard the natural ecosystem in Java. The need for urban land may lead to land use with various protection functions and productive agricultural land, resulting in a decrease in food production and environmental degradation. This study aims to analyze the dynamics of land-use change in Serang Raya in 2000-2018, to predict land use in 2030, and to analyze the unconformity between land use in 2018 and the expected land use in 2030 with the spatial plan of Banten Province 2010-2030. The analytical methods used image interpretation, CA-Markov land use predictions, and overlay analysis to identify unconformity. The results showed that land use in Serang Raya had changed with a dominant pattern of change: rice fields became open land, plantations became built-up areas, and rice fields became built-up areas. Prediction of land use in 2030 shows an increase of built-up area, which is relatively high along with a decrease in paddy fields and plantations. The existing land use conditions in 2018 led to unconformity with the space allocation of 8.92% and increased to 9.31% compared to the predicted land in 2030.Keywords: Spatial Unconformity, Land Conversion, Land Prediction

Land use change can be predicted by spatial modeling. One of spatial method to predict land use change is CLUES (Conversion of Land Use Changes and its Effect at small regional extent). The objective of the research is to determine factors that influence land use change in Kabupaten Majalengka and to predict its land use in the future. Data applied in this research project was biogeophysic, socio economic and existing land use data of Kabupaten Majalengka. Eight land use classes applied were shrub, forest, pond, dry land agriculture, pasture, settlement, and plantation and rice field. Land use classes were dependent variables; and biogeophysical and socio economical data were independent variables. Binary logistic regression was applied to determine influenced factors to land use change. Factors that influence of type of land use’s occurrence were different for each type of land use. The value of the factors was used as an input in land use modeling using CLUES. Scenario that used were scenario with land use change rate same as previous change. The simulation was applied for 12 years with the baseline data in the year of 2009. The simulation result shows that the land use types of shrub, forest, and plantation are decrease. The area that increased are land use types of dry land agriculture, settlement and rice field; however land use types of pond and pasture are remain constant.

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.

Bogor City is located 60 kms from the Indonesian Capital Jakarta and 120 kms from Bandung the capital of West Java Province. Strategic agricultural land and inexpensive land prices have to invest in agricultural and non-agricultural sectors. Land use conversion is inevitable, although the government has set up the regulation to control land usage (Act No 26 year 2007). This research are conducted: (1) To analyze existing land use of Bogor City (land use in 2014). (2) To analyze the land use change in period of 2005-2012. (3) To analyze the consistency of existing land use compare to spatial allocation in the RTRW of Bogor City. (4) To identify regional development hierarchy levels in Bogor City. (5) To determine factors that influence land use change. This research was started with a land use map that was retrieved from an administrative map 1: 5000 scales to Iconos image of Bogor City in years of 2005 and 2012. Consistency and inconsistency were obtained by overlaying the 2012 land use map and land allocation map of Bogor City years 2011-2031 (RTRW map). PODES is used to determine the regional hierarchy level by multiple regression methods. Existing land use classified into 9 land use, in sequence from the largest one are: mixed garden, irreguler settlement, rice field, reguler settlement, trading area, water body, dryland farm, open land and government office area. Dryland farm land use changes into 2 land use, open field land use change into 6 land use and rice field land use change into 6 land use. The consistent use of the existing land use compare to RTRW is 40.95% while inconsistence existing land use is 59.05%. During the period of 2006 to 2012, most of the regional development hierarchies in Bogor City were relatively constant (42 villages / 61.8%), 12 villages (17.6%) are increasing in the hierarchy, whereas 14 villages (20.6%) are decreasing in the hierarchy. Factors that influencing land use change of Bogor City in the period of 2005-2012 are: the extent of ​​agricultural land in 2005, the growth of social facilities, and the growth of economy facilities. Keywords : Land Use Conversion, Land Use Inconsistency, Regional Hierarchy, Spatial Plan.

An integrated machine learning and remote sensing approach for monitoring forest degradation due to Rohingya refugee influx in Bangladesh

It became an important key around the urbanised areas how to control and preserve the aglicultural and forestry fields against to the urbanization. But, the land use plannings were mostly discussed and organized from the side of city and urban planning.In flat areas, the growing patterns of urbanization also has no relations to natural land conditions. The land use plannings relate to natural land potentials are to be discussed and appropriate land use controls should be bring into practice.In this study, the relation between the landscape changes and land use changes were discussed (figure1). Then the general ideas that explain the existing situations of land uses were proposed. Those are “adaptable” zone, “most adaptable” zone and “stable” zone. On the process. the land use items were divided into three groupes. The first is the forestry area, the second is the aglicultural area and the third is the urbanized area. Adaptable zones and most adaptable zones can be explain with the natural land conditions.Stable zones can be determine through the dynamic balance of existing land use groupes.Finally, the stable zones of every land use groupes were suggested by the slope degree of land in Osaka prefecture through mesh analysis. And the process of calculation of slope degree is shown in f gure 5.The forestry areas are stable on sloped zonesand the slope degree is over 7. The aglicultural areas are stable on the sloped and flat areas and their slope degree is fom 0 to 8. But the zone of the slope degree from 0 to 4 can be more stable zones as rice fields and farm areas. But the flat areas (slope degree=0) were mostly urbanized and natural land uses were reduced.Here the relation between land use and slope degree were discussed mainly. But there are more and more elements that relate to land use and we should find out more planning and analysing units that can cover the land use elements and natural elements.

As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city’s overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于CA-Markov模型的石羊河流域生态承载力时空格局预测 DOI: 10.5846/stxb201711302153 作者: 作者单位: 兰州大学资源环境学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金（41671516，41701623）；中国科学院西北生态环境资源研究院青年人才成长基金；甘肃省软科学专项（1504ZKCA090-1） Spatio-temporal pattern prediction of the biocapacity in the Shiyang River Basin on the basis of the CA-Markov model Author: Affiliation: Lanzhou University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:生态承载力是区域可持续发展的重要物质基础。为了探讨我国干旱区内陆河流域未来生态承载力的时空格局变化，以石羊河流域为研究区，基于该流域1992、2002年和2012年3期Landsat TM遥感影像，选取10个影响生态承载力变化的主要驱动因素，利用元胞自动机-马尔科夫模型（CA-Markov），以土地利用预测为切入点，对该流域2022年的生态承载力时空格局进行了模拟预测。首先基于1992-2002年数据预测了2012年石羊河流域土地利用状况，并与当年实际土地利用状况进行对比和验证，结果显示：Kappa系数为0.7956，说明本文所选用的预测方法预测结果可靠，可以用于该流域土地利用预测；其次，以2012年为起始年，模拟预测了2022年土地利用空间数据，并计算生态承载力时空格局，结果显示：2022年石羊河流域单位面积生态承载力与1992年、2002年和2012年相比整体空间分布格局变化不大，但区域内部变化却呈明显的空间分异特征；1992-2022年流域生态承载力总量以2002年为拐点呈先减少后增加的趋势，其中建设用地的生态承载力增加最为显著；预测可知，较2012年，2022年流域上游山区的林地、中游绿洲的建筑用地生态承载力均增加较快，且呈现较明显的斑块聚集，而中、下游绿洲区耕地、草地生态承载力增、减变化复杂，斑块分布较为离散和破碎；2012-2022年土地利用类型将发生频繁转换，导致流域生态承载力结构组成变化较大，其中未利用地的转出对2022年流域生态承载力的增加贡献突出。研究结果表明自2002年以后，该流域实施的退耕还林还草、关井压田等生态工程已经并将继续对该流域生态效益的提高起到积极的作用。 Abstract:The biocapacity is an important material foundation of regional sustainable development. In order to explore the spatio-temporal pattern of biocapacity in inland rivers, arid China in the future, for a case study of the Shiyang River Basin, using the CA-Markov model as a predication method and choosing 10 main driving factors influencing the biocapacity, we predicted the land use and assessed biocapacity in 2022 based on LandsatTM images in 1992, 2002, and 2012. First of all, by comparing the predicted land use with the actual land use situation in 2012, we got the Kappa coefficient of 0.7956, which showed that the driving factors and the CA-Markov model were reliable for predicting the land use of the basin. Second, based on the model above, we predicted the land use condition and assessed biocapacity in 2022 with the year 2012 as a starting point. The results showed the following:in the Shiyang River Basin as a whole, the distribution pattern of biocapacity per unit area in 2022 will be similar to the situation in 1992, 2002, and 2012; however, there was remarkable spatial heterogeneity within the basin. The total biocapacity of the basin in 1992-2022 has been increasing since the turning point in 2002, and the increase of biocapacity in the built-up area was the most significant part of them. In the comparison with 2012, the biocapacity of the forestland in the mountainous area of the upstream reach and the built-up area in the oasis area of the midstream reach in 2022 should increase faster than other areas of the river basin, and there was a significant patch cohesion. Nonetheless, in the oasis areas of midstream and downstream reaches, the changes of farmland and grassland biocapacity should be complicated and the distribution of the land use patch should be discrete and fragmented. The frequent transition of land use types in 2012-2022 should lead to a large structural change of the biocapacity in the river basin, including an important contribution to the increase of biocapacity supported by the transfer-out area of unused land in 2022. Above all, the results provided a more extensive understanding of the spatio-temporal dynamic and spatial pattern of biocapacity in the Shiyang River Basin. Ecological engineering such as the Grain for Green Project will continue to produce obvious ecological benefits in the future. 参考文献 相似文献 引证文献

Rapid urbanization is a serious concern for most of the developing countries as well as for India. Rapid and dynamic urbanization requires continuous monitoring of the underlying process of urbanization. But some loopholes of the Census of India data restrict continuous monitoring of rapid urbanization as the Census of India is the only source of urbanization-related numerical data in India. Past land use pattern of the study area shows a rapid growth of urban areas from 38.70 km2 in 2009 to 54.92 km2 in 2017 at the expense of agricultural land, vegetation, and also wetland areas. Markov chain-derived transition matrix-based cellular automata modeling is used to predict the land use pattern for 2017 on the basis of past two land use for a rapidly urbanizing phase integrating fuzzy standardization and weighted linear combination based potential urban development surface. Predicted land use map is used to compare with the actual land use map of 2017 extracted from satellite image for assessing the applicability of this model for future land use change prediction. Validation result shows a 94.24% (in terms of areal extension) agreement between actual and predicted urban area map of 2017. Then, the final urban growth map for 2020 is predicted using 2014 and 2017 land use maps and the same urban development potential area map used for predicting the 2017 urban area map. The predicted map shows a rapid urbanization in the adjacent areas of Salt Lake City especially towards the Northern and Eastern direction of the Salt Lake area, which increases the conciseness of the urban planners.

Land use changes cause uncertain conditions for rice production and food security in Indonesia as well as in Bengkulu. The land use changes on infertile peaty soils from paddy cultivation to oil palm plantation and other land uses treat rice production sustainability. The purposes of this research was to show land use conversion from rice field to other land functions in Air Hitam irrigation area, Bengkulu. This study was done from July to November 2021 at Air Hitam rice fields in Central Bengkulu Regency, Bengkulu Province. The existing land use in three periods was analyzed using satellite imagery with appropriate resolutions and multitemporal time comprising Landsat 5 TM (Thematic Mapper), Landsat 7 ETM+ (Enhanced Thematic Mapper + and Landsat 8 OLI (Operational Land Imager) taken out 2000, 2010 and 2020 released by United States Geological Survey (USGS). The dominant land uses at the Air Hitam area in year of 2000 were mostly rice fields covering about 71.84%, and the rice fields in year of 2010 decrease to 53.63%. The rice fields conversion still continuous, and in 2020 the rice fields dropped to 47.65% while oil palm plantation covers about 38.74% of the area. In the last two decades the oil palm plantation cultivated by small scale farmers sharp increase, in contrast, rice fields at this area were huge loss. The rice field loss of 292.27 ha at the Air Hitam irrigation area threat rice production in Bengkulu.

The process of rapid urbanization has intensified the conversion of different land use types, resulting in a substantial loss of ecological land and ecological security being threatened. In the context of China’s vigorous advocacy of an ecological civilization, it is important to explore future land use patterns under ecological security constraints to promote sustainable development. The insufficient consideration of land ecological security in existing land use pattern simulation studies makes it difficult to effectively promote improvement in the ecological security level. Therefore, we developed a land use simulation framework that integrates land ecological security. Taking the sustainable development of land ecosystems as the core, the land ecological security index (LESI) and ecological zoning (EZ) were determined by the pressure–state–response (PSR) model and the catastrophe progression method (CPM). Natural development (ND) and ecological protection (EP) scenarios were then constructed taking the LESI and EZ into consideration. The CA–Markov model was used to simulate the land use pattern of Guangzhou for 2030 under the two scenarios. The results showed that (1) the study area was divided into four categories: ecological core zone, ecological buffer zone, ecological optimization zone, and urban development zone, with area shares of 37.53%, 31.14%, 16.96%, and 14.37%, respectively. (2) In both scenarios, the construction land around the towns showed outward expansion; compared with the ND scenario, the construction land in the EP scenario decreased by 369.10 km2, and the woodland, grassland, and farmland areas increased by 337.04, 20.80, and 10.51 km2, respectively, which significantly improved the ecological security level. (3) In the EP scenario, the construction land in the ecological core zone, ecological buffer zone, and ecological optimization zone decreased by 85.49, 114.78, and 178.81 km2, respectively, and no new construction land was added in the ecological core zone, making the land use pattern of the EP scenario more reasonable. The results of the study have confirmed that the land use pattern simulation framework integrating land ecological security can effectively predict land use patterns in different future scenarios. This study can provide suggestions and guidance for managers to use in formulating ecological protection policies and preparing territorial spatial planning.

It requires spatial planning is suitable for the direction of land use. This study aimed to evaluate the suitability of existing land use with regional spatial planning, and determine the course of spatial use control. The research using a comparative quantitative method and for making land units using Geographic Information Systems (GIS). Land units were produced from overlapping between the administration map, land use map (existing), and map of spatial plans. Three categories were made consists of suitable, unsuitable, and not implemented yet. The results of the research showed that existing land use in Sidenreng Rappang regency consists of nine types, namely paddy fields, plantations, settlements, swamps, shrubs, moor, forests, lake, and mixed gardens. Analysis of land use suitable with the regional spatial planning covers 123,882 ha (63.57%), 66,033 ha (33.88%) unsuitable, and 4,952 ha (2.54%) has not been implemented yet. The direction for controlling the use of the land is applying four control instruments, both those that have been implemented and those that have not been achieved so far, namely licensing, zoning regulations, granting incentives and disincentives, and imposing sanctions. Land use directives in Sidenreng Rappang Regency are well implemented (66.11 %) but require instructions that have not been achieved with a spatial regulation (33.88%).

This research was carried out in Pengeragoan Village, Pekutatan District, Jembrana Regency, from July to September 2023. The aim of this research was to determine the chemical properties of soil and determine soil fertility status classes and provide direction in land management according to the level of soil fertility status for each type. Land use in Pengeragoan Village, Pekutatan District, Jembrana Regency. The types of land use that exist include coffee plantations, cocoa plantations, coconut plantations, mixed plantations, natural forests, teak forests and rice fields. Sampling was carried out compositely on several existing land use types, namely coffee plantations (Tki), cocoa plantations (Tko), coconut plantations (Tka), mixed plantations (Tc), natural forests (Tha), teak forests (Tj), and rice field (Ts). Determination of soil fertility status is carried out by measuring soil chemical properties parameters, namely soil Cation Exchange Capacity (CEC), Base Saturation (KB), P-total, K-total and C-organic. The research results show that the soil fertility status in several types of land use falls into the criteria of high, medium and low. Natural forest and mixed garden land use types have high soil fertility status. The land use types of coffee plantations, cocoa plantations and teak forests have medium soil fertility status. The land use types of coconut plantations and rice fields have low soil fertility status. The management direction recommended from the results of evaluating soil fertility status and several supporting soil chemical properties for each type of land use is the need to add N and P nutrients to the land use types of coffee plantations, mixed plantations and natural forests. In the types of land use of cocoa plantations, coconut plantations, teak forests and rice fields, it is necessary to add nutrients N, P and organic materials in the form of plant litter and organic fertilizer.

Land use change is an important issue that occurs massively in the Ciawi Regency area, mainly due to rapid population growth and the increasing need for residential space, public facilities, and infrastructure. The problem that arises from this phenomenon is the occurrence of intensive land use conversion, especially the increase in built-up land area which has the potential to disrupt the environmental balance. This study aims to analyze land use changes from 2003 to 2023, as well as predict land use conditions in 2033. The study covers the entire administrative area of Ciawi Regency, Bogor, with a focus on seven land use classes, including built-up land, forests, rice fields, and gardens. The methodology used includes classification of Landsat 5 and 8 images using the Random Forest algorithm through the Google Earth Engine (GEE) platform, as well as predictive modeling using the Cellular Automata – Artificial Neural Network (CA-ANN) method through the MOLUSCE plugin in QGIS. The driving variables used in the prediction include distance to road, distance to settlement, and distance to river. The results of the study show a significant increase in built-up land from 291.45 hectares (2003) to 1,262.37 hectares (2023), and is predicted to reach 1,073.07 hectares in 2033. Prediction model validation showed an overall accuracy of 92.92% and a Kappa coefficient value of 0.82, which signifies excellent model quality. These findings are expected to be the basis for consideration in spatial planning and sustainable development policies in Ciawi Regency.

Paser Regency is one of the food buffer areas for the New Capital City (IKN) of Indonesia in East Kalimantan Province. The relocation of the capital to IKN increases the number of populations and food demand as well. To meet food needs in IKN in the future, the development of agricultural land is needed. This study aimed to identify the type and direction of land use in Paser Regency to ensure food self-sufficiency. Sentinel-2 satellite Imagery with the Maximum Likelihood Classification (MLC) method was used in this study. The sentinel data from 2018 to 2022 was analyzed to identify the existing land use types over the past five years. The land use direction was determined based on the existing land use type, soil type, and land slope class. The results with an accuracy value for the overall test of 91.68% and kappa accuracy of 0.86 showed a change in land use from 2018 to 2022. Eight types of main land used were identified such as non-swamp rice fields, swamp rice fields, oil palm plantations, moors, shrubs, forests, settlements, and water bodies. The application of NDVI Sentinel-2Ac Imagery can identify a potential area of 13,249.7 hectares for rice field development and can significantly increase rice production to ensure food self-sufficiency in IKN.