Identify Land Use Research Articles

Spatiotemporal Evolution and Drivers of Ecological Quality in the Tengger Desert (2001–2021)

Desert ecosystems, particularly in arid regions like the Tengger Desert, are highly sensitive to both anthropogenic activities and climate change, making the monitoring and evaluation of ecological quality critical for sustainable management and restoration efforts. This study analyses the spatiotemporal evolution of ecological quality in the Tengger Desert from 2001 to 2021 using the Remote Sensing Ecological Index (RSEI), incorporating meteorological factors (temperature, precipitation, wind speed), topographical factors (elevation, slope, relief) and anthropogenic indices (land use and land cover). The mean RSEI fluctuated between 0.1542 and 0.2906, indicating poor ecological quality, with a peak in 2008 attributed to national ecological projects. Despite initial improvements, overall ecological quality declined at a rate of 0.0008 a−1 from 2008 to 2021. Spatially, degradation was most pronounced in the central and southern areas. Due to sand-binding engineering in the Tengger Desert in 2008 and the mountain climate suitable for vegetation growth, improvements occurred in the northeast and southwest. Moran’s I and Hurst index analyses revealed significant spatial clustering of ecological quality and persistence of degradation trends, with over 49.53% of the area projected to experience further deterioration. Geodetector analysis identified land use and land use cover as the most influential factors on RSEI, especially in combination with wind speed, temperature, and precipitation, underscoring the role of both human activities and climate. The study highlights the need for sustained ecological management, particularly in areas showing continuous degradation, to prevent further ecological deterioration.

ENCODING RESTRICTIONS AS AN OBJECT OF CADASTRAL SYSTEMS IN UKRAINE

Transparency and uniformity of registration of land use restrictions are interdependent and complementary aspects critical for ensuring effective management of land resources. The interaction of transparency and uniformity creates conditions for accessibility, comprehensibility, and better perception of data. The study analyses land use restrictions identification during their registration in the State Land and Urban Planning Cadastres to determine the uniformity of the registration. The analysis mainly focuses on the main attributes of the land use restrictions, such as code and name. Based on the research results, we established that the classification of land use restrictions for registration in various state cadastral systems, particularly State Land and Urban Planning Cadastres, is mostly not identical and requires coordination. There is an unjustified duplication of some land use restrictions and ambiguity in assigning them to specific categories. A particular problem is coding land use restrictions as registration objects without considering the hierarchical spatial organisation of restriction zones and the principle of logical construction. There is also an inexplicable duplication of some categories of land use restrictions, as in: the protected archaeological territory (codes 06.06.3 and 16.04), the historic and cultural protected territory (codes 06.06.5 and 16.03), the historical and cultural reserve (codes 06.06.4 and 16.02), the historical area of ​​the inhabited place (codes 06.06.2 and 16.07). In addition, there are minor differences in the names. It is worth noting that correctly identifying land use restrictions is essential for developing local urban planning documents due to the requirements of integrating information from various sources and updating them regularly. Transparency and uniformity of registration of land use restrictions in the State Land Cadastre and Urban Planning Cadastre are the main factors contributing to the effective management of land resources, ensuring legal certainty, attracting investments, and protecting the rights of land owners. They also contribute to reducing corruption, increasing trust in public authorities, and protecting the environment. Keywords: restrictions, code, state land cadastre, urban planning cadastre.

Модернізація державного контролю (нагляду) за використанням та охороною земель в Україні: перспективи використання інформаційних продуктів дистанційного зондування Землі

Objective: This article aims to analyze existing challenges in the implementation of land use and protection control and to explore promising directions for the modernization of state control (supervision) over land use and protection. The focus is primarily on the use of remote sensing information products as an evidence base for identifying land use violations. Methodology: A comprehensive approach was employed, which includes the analysis of national legislation on state control over land use and protection, as well as a review of remote sensing information products and cadastral system data available for official use. Scientific Novelty: The scientific novelty of this study lies in the comprehensive analysis of contemporary organizational problems affecting the effectiveness of state control (supervision) over land use and protection in Ukraine. It justifies and identifies promising directions for its modernization through the implementation of remote sensing information products. The research reveals the potential of using remote sensing products to identify various types of land law violations and to ensure the legal reliability of the evidence base. Practical Significance: The recommendations presented can be utilized by government authorities to improve legislation regarding state control (supervision) over land use and protection. This improvement would involve the use of remote sensing information products in supervisory and control measures.

Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina

Abstract The research subject is the application of geographic information systems (GIS) in assessing land suitability for agriculture in the Sana River Basin in Bosnia and Herzegovina. The aim of the research is to apply the analytic hierarchy process (AHP) in identifying suitable areas for agricultural production. Within the AHP framework, the following factors were considered: pedology, land use, elevation, slope, aridity index, and distance from rivers. The results of the suitability assessment underwent reclassification (RP) and cluster analysis processes (CAPs). It was found that very unsuitable land (1) covers an area of 0.15% (RP) or 5.83% (CAP), unsuitable land (2) covers 3.44% (RP) or 17.52% (CAP), conditionally suitable land (3) covers 32.11% or 28.47% (CAP), suitable land (4) covers 56.29% or 28.57% (CAP), and very suitable land (5) covers 7.98% (RP) or 19.59% (CAP). At the study area level, a supervised classification process was conducted to identify land use classes: meadows/pastures, water, forest, agricultural, and built-up areas. RP and CAP results were overlaid with supervised classification results to determine the amount of land used for agricultural purposes within each suitability class. It was determined that currently, for agricultural purposes, 0.04 km2 (RP) or 0.88 km2 (CAP) of very unsuitable land (1) is used, 0.41 km2 (RP) or 7.28 km2 (CAP) of unsuitable land (2), 15.75 km2 (RP) or 27.52 km2 (CAP) of conditionally suitable land (3), 185.15 km2 (RP) or 107.06 km2 (CAP) of suitable land (4), and 42.99 km2 (RP) or 101.65 km2 (CAP) of very suitable land (5). The research findings hold substantial importance in elucidating both the potential and constraints of land use practices as a vital natural resource within agriculture. They also have practical importance for relevant institutions in terms of agricultural sector development and making timely land use planning decisions for sustainable development.

Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas

Identifying land use mix (LUM) in urban areas is challenging, often requiring extensive human intervention and fieldwork. Accurate classification of LUM is crucial for various disciplines, including urban planning, urban economics, and public health. This study addresses this need by employing Voronoi triangulation and an entropy-based LUM formula using point-based geospatial data collected from publicly available sources. The methodology was tested in two distinct urban settings: Ankara and Kadıköy. Ankara, the capital city, provides a large and diverse urban environment, while Kadıköy, a district in Istanbul known for its dynamic urban life, offers a contrasting scenario. Results were analyzed concerning local spatial autocorrelation and point of interest (POI) intensity. The comparative analysis demonstrated that the approach performs well across different urban contexts, with improved results observed in Kadıköy due to its higher density of mixed-use development. Specifically, we managed to identify mixed land use areas with an accuracy of up to 78% and an F1-score of 83% in urban regions. These findings highlight the robustness and applicability of our approach in diverse urban environments, providing valuable insights for city planners and policymakers in optimizing the allocation of urban resources and enhancing land use efficiency.

Spatiotemporal Variation in Ecological Environmental Quality and Its Response to Different Factors in the Xia-Zhang-Quan Urban Agglomeration over the Past 30 Years

The interactions between economic development, environmental sustainability, population growth, and urbanization are vital in assessing the ecological dynamics of urban agglomerations. This study explores the relationship between economic development, environmental sustainability, population growth, and urbanization within the Xia-Zhang-Quan urban agglomeration in Fujian Province from 1989 to 2022. Utilizing Landsat remote sensing images, we calculated the Remote Sensing Ecological Index (RSEI) to evaluate changes in ecological quality. The results show that the average RSEI values for 1989, 2000, 2010, and 2022 were 0.5829, 0.5607, 0.5827, and 0.6195, respectively, indicating an initial decline followed by a significant increase, culminating in an overall upward trend. The spatial distribution of RSEI classification shows that the study area has the largest proportion of mainly “good” ecological quality. The proportion of areas with “excellent” ecological environmental quality has increased (13.41% in 1989 and 25.12% in 2022), while those with “general” quality has decreased (28.03% in 1989 and 21.21% in 2022). Over the past three decades, Xiamen experienced substantial ecological degradation (RSEI change of −0.0897), Zhangzhou showed marked improvement (RSEI change of 0.0519), and Quanzhou exhibited slight deterioration (RSEI change of −0.0396). Central urban areas typically had poorer ecological conditions but showed signs of improvement, whereas non-central urban regions demonstrated significant environmental enhancement. The factor detector analysis identified land use as the dominant factor influencing ecological environmental quality, with precipitation having a relatively minor impact. Interaction analysis revealed that all other factors demonstrated bi-variable enhancement or nonlinear enhancement, suggesting that the interactive effects of these factors are greater than the effects of individual factors alone. Land use consistently showed solid explanatory power. Temperature also exhibited significant influence in 2022 when interacting with other factors. Due to urban planning that can plan for land use, these findings suggest that effective urban planning can harmonize economic development with ecological protection within the Xia-Zhang-Quan urban agglomeration.

Pengelolaan Permukiman Pesisir dan di Atas Air Berkelanjutan di Desa Teluk Sunting, Kabupaten Kepulauan Anambas Provinsi Kepulauan Riau

Coastal settlements are highly vulnerable because they are greatly affected by global climate change. Population growth will further increase the use of unorganized built space in the coastal area of Teluk Sunting Village which in the future could lead to conflict as a result of regulations and policies regarding coastal areas. This research was conducted in the coastal residential area of Teluk Sunting Village, Central Siantan District, Anambas Islands Regency. The research method consists of data collection techniques, sampling techniques, data requirements, data presentation, analysis methods and data analysis techniques. The results obtained are that the administrative area of Teluk Sunting Village contains cultivation and protected areas according to the RTRW Spatial Pattern Plan. The area is 5.29 Ha of mangrove ecosystem area and 34.83 Ha of protected forest area. Area of 19.22 Ha cultivation area. The environmental quality results obtained in RT 1 settlements are low quality settlements, RT 3 is medium quality settlements and RT 2 and RT 4 settlements are high quality settlements. The conclusions obtained from the four approaches taken to identify land use patterns, namely land use patterns, land use and use with land tenure, conformity with the RTRW and conformity with the zoning of Aquatic Tourism Parks, are in accordance with the designation. The involvement of stakeholders in the management of coastal and water areas in Teluk Sunting Village is very appropriate to support and ensure the sustainability of the area in the future.

Classification and detection of changes in land use and land cover using satellite images and GIS- near Suez Canal area

Egypt has developed significantly in the vicinity of the Suez Canal and its surrounding towns over the last ten years. Utilizing satellite pictures to identify land use and cover types from thematic maps remains difficult. Select the optimal strategy for successful satellite image classification across various applications. Enhancing classification accuracy requires the most effective algorithm. The effectiveness of a change detection system has not been tested in the community of Abu SUWEIR, located near the Suez Canal region in Ismailia City, Egypt. This project analyzes seven classification algorithms with the use of remotely sensed Planet Scope multispectral pictures to evaluate and monitor changes in land cover in ABU SUWEIR Village from 2016 to 2022. The post-January 25, 2011 revolution period in Egypt saw a transition from anarchy to the commencement of progress and improvement. This analysis demonstrates that the ANN classifier achieved an overall accuracy of 77.33% in 2016 and 72.00% in 2022, with corresponding KAPPA coefficients of 0.66 and 0.58 for each year. Agriculture was the most common land use type according to the ANN categorization, making up over 60.48% of the study area in 2016 and 53.89% in 2022. Bare terrain is the second most common landcover type, accounting for 30.31% in 2022 and 25.91% in 2016. On the contrary, urbanized regions, accounting for 13.61% in 2016 and 15.80% in 2022, are the least categorized.

Peri-urban landscapes and the potential of integrated foodscapes to support healthy cities: an Aotearoa New Zealand case study

ABSTRACT Aotearoa New Zealand is experiencing unprecedented competition in land use priorities on the edges of its cities and settlements – referred to as the peri-urban zone. Housing and food production compete with one another in a legislative planning context that supports a dichotomy of urban or rural land use. Food production is being driven further away from urban settlements, where 85% of New Zealanders live, as demand for housing increases, and land is re-zoned from rural to urban. Consequently, cities and settlements within Aotearoa New Zealand are increasingly seen to be spatially disconnected from their productive hinterlands. The resulting land use polarity occurring in the peri-urban zone is problematic for local food production, driving issues of agricultural land fragmentation, the loss of highly productive soils, ‘reverse sensitivity’, and impacts on human health and wellbeing. Engaging with both residents and food producers who reside and work within the peri-urban zone, this research applies a ‘design thinking’ methodology to the case study site of Ōtautahi Christchurch, exploring the question, ‘how can food production and housing best co-exist at the edges of Aotearoa New Zealand’s cities and settlements?’ to support human health and wellbeing outcomes. This paper presents five participant-driven future land use scenarios, which were developed based on survey responses collected from both peri-urban residents and food producers. The land use scenarios were then tested in a workshop setting with both groups assessing the potential of each scenario to address the land use conundrum that Aotearoa New Zealand peri-urban areas are currently facing, ultimately identifying land use strategies for healthier city areas that are designed for both housing and local food production.

Assessment of the degree of reliability in the declarations of the rural environmental register relation to the SIGEF area certifications

The Brazilian Forest Code was sanctioned in 2012, bringing forth a tool for analyzing rural properties known as the Rural Environmental Registry (CAR). This is a nationwide electronic public record mandatory for all rural properties. The registration tool has shown some discrepancies, particularly in the georeferencing section aimed at identifying land use, occupation, and perimeter information. This study aims to assess the divergences in information obtained through CAR due to different map insertion methods and to analyze the precision and accuracy of the methods. For each property, a map generated from Google Earth was produced in a KML file format. These maps were obtained through public consultation of the rural environmental registry provided by the Brazilian Institute of the Environment, comparing them against the orbital image contained therein for each method. The comparisons between methods were analyzed using simple correlation, categorized into four groups based on property size. The results obtained indicate that the registration quality is directly related to the quality of the georeferencing section within the CAR application. When combined with certification data, we obtain a more reliable declaration result.

Validation and field application of a low-cost device to measure CO2 and evapotranspiration (ET) fluxes

Abstract. Mitigating the global climate crisis and its consequences, such as more frequent and severe droughts, is one of the major challenges for future agriculture. Therefore, identifying land use systems and management practices that reduce greenhouse gas (GHG) emissions and promote water use efficiency (WUE) is crucial. This, however, requires accurate and precise measurements of carbon dioxide (CO2) fluxes and evapotranspiration (ET). Despite that, commercial systems to measure CO2 and ET fluxes are expensive and thus often exclude research in ecosystems within the Global South. This is especially true for research and data of agroecosystems in these areas, which are to date still widely underrepresented. Here, we present a newly developed low-cost, non-dispersive infrared (NDIR)-based CO2 and ET flux measurement device (∼ EUR 200) that provides reliable, accurate and precise CO2 and ET flux measurements in conjunction with manually operated closed chambers. To validate the system, laboratory and field validation experiments were performed, testing multiple different low-cost sensors. We demonstrate that the system delivers accurate and precise CO2 and ET flux measurements using the K30 FR NDIR (CO2) and SHT31 (RH, relative humidity) sensor. An additional field trial application demonstrated its longer-term stability (> 3 months) and ability to obtain valid net ecosystem C balances (NECBs) and WUE. This was the case, even though environmental conditions at the field trial application site in sub-Saharan Africa were rather challenging (e.g., extremely high temperatures, humidity and rainfall). Consequently, the developed low-cost CO2 and ET flux measurement device not only provides reasonable results but also might help with democratizing science and closing current data gaps.

Identifying Land Use Functions in Five New First-Tier Cities Based on Multi-Source Big Data

With the continuous development of big data technology, semantic-rich multi-source big data provides broader prospects for the research of urban land use function recognition. This study relied on POI data and OSM data to select the central urban areas of five new first-tier cities as the study areas. The TF-IDF algorithm was used to identify the land use functional layout of the study area and establish a confusion matrix for accuracy verification. The results show that: (1) The common feature of these five cities is that the total number and area of land parcels for residential land, commercial service land, public management and service land, and green space and open space land all account for over 90%. (2) The Kappa coefficients were all in the range [0.61, 0.80], indicating a high consistency of accuracy evaluation. (3) Chengdu and Tianjin have the highest land use function mixing degree, followed by Xi‘an, Nanjing, and Hangzhou. (4) Among the five new first-tier cities, Hangzhou and Nanjing have the highest similarity in land use function structure layout. This study attempts to reveal the current land use situation of five cities, which will provide a reference for urban development planning and management.

Multi-decadal coastal change detection using remote sensing: the Mediterranean coast of Egypt between El-Dabaa and Ras El-Hekma

A key source of information for many decision support systems is identifying land use and land cover (LULC) based on remote sensing data. Land conservation, sustainable development, and water resource management all benefit from the knowledge obtained from detecting changes in land use and land cover. The present study aims to investigate the multi-decadal coastal change detection for Ras El-Hekma and El-Dabaa area along the Mediterranean coast of Egypt, a multi-sectoral development area. Besides, the superiority of the area is highly dependent on its proximity to three development projects: the tourism and urban growth pole at Ras El-Hekma, the beachfront Alamain New Mega City, and the Nuclear Power Plant at El Dabaa. This study utilized multi-spectral Landsat satellite images covering 1990, 2010, and 2020 to perceive the post-classification change detection analysis of the land use and land cover changes (LULCC) over 30 years. The results of the supervised classification from 1990 to 2020 showed a 47.33 km2 (4.13%) expansion of the agricultural land area, whereas the bare soil land area shrunk to 73.13 km2 (6.24%). On the other hand, the built-up activities in the area launched in 2010 and escalated to 20.51 km2(1.77%) in 2020. The change in land use reveals the shift in the economic growth pattern in the last decade toward tourism and urban development. Meanwhile, it indicates that no conflict has yet arisen regarding the land use between the expanded socioeconomic main sectors (i.e., agriculture, and tourism). Therefore, the best practices of land use management and active participation of the stakeholders and the local community should be enhanced to achieve sustainability and avoid future conflicts. An area-specific plan including resource conservation measures and the provision of livelihood alternatives should be formulated within the National Integrated Coastal Zone Management (ICZM) plan with the participation of the main stakeholders and beneficiaries. The findings of the present work may be considered useful for sustainable management and supportive to the decision-making process for the sustainable development of this area.

Identifying land use land cover dynamics using machine learning method and GIS approach in Karaivetti, Tamil Nadu

<p>An important analytical tool for tracking, mapping, and quantifying changes in land use and land cover (LULC) across time serves as the use of machine learning techniques. The environment and human activities both have the potential to change how land is used and covered. Classifying LULC types at different spatial scales has been effectively achieved by models like classification and regression trees (CART), support vector machines (SVM), extreme gradient boosting (XGBoost), and random forests (RF). To prepare images from Landsat before sending and analysis for an aspect of our research, we employed the Google Earth Engine. High-resolution imagery from Google Earth images were used to assess each kind of method and field data collection. Utilizing Geographic Information System (GIS) techniques, LULC fluctuations between 2015 and 2020 were assessed. According to our results, XGBoost, SVM, and CART models proved superior by the RF model regarding categorization precision. Considering the data, we collected between 2015 and 2020, from 11.57 hectares (1.74%) in 2015 to 184.19 hectares (27.65%) in 2020, the barren land experienced the greatest variation, that made an immense effect. Utilizing the support of satellite imagery from the Karaivetti Wetland, our work combines novel GIS techniques and machine learning strategies to LULC monitoring. The created land cover maps provide a vital benchmark that will be useful to authorities in formulating policies, managing for sustainability, and keeping track of degradation.</p>

The Effectiveness of Municipal Comprehensive Planning in Mitigating Forest Fragmentation

To mitigate forest fragmentation, it is critical to investigate the factors that contribute to it. Considerable research examines various factors shaping forest fragmentation, but these studies have not typically identified land use planning as an important factor. Our study helps fill this gap in the literature. To examine the influence of land use planning on forest fragmentation, we assessed the quality of 29 municipal comprehensive plans with respect to forest protection in Wisconsin, U.S.A., a sample drawn from a spatial analysis of forest fragmentation dynamics from 2001 to 2011. We found that the comprehensive plans of communities that have experienced lower-than-expected forest fragmentation change were, on average, of significantly higher quality than those of the communities that have experienced higher-than-expected forest fragmentation change during this period. We conclude that the quality of community-based comprehensive plans, especially the strength and breadth of policies addressing forestland, matters in mitigating the fragmentation of forestland.

IDENTIFIKASI KAWASAN HUNIAN DI SEKITAR RUMAH SAKIT HARAPAN KITA DAN RUMAH SAKIT KANKERDHARMAIS, JAKARTA BARAT, INDONESIA

Harapan Kita Heart Hospital and Dharmais Cancer Hospital are national scale hospitals in Indonesia, are central hospitals with the most complete service facilities for heart disease and cancer. these two hospitals are referral centers for patients from various provinces and cities throughout Indonesia. There are various levels of care needed, patients must be accompanied by family or relatives at the time of treatment. For this reason, patients need a place to live around these two hospitals. The purpose of this study is to identify residential areas around Harapan Kita Heart Hospital and Dharmais Cancer Hospital, as well as identify land use suitability with the DKI Jakarta RDTR 2022. The study location is RT01 and RT 08, RW 06 and RW 09, South Bamboo City Village, Palmerah District, West Jakarta City, which is directly adjacent to the two hospitals. Descriptive analysis was used to determine the changes that occurred based on in-depth interviews with 30 study respondents who were boarding house owners or guards. The results showed a change in residential form by 80% from a onestory house to a 2.3-storey building, with the Basic Building Coefficient and Building Floor Coefficient not in accordance with the DKI Jakarta RDTR 2022.

Implications of Changing Urban Land Use on the Livelihoods of Local People in Northwestern Bangladesh

Bangladesh is one of the emerging economies in the Global South, and rapid urbanization is the driving force behind its economic development. Urban growth and development are closely associated with land use/land cover changes in any area, which sometimes negatively affect the livelihood and wellbeing of local people. We investigated the impacts of urban land use changes on the local people of northwestern Bangladesh using Nilphamari Sadar as a case study site. In order to identify land use patterns and variations in land cover, a 10-year period of land use/land cover changes from 2000 to 2020 was monitored using satellite imagery and the supervised classification method. Primary data were collected through household surveys and focus group discussions. Our analysis suggests that land use has changed over the past 20 years in the study area. As a result, the land area under settlements has substantially increased from 19 square kilometers in 2000 to 48 square kilometers in 2020. Waterbodies were 24 square kilometers in 2010, dropping to 4 square kilometers in 2020, whereas croplands decreased to 218 square kilometers in 2020 from 259 square kilometers in 2000. These changes have proportionately and adversely affected the lives and livelihoods of the local people by altering their longstanding traditional livelihood options, limiting their access to common resources, failing to adapt to new environmental arrangements and economic structures, and creating new forms of vulnerability. We suggest that urban planning processes should consider local people’s interests with the entitlement of local and marginalized people to the benefits generated by urbanization.

A multi-spectral image-based high-level classification based on a modified SVM with enhanced PCA and hybrid metaheuristic algorithm

In the context of Precision Agriculture (PA), an extremely fine crop classification has recently grown in importance. Satellite photos are often used to identify land use. Furthermore, this classification poses new difficulties since in addition to utilising the multi-temporal features of the multi-spectral images; it also calls for pixel-based examination and a greater number of trainings. Multiple machine learning methods are applied to multi-spectral and multi-temporal satellite data in this paper to create crop categorization models. The UAV platform is used for monitoring the crop field. The multispectral photo is caught as the intake figure which is taken out of the dataset. In precision agriculture, the pre-processing is performed for removing the noise content present in the input image. Feature section is done through PCA and the kernel modified SVM is used for classification. The implementation is performed using PYTHON platform. Paddy and Wheat are selected for the similarity. The accomplishment of the considered methodology is paralleled with and without the optimization, and also compared with the existing methods like Naïve bayes (NB), K- Nearest Neighbour (KNN), K- means and Random Forest (RF).

Comparative Study Between Archaic and Advance Technique to Evaluate the Impact of Mining Activities on Neighboring Environment

Abstract: Geo-material plays significant role in the expansion of social progress, industrial growth and treasure of all nations. Rocks are one of the most accessible natural resource and one of the most important building material for urban development. It has been used from the initial spells of our progress for a multiple uses that have increased in quantity and complexity with time. Stone is a major basic raw material applied in crushed form for construction. Minerals are non-renewable in nature and also limited as well and Rocks are the assemblage of minerals. A mineral formerly extracted from the earth were leftover forever so the suitable arrangement of use for minerals including exploration and exploitation should be implemented. Compared to traditional methods, new advanced methodologies are widely used for surveying, mapping and monitoring of these natural resources. Remote Sensing and GIS/ GPS technology including drone technology has been capable as native/ global survey using aerial photo and satellite imagery for temporal study. The multi-temporal nature of satellite imagery helps to monitor present and past situation of the earth features. Thus, provides a useful information to identify land use/ land cover changes and calculate the amount of change. Satellite images are a valuable source of data for surveying, mapping, and monitoring of terrestrial resources as compared to older approaches such as topography and aerial photography. The multi-date character of space technology integrated with drone technology allows to manage/ monitor the mining activities.

IDENTIFICATION OF CHANGES IN LAND USE IN THE KALASAN DISTRICT WITH A SPATIAL APPROACH

Abstract - Land use change is the change from the previous land use function to another function, one of which is the change from agricultural land to non-agricultural land. Land use change in Kapanewon Kalasan is driven by various community activities. This is due to the rapid population growth and the location of Kapanewong Kalasan, which is the center of crowds near Yogyakarta city. It is in Central Java and is traversed by the Yogyakarta-Surakarta road. The purpose of the study on land use change in Kapanewon Kalasan by spatial approach is to (1) identify land use change from 2010 to 2020, and (2) identify land use change based on the type of spatial approach for a specific time. The research method is a quantitative method by used spatial process analysis with the overlay technique on ArcGIS ArcGIS. The findings show that over the past decade, Kapanewon Kalasan land use increased by 78.1%, land use designated for dry farming increased by 23.1%, paddy field use increased by 90.5%. Overlay results show that many agricultural drylands are being converted to paddy fields and residential areas, thus the increase in agricultural dryland use is small. Keywords: Land use change, Spatial approach, Spatial process analysis, ArcGis, Overlays