Land Mapping Research Articles

Spatial analysis and classification of land use patterns in Lucknow district, UP, India using GIS and random forest approach

Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km2 in built-up areas. A net decrement of 75.44 km2 was observed in forest areas. A drastic net decrease of 674.52 km2 was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.

Assessing the transferability of a multi-source land use classification workflow across two heterogeneous urban and rural areas

ABSTRACT Mapping Land Use (LU) is crucial for monitoring and managing the dynamic evolution of the human activities of a given area and their consequential environmental impacts. In this study, a multimodal machine learning framework, using the XGBoost classifier applied to attributes constructed from heterogeneous spatial data sources, is defined and used to automatically classify LU in the two French departments of Gers and Rhône. It reaches a mean F1 score of 83 and 86% respectively. This research work also assesses the robustness and transferability of the machine learning model between these two diverse study areas and highlights the challenges encountered, arising mainly from the differences of distribution of the attributes and classes between the study areas. Adding a few samples from the test study area allows the model to learn some specificities of the test study area, and thus improves the results. Moreover, the study evaluates the individual contributions of each data source to the accuracy of predictions of the LU classes, providing insights concerning the relevance of each data source in enhancing the overall precision of the Land Use classification. The findings contribute to a validated LU classification workflow, identify valuable data sources, and enhance understanding of model transferability challenges.

Inovasi Agroteknologi untuk Meningkatkan Produktivitas dan Keberlanjutan Pertanian di Kepulauan Nias

This research aims to explore the application of agrotechnology innovations in improving agricultural productivity and sustainability in Nias Islands. The main challenges of the agricultural sector in this region include limited resources, climate instability and minimal access to technology. The research used a mixed approach (qualitative and quantitative) with surveys, in-depth interviews and field observations. Results show that micro-irrigation technology and waste-based organic fertilizer are the most widely adopted innovations. These technologies can increase crop productivity by up to 30%, although there are still obstacles in the adoption of advanced technologies such as satellite image-based land mapping due to limited infrastructure and farmer knowledge. The research also identified positive socio-economic impacts, including improved farmer welfare and land sustainability. In conclusion, the development of simple technologies such as micro-irrigation and organic fertilizer offers great potential to improve production efficiency, provided there is close collaboration between the government, academia and local communities. Key recommendations include improved access to technology, intensive training, and integration of local wisdom in sustainability strategies.

Mapping land cover in the low Arctic using multiple sources of earth observation data

Land cover maps in high latitude regions are important for permafrost modeling and mapping and other climate change related studies. National and global land cover products cannot meet the requirements for regional permafrost mapping due to their broad land cover types. This study produced a land cover map in a low Arctic region using multi-source earth observation data, including different modes of RADARSAT-2 (RS2), Sentinel 2 (S2), and the high resolution ArcticDEM. We assessed the performances of two machine learning classification algorithms (Random Forest (RF) and Support Vector Machine (SVM)) with different combinations of input data, including evaluating the effects of incidence angle from different RS2 modes on classification results. This study concludes that the combination of two imaging modes of RS2, S2 composites and the high resolution ArcticDEM achieves a more accurate land cover map (the overall accuracy is 93.6%) than other combinations with the overall accuracies ranging from 38% to 87%; the performance of RF is better than that of SVM, the overall accuracy difference ranges from 21% to 1%; and the shallow SAR incidence angles outperform steep ones in classification results of different combinations for both classifiers.

Unveiling the Vulnerability: Mapping Desertification and Land Degradation in the Eastern fringes of the Thar Desert

This study focuses on identifying the key drivers of desertification in the Sirsa district of Haryana, India. It takes into account several factors, such as climate, soil, regional hydrology, vegetation condition, land use, available amenities, and economic conditions. Using a hierarchy-based model within a geographic information system framework, these parameters were integrated to create the Desertification-Land Degradation Vulnerability Index (DLVI). The analysis categorizes the results into five vulnerability zones-very high, high, moderate, low, and very low-based on their relative susceptibility to desertification and land degradation. The findings highlight that areas experiencing lower rainfall, higher temperatures, and greater population density with limited social amenities face a higher risk of desertification, particularly in the south-southwest and western parts of Sirsa. To validate the DLVI map, Land Degradation Status Maps are used, employing the Receiver Operating Curve and the Area Under the Curve. This validation process demonstrates an accuracy rate of 61.6%. The model-based approach, which integrates various factors encompassing the geo-environmental and socio-economic aspects, offers valuable insights for the formulation of effective mitigation strategies to combat land degradation and desertification in the future.

Mapping Land Use and Assessing Coastal Urbanization Impacts: A Case Study in Tangier Northern Morocco

Mapping Land Use and Assessing Coastal Urbanization Impacts: A Case Study in Tangier Northern Morocco

Assessing the efficacy of artificial intelligence based city-scale blue green infrastructure mapping using Google Earth Engine in the Bangkok metropolitan region

Urbanization disrupts natural water, energy, and nutrient cycles, but integrating green and blue infrastructures (BGI) can mitigate these effects by facilitating processes like evapotranspiration, soil water infiltration, and root nutrient absorption. In Bangkok Metropolitan Region (BMR), escalating urbanization poses challenges to these natural cycles. Mapping Land Use and Land Cover (LULC) and identifying green infrastructure locations are crucial for effective urban planning, sustainable development, and environmental conservation amidst rapid urban growth. Continuous monitoring of dynamic urban areas is time-consuming, labor-intensive, and costly. Previous research primarily focused on land use and land cover classification followed by BGI identification. An automated classification system focusing solely on BGI can greatly enhance efficiency, accuracy, and cost-effectiveness in land classification for urban planning and decision-making. However, automating this system remains a significant challenge for the remote sensing community. Therefore, the research is first to develop a cloud based artificial intelligence tools such as Smile Random Forest and Smile CART integration with Google Earth Engine (GEE) to identify BGI for BMR. For this analysis, we have used open-sources and mostly used satellite images (e.g., Landsat and Sentinel) and consider to analyze seasonal changes for waterbodies, natural vegetations and human intervened vegetations with developing cloud-based artificial intelligence (AI). Surprisingly, Landsat-9 data demonstrated superior accuracy compared to Sentinel-2, indicating that the advanced technology of Landsat 9 may be more effective for BGI classification using AI. The study revealed a most distinct transition from deep green to green infrastructures during the transition from summer to monsoon season, whereas significant changes in blue infrastructure occurred between the monsoon and winter seasons. Seasonal variations in BGI are complex and influenced by factors such as the types of BGI implemented and the nuances of local climatic conditions. These advancements could provide precise insights for urban managers and policymakers, offering valuable tools to identify and understand BGI dynamics across various urban scales.

Study of The Erosion Hazard Level (EHL) of Nagari Lawang, Matur District, Agam Regency

Abstract This research aimed to determine the erosion hazard level (EHL) of Nagari Lawang. Determination of EHL was approached by using the (USLE) Universal Soil Loss Equation based on land map units, with conservation direction based on the level of erosion hazard that occurred. The research showed that the Nagari Lawang area’s Erosion Hazard Index (EHI) was dominated by the very light category, with an area of 727.58 ha (52.75%), with an average erosion rate of 3.32 tons/year. In regions with EHI with the light, medium and heavy categories, the total area was 651.6 ha (47.25%), including worrying erosion, because the erosion had exceeded the permitted erosion.

Dynamics of Irrigated Land Expansion in the Ouémé River Basin Using Field and Remote Sensing Data in the Google Earth Engine

The availability of reliable and quantified information on the spatiotemporal distribution of irrigated land at the river basin scale is an essential step towards sustainable management of water resources. This research aims to assess the spatiotemporal extent of irrigated land in the Ouémé River basin using Landsat multi-temporal images and ground truth data. A methodology was built around the use of supervised classification and the application of an algorithm based on the logical expression and thresholding of a combination of surface temperature (Ts) and normalized difference vegetation index (NDVI). The findings of the supervised classification showed that agricultural areas were 16,003 km2, 19,732 km2, and 22,850 km2 for the years 2014, 2018, and 2022, respectively. The irrigated land areas were 755 km2, 1143 km2, and 1883 km2 for the same years, respectively. A significant increase in irrigated areas was recorded throughout the study period. The overall accuracy values of 79%, 82%, and 83% obtained during validation of the irrigated land maps indicate a good performance of the algorithm. The results suggest a promising application of the algorithm to obtain up-to-date information on the distribution of irrigated land in several regions of Africa.

Electronic Certificates in Indonesia: Enhancing Legal Certainty or Introducing New Challenges?

The Indonesian government is moving towards digital transformation in land administration by introducing electronic certificates (e-certificates) through the Regulation of the Minister of ATR/BPN Number 1 of 2021. This initiative aims to improve the security and legal certainty of land ownership, building upon existing programs like the Complete Systematic Land Registration Program (PTSL). This study used a normative legal research method to investigate the opportunities and challenges associated with implementing e-certificates in Indonesia. Primary legal sources, such as the Electronic Information and Transactions Law (UU ITE) and relevant ministerial regulations, were analyzed. Secondary data from legal journals, academic publications, and theses were also examined. The study revealed that e-certificates offer several advantages, such as increased efficiency in land administration, potential integration with blockchain technology, and improved land mapping and survey processes. However, challenges remain, including cybersecurity risks, limited technological infrastructure, and the digital literacy gap. Furthermore, the legal status of e-certificates as evidence in court, while recognized by the Ministry of ATR/BPN and UU ITE, requires further clarification within the Indonesian legal framework. This study concludes that e-certificates hold the potential to modernize land administration and enhance legal certainty in Indonesia, but successful implementation requires addressing critical challenges and ensuring comprehensive legal and regulatory clarity.

Surveying, mapping, and translating: how property rights were created in Shanghai, 1860–1900?

Abstract From 1860 to 1900, a modern system of property rights emerged in the International Settlement in Shanghai. This paper examines the largely overlooked process by which the Shanghai Municipal Council (SMC) brought about a system of well-functioning property rights through land surveys, mapping, and assessments. These methods worked hand-in-hand with road planning and construction in facilitating the expansion of the International Settlement to the Chinese-controlled area. Colonial officials, merchants, and Chinese intellectuals worked collectively and sometimes separately to generate knowledge about land and property by translating terms in the Chinese tradition. It argues that the efforts in institution-building and translation helped normalize the definition of property rights as things exclusively owned, strengthening the SMC's control over the land in Shanghai. These processes illuminate the relationship between empire-making and property rights by showing how property rights emerged and functioned in a semi-colonial context where multiple foreign authorities coexisted with the local government. The relatively secure system of property rights, which both foreign and Chinese merchants exploited, formed the foundation of a prosperous Shanghai in the twentieth century.

Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection

The land use land cover (LULC) map is extensively employed for different purposes. Machine learning (ML) algorithms applied in remote sensing (RS) data have been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that random forest (RF) and support vector machine (SVM) consistently achieve high accuracy for land classification. Considering the important role of Portugal’s Serra da Estrela Natural Park (PNSE) in biodiversity and nature conversation at an international scale, the availability of timely data on the PNSE for emergency evaluation and periodic assessment is crucial. In this study, the application of RF and SVM classifiers, and object-based (OBIA) and pixel-based (PBIA) approaches, with Sentinel-2A imagery was evaluated using Google Earth Engine (GEE) platform for the land cover classification of a burnt area in the PNSE. This aimed to detect the land cover change and closely observe the burnt area and vegetation recovery after the 2022 wildfire. The combination of RF and OBIA achieved the highest accuracy in all evaluation metrics. At the same time, a comparison with the Normalized Difference Vegetation Index (NDVI) map and Conjunctural Land Occupation Map (COSc) of 2023 year indicated that the SVM and PBIA map resembled the maps better.

Multi-source environmental diversity mapping to assess potential level of soil degradation on coffee plantations: Ende Regency, East Nusa Tenggara

Abstract This study investigates the potential level of soil degradation from coffee plantation land use in the Ende district of Flores, East Nusa Tenggara. Given its significant economic role in the region, coffee cultivation has been noted for its detrimental impact on soil quality and health. The main goal of this study is to thoroughly assess the level of soil degradation and determine the key factors driving it. The methodology utilizes a scoring system based on land map units overlaid with thematic maps encompassing administrative boundaries, soil types, rainfall distribution, land use patterns, and slope gradients. Over the past six years, coffee productivity in Ende Regency has consistently declined, averaging 6.49 tons per year. The findings indicate that a substantial portion of Ende Regency, encompassing approximately 107,977.90 hectares, falls within the low potential for soil damage category. Recommendations for mitigation include adopting enhanced land management practices and implementing integrated pest and disease control strategies in coffee cultivation. This study aims to understand sustainable agricultural practices in coffee production contexts, emphasizing the importance of balancing economic development with environmental stewardship in fragile ecosystems like those found in Ende Regency.

Physicochemical characteristics of andisols and their correlation to potato yield based on land mapping units

The high phosphate retention in andisol soil is often associated with a decrease in potato yield. Additionally, identification of the physical and chemical characteristics of andisols and their correlation to potato yield through soil mapping units (SMUs) is necessary to facilitate field surveys. Therefore, this study aimed to identify the physicochemical characteristics of andisols with a focus on available phosphorus (P) and the correlation to potato production based on SMUs. The experiment was conducted in Karo District, North Sumatra, Indonesia, from July 2022 to February 2023. A descriptive-analytical method was used by overlaying maps of slope, soil types, and altitude until 10 SMUs were selected that were planted with potatoes. Soil physicochemical properties were identified, while Pearson correlation analysis was performed on available-P and potato yield using IBM SPSS software. The results showed that the silt and clay fractions positively correlated with available-P in andisols. All soil physical characteristics were categorized as very weak to weak in supporting potato yield. A total of four SMUs including 1, 5, 8, and 10 were found to have lower available-P and very low to moderate correlations with soil pH, organic-C, C:N ratio, cation exchange capacity (CEC), as well as total-P. Based on the results, potato yield could increase by 1.765 and 0.380 tons ha-1 through the addition of organic-C and C:N ratio in Karo District. Therefore, soil amendment is required as an alternative to improve andisols soil.

Land use and land cover mapping of the Saco River’s watershed, State of Maranhão, Brazil

Land use and land cover mapping benefits landscape understanding and its changes, especially those resulting from anthropogenic actions in the physical environment. The present study aimed at mapping land use and coverage of the Saco River’s watershed (located in Codó, state of Maranhão). The area comprises the coconut forest (named Mata dos Cocais), a region that encompasses the characteristics of different biomes on the same temporal and spatial scale. The methodology applied was based on remote sensing techniques developed in a Geographic Information System (GIS) environment, processing data from Landsat 8 and supervised classification. The results showed a predominance of dense vegetation in the basin under study, in addition to a reduced occupation by classes of urban area and water bodies. The overall accuracy was 79% and dense vegetation presented a higher user and producer accuracy than the general, with 91 and 87%, respectively. On the other hand, the highest commission and omission errors were those in urban areas and water bodies, which coincided with the lowest occupation classes in the Saco River basin. These results are pioneering for the coconut forest and provide data for strategic planning of environmental actions.

The application of remote sensing and geographic information system techniques for the analysis of land use and land cover changes in Wayanad district of Kerala

Mapping Land Use and Land cover Changes (LULC) and detecting changes using remote sensing and Geographic Information System (GIS) techniques is a cost-effective way of gaining a good understanding of the land cover alteration processes caused by land use change and their effects. This study assessed the transformation of the Wayanad district landscape over a period of 23 years. LANDSAT satellite images (of 30 m resolution) encompassing the area at three epochs were classified into nine classes (coffee dominated mixed crop, built-up, evergreen forest, deciduous forest, grassland, mixed crop with built-up, paddy, tea plantation, and waterbody) using the maximum likelihood algorithm, resulting in classes for each land use. The results showed that over the past 23 years, coffee-dominated mixed crops have increased by 0.84% in 2014 and 11.84% in 2022 compared to 1999; deciduous forest area decreased 3.6% and increased 0.6% in 2014 and 2022, respectively. Tea plantations increased by 0.9% in 2014 and by 0.49% in 2022, which decreased by 0.41% compared to 2014. Built-up has increased by 6.42% and 6.02% in 2014 and 2022 respectively, and slightly decreased by 0.4% compared to 2014. Evergreen forest areas increased in 2014 by 6.68% and 2.28% in 2022 and decreased by 4.44% compared to the previous time period. Grass land areas have decreased by 6.25% and 5.33%, respectively, and mixed crops with built-up areas has decreased by 0.74% and remained the same in the last two epochs, while paddy and waterbodies have decreased by 3.4%, 15.79%, and 0.07 and 0.19%, respectively, in 2014 and 2022 of the total geographical area Keywords: Remote sensing, GIS techniques, LULC, forest cover

Utilization of Open Polygon Method for Land Mapping in Mojoroto Kediri with Geospatial Approach

Land mapping is an important research activity to determine the location of points on the earth's surface, describing the physical condition of parts of the surface that resemble the actual condition. The objective of this process is to gain a comprehensive understanding of the land to be built and used through land area measurements, topographic maps, and land volume analysis at various altitudes. In this mapping, the open polygon method is used for calculations which are conventional methods using data point values as data centers to represent the area of influence. During this mapping, there are three main points used with the open polygon method. A total of 26 ground contours were recorded, and 4 road contours recorded. During the mapping process, corrections are required on the open polygon calculations to ensure the accuracy of the results. This correction involves adjusting the data to take into account field variability and inaccuracies of measuring instruments, so that the resulting data can be reliable for further analysis and correct decision-making. The land that has been painted can be used for various fields such as construction, and mining. Land mapping allows partial surface physical state imaging, classification of land, and can be used to plan, build, and maintain infrastructure such as highways and bridges. The result of this practice obtained data of the maximum height of 90,0945 masl and minimum 88,76 masl with the average ground height 90,05059 masl, while the average highway height is at 88,86175 masl.

Hyperspectral imagery quality assessment and band reconstruction using the prophet model

AbstractIn Hyperspectral Imaging (HSI), the detrimental influence of noise and distortions on data quality is profound, which has severely affected the following‐on analytics and decision‐making such as land mapping. This study presents an innovative framework for assessing HSI band quality and reconstructing the low‐quality bands, based on the Prophet model. By introducing a comprehensive quality metric to start, the authors approach factors in both spatial and spectral characteristics across local and global scales. This metric effectively captures the intricate noise and distortions inherent in the HSI data. Subsequently, the authors employ the Prophet model to forecast information within the low‐quality bands, leveraging insights from neighbouring high‐quality bands. To validate the effectiveness of the authors’ proposed model, extensive experiments on three publicly available uncorrected datasets are conducted. In a head‐to‐head comparison, the framework against six state‐of‐the‐art band reconstruction algorithms including three spectral methods, two spatial‐spectral methods and one deep learning method is benchmarked. The authors’ experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands. In addition, the authors assess the classification accuracy utilising these reconstructed bands. In various experiments, the results consistently affirm the efficacy of the authors’ method in HSI quality assessment and band reconstruction. Notably, the authors’ approach obviates the need for manually prefiltering of noisy bands. This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.

Land Suitability Analysis and Mapping Using Geospatial Technique and Multi Criteria Decision Analysis for Urban Green Area of Goba Town, Oromia, Ethiopia

Urban green areas have a significant role in improving human well-being in a variety of ways, providing ecosystem services, optimizing the health of urban residents, and making a great contribution to environmental health. However, there are very limited studies or not yet conducted on urban green area suitability analysis using geospatial technique and MCDA that support decision-makers, urban green area planners, and managements. The objective of this study is to analyze the factors that determine land suitability for urban green areas and to develop an urban green area suitability map for Goba Town using geospatial technique and the MCDA-AHP approach. In this study, GIS-based multi-criteria analysis (MCA) has been adopted to select suitable sites for urban green areas. The methodology involves a structured framework to identify and prioritize areas suitable for urban green development based on six key determinant factors: viz., land use land cover (LULC), elevation, proximity to settlements, distance to main rivers, slope, and distance to roads. The generated suitability map was validated by comparing it to existing green areas and conducting field surveys to verify the model's accuracy. The results of the study revealed that about 877.92 ha (18.31%), 1506.46 ha (31.41%), 1280.76 ha (26.71%), 756.05 ha (15.76%), and 373.71 ha (7.79%) were very low, low, moderate, high, and very high, respectively. The finding of the current study supports, as a baseline, assisting local governments and environmental agencies in sustainable urban development and forming resilient cities in advance of environmentally dynamic dynamics. Inclusion GIS and MCDA-based land suitability analysis provides a comprehensive framework for urban green area planning. Future studies might recommend using ecosystem service valuation as a factor in determining land suitability for green areas. Furthermore, various stakeholders need to work on the management of urban green areas and the community's awareness creation on urban green area ownership.

Variation of Soil Erosion Estimates when Using Different Maps of Arable Land of the Belgorod Region

Current medium- and small-scale estimates of soil erosion in Russia are very few. At the same time, a favorable situation has now developed for assessing the rates and volumes of soil erosion losses. Erosion models have been developed that are adapted to available digital elevation models, various farmland masks and climate databases have been created. The paper studies the accuracy of erosion estimates using various maps of arable land. Two maps are public (ESA WC, GLCLU), the third is the official Ministry of Agriculture (MA) of the Russian Federation, the fourth map is an author’s reference map of Alekseevsky district. It has been established that the map of the MA gives the most average arable land areas among the first three maps. Public access maps showed maximum and minimum estimates of arable land area. Comparison with the standard showed that the accuracy of the map of the MA does not exceed 90%, the remaining maps – 84 and 83%. The area of arable land in the Belgorod region varies slightly (from 1,445 to 1,586 thousand hectares); so the region is favorable for erosion modelling. Deviations from the average rates of soil erosion calculated using different maps of arable land in the region as a whole amounted to 7%, and in some areas reached 27%. Thus, today assessments of soil erosion at the regional level can be carried out with an error of at least 10–15% only as a result of the uncertainty in mapping the boundaries of arable land. In the Russia as a whole, data on the area of arable land varies significantly, from 80 to 132 million hectares. Consequently, the use of existing maps of arable land can lead to significant uncertainties in soil erosion estimates averaged at the level of districts and above.