Land Classification Research Articles

Analysis of Agricultural Land Classification in East Kotawaringin District, Central Kalimantan Province using Landsat 8-OLI Image

Purpose:This research aims to map agricultural land in East Kotawaringin Regency, Kalimantan Province, and analyze it using Landsat 8 OLI satellite imagery in 2023.Methodology:The method used in this research is supervised classification, which will then carry out a data analysis process related to natural conditions in the field. Based on the results of satellite image data processing, there are five categories of land cover classification in the East Kotawaringin Regency area: built-up land, empty land, fields/moors, gardens and forests.Findings:According to the accuracy test results, the overall accuracy value of satellite imagery in East Kotawaringin Regency is 87%. Community activities, such as economic activities and meeting needs for public facilities and housing, influence the distribution of existing land cover areas.Implication:The classification results obtained later through maps of agricultural land in East Kotawaringin Regency can be used to find various studies, especially on expanding the agricultural sector.

Prediction of Canopy Cover for Agricultural Land Classification in Land Parcel Identification System (LPIS) Data Using Planet-Scope Multispectral Images: A Case Study of Gelendost District

Prediction of Canopy Cover for Agricultural Land Classification in Land Parcel Identification System (LPIS) Data Using Planet-Scope Multispectral Images: A Case Study of Gelendost District

Assessing Soil and Land Suitability of an Olive–Maize Agroforestry System Using Machine Learning Algorithms

Exponential population increases are threatening food security, particularly in mountainous areas. One potential solution is dual-use intercropped agroforestry systems such as olive (Olea europaea)–maize (Zea mays), which may mitigate risk by providing multiple market sources (oil and grain) for smallholder producers. Several studies have conducted integrated agroforestry land suitability analyses; however, few studies have used machine learning (ML) algorithms to evaluate multiple variables (i.e., soil physicochemical properties and climatic and topographic data) for the selection of suitable rainfed sites in mountainous terrain systems. The goal of this study is therefore to identify suitable land classes for an integrated olive–maize agroforestry system based on the Food and Agriculture Organization (FAO) land suitability assessment framework for 1757 km2 in Khyber Pakhtunkhwa province, Pakistan. Information on soil physical and chemical properties was obtained from 701 soil samples, along with climatic and topographic data. After determination of land suitability classes for an integrated olive–maize-crop agroforestry system, the region was then mapped through ML algorithms using random forest (RF) and support vector machine (SVM), as well as using traditional techniques of weighted overlay (WOL). Land suitability classes predicted by ML techniques varied greatly. For example, the S1 area (highly suitable) classified through RF was 9%↑ than that of SVM, and 8%↓ than that through WOL. The area of S2 (moderately suitable) classified through RF was 18%↑ than that of SWM and was 17%↓ than the area classified through WOL; similarly, the S3 (marginally suitable) class area via RF was 27%↓ than that of SVM, and 45%↓ than the area classified through WOL. Conversely, the area of N2 (permanently not suitable class) classified through RF and SVM was 6%↑ than the area classified through WOL. Model performance was assessed through overall accuracy and Kappa Index and indicated that RF performed better than SVM and WOL. Crop suitability limitations of the study area included high elevation, slope, pH, and large gravel content. Results can be used for sustainable intensification in mountainous rainfed regions by expanding intercrop agroforestry systems in developing nations to close yield gaps.

KAJIAN STUDI GEOTEKNIK KLASIFIKASI TANAH PERMUKAAN UNTUK PERENCANAAN PEMBANGUNAN DIKAWASAN UNIVERSITAS SAMUDRA

Soil classification is a science that aims to study ways to differentiate soil properties and divide soil into classes based on general characteristics. The aim of this research is to determine the physical properties of the land surface and determine the classification of surface land in the Samudra University area to be mapped based on soil type using ArcGis. 38 undisturbed soil samples were taken using a hand drill and 38 soil samples were tested in the laboratory to determine the physical properties of the soil and to map research results for surface soil types using Arcgis with the kriging interpolation method. From the test results, the lowest water content was at point 35 with a value of 4 55% and the highest was at point 5 with a value of 62 64%. The lowest liquid limit was at point 1 with a value of 15 65% and the highest was at point 13 with a value of 47 99% and the lowest plasticity index was at point 5 with a value of 5 70% and the highest at point 3 with a value of 26 93% while the lowest filter analysis was at points 10 and 12 with a value of 0% and the highest was at point 5 with a value of 40 74%. From the research results, it was concluded that the soil types in the Ocean University area received 7 classification groups based on AASHTO with the most dominant group being group A.2-6 with 19 points.

Assessing forest fragmentation due to land use changes from 1992 to 2023: A spatio-temporal analysis using remote sensing data

The increasing pressures of urban development and agricultural expansion have significant implications for land use and land cover (LULC) dynamics, particularly in ecologically sensitive regions like the Murree and Kotli Sattian tehsils of the Rawalpindi district in Pakistan. This study's primary objective is to assess spatial variations within each LULC category over three decades (1992–2023) using cross-tabulation in ArcGIS to identify changes in LULC and investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0) to classify forest into several classes such as patch, edge, perforated, small core, medium core, and large core. Utilizing remote sensing data from Landsat 5 and Landsat 9 satellites, the research focuses on the temporal dynamics in various land classes including Coniferous Forest (CF), Evergreen Forest (EF), Arable Land (AR), Buildup Area (BU), Barren Land (BA), Water (WA), and Grassland (GL). The Support Vector Machine (SVM) classifier and ArcGIS software were employed for image processing and classification, ensuring accuracy in categorizing different land types. Our results indicate a notable reduction in forested areas, with Coniferous Forest (CF) decreasing from 363.9 km2, constituting 45.0 % of the area in 1992, to 291.5 km2 (36.0 %) in 2023, representing a total decrease of 72.4 km2. Similarly, Evergreen Forests have also seen a significant reduction, from 177.9 km2 (22.0 %) in 1992 to 99.8 km2 (12.3 %) in 2023, a decrease of 78.1 km2. The study investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0), revealing an increase in fragmentation and a decrease in large core forests from 20.3 % of the total area in 1992 to 7.2 % in 2023. Additionally, the patch forest area increased from 2.4 % in 1992 to 5.9 % in 2023, indicating significant fragmentation. Transition matrices and a Sankey diagram illustrate the transitions between different LULC classes, providing a comprehensive view of the dynamics of land-use changes and their implications for ecosystem services. These findings highlight the critical need for robust conservation strategies and effective land management practices. The study contributes to the understanding of LULC dynamics and forest fragmentation in the Himalayan region of Pakistan, offering insights essential for future land management and policymaking in the face of rapid environmental changes.

A dataset of vector and coverage of river and lake surface water in Guangxi, Yunnan, Guizhou (China), and the ASEAN member states

Runoff plays an important role in the water cycle, and information on the distribution of rivers in a watershed is essential for water resources, ecology, environment as well as socio-economic activities in a certain region. The region involving Guangxi, Yunnan and Guizhou in China, and the ASEAN member states is known for cloudy rainfall and distinct mountainous terrain. A comparative analysis of existing river datasets highlights the insufficient characterization of river information in this unique region. To obtain more accurate river distribution data for assessing water resources and the ecological environments, we integrated in this paper the Eurasian continental river vector data (2010) and the ESA land classification data (2020). Following this, we employed an expansion and contraction buffer analysis method to address the issue of river discontinuity. And in this way, we produced a dataset of river surface water in Guangxi, Yunnan and Guizhou in China and the ASEAN member states. After further processing the ESA land classification data, we derived a vector dataset of lake surface water encompassing Guangxi, Yunnan, and Guizhou in China, along with the ASEAN member states. Finally, we calculated the coverage of river and lake surface water and compiled a coverage dataset with a resolution of 1km grid across Guangxi, Yunnan, Guizhou in China, and the ASEAN member states. Comparing with the HydroRIVERS dataset and our water remote sensing datasets (Global Surface water, Esri Land Cover, Dynamic World V1, GRWL), this dataset excels in characterizing the river system in the study area, and provides richer details than the input datasets, as evidenced by the following. This dataset can fill in gaps in the mountainous tributary sections of the Eurasian continent's river vector data from 2010, and effectively address the issue of discontinuous and missing rivers. The dataset can be used to provide basic prior data for water extraction in Guangxi, Guizhou, Yunnan and the ASEAN state members, and it holds great value in flood forecasting and water resources management. Moreover, it can also serve social and economic activities such as ecological preservation, transportation planning, agricultural irrigation and energy use.

Evaluation of land suitability for sorghum crops in Lampasio District, Tolitoli Regency

Sorghum is an alternative food ingredient that contains high levels of carbohydrates and calories so it can be a solution to food security problems. This research aims to evaluate the suitability of land for sorghum (Sorghum bicolor L.) in Tolitoli Regency, Indonesia. This research was conducted in Lampasio District, Tolitoli Regency using purposive sampling and survey methods on five land map units (SPL). The results of the research show that the area is classified as quite suitable land class (S2) with limiting factors for drainage and nutrient retention (oa, nr) with a land area of 3,652.7 ha spread over 5 SPL, namely SPL 1,2,3 and 5 while class The land (S3) is SPL 4. However, this can be overcome by adding fertilizer and adding organic material. So that it can improve the status of each land to be very suitable (S1) and quite suitable (S2).

Creating territorialized sustainability indices to evaluate pollination

CONTEXTIdentifying sustainable practices and landscape features that enhance the abundance of organisms providing ecosystem services can be challenging due to a limited understanding of the link between these practices and ecosystem functioning. One approach to assessing system complexity is to incorporate reliable indices that are markers of an essential ecosystem regulation service such as insect pollination. OBJECTIVEConsolidate indicators of sustainable farming practices and landscape features into two indices that consider different spatial scales (regional and local) to categorize peri-urban farms within two gradients of territorialized environmental sustainability. These indices enable the assessment of agroecosystem sustainability for a particular ecosystem service (insect pollination), which is critical for many crops. METHODSThe regional-scale index utilized four indicators of composition and configuration that reflected the landscape's heterogeneity and its influence on key resources and bee movement. For these features, we used three land classes: semi-natural, farm, and extensive crops within a 3000-m radius circle from the centre of the farm. The local-scale index included eight indicators relating to the sustainability of farming practices derived from semi-structured interviews with the farmers. Additionally, we included an indicator based on the extent of semi-natural areas within a 100-m radius circle. RESULTS AND CONCLUSIONSBoth indices generated a synthesis to obtain two gradients (regional and local) of territorialized environmental sustainability for the farms. We validated these metrics through records of bee abundance on Cucurbita maxima flowers in each farm, as an indicator of the bee-pollination service. The variations of regional and local indices among and within the same farm (e.g., a high local-scale index and a low regional-scale index) emphasize the importance of considering both spatial contexts when evaluating ecosystem services. Both indices correlated with bee abundance, with the local-scale index exhibiting a positive stronger correlation than the regional (0.68 vs. 0.61 respectively). For farmers, making changes to farming practices at the local scale is more achievable than altering features at the regional scale, where government territorial planning and coordinated efforts among residents in the area are required. Consequently, enhancing a local-scale practice might be a more feasible way to increase farm sustainability. SIGNIFICANCEThe indices will enable a more straightforward comparison of sustainability among farms or within the same farm over time than analyzing each socio-environmental metric separately. Following the methodology proposed here, other indices for other essential ecosystem services in agroecosystems could be developed.

Connecting forest soil properties with ecosystem services: Toward a better use of digital soil maps—A review

AbstractThe soil supports many ecosystem services (ES) essential to human well‐being. Rapid developments in digital soil mapping (DSM) allow the mapping of soil types and soil properties with improved resolution and accuracy. However, the potential of DSM to improve the assessment and mapping of ES is not fully exploited. To better understand this potential, we synthesized the peer‐reviewed literature. We examined what empirical studies reveal about the role of soil properties in the assessment of four major ES provided by the forest: (I) timber production, (II) soil carbon storage, (III) regulation of water flow and provision of clean water, and (IV) the soil as a habitat for organisms. Results revealed that soil properties are strongly related to the provision of ES. Therefore, using DSM could greatly improve the assessment of the ES provided by forests. Several variables were related to specific ES regardless of region or ecosystem types, but others were found to be situation‐specific (climate and soil type) and need to be considered at the proper scale or within a proper land classification framework. DSM products have the potential to greatly improve the assessment of ES by turning qualitative relationships between soil and ES to quantitative ones. This could also lead to the discovery of new soil–ES relationships. For this potential to be realized, progress should be made in mapping the most crucial soil parameters with greater precision and in promoting the use of soil parameters in ES assessment.

Assessment of universal thermal climate index (UTCI) using the WRF-UCM model over a metropolitan city in India.

Rapid urbanization increases urban air temperature, considerably affecting health, comfort, and the quality of life in urban spaces. The accurate assessment of outdoor thermal comfort is crucial for urban health. In the present study, a high-resolution mesoscale model coupled with a layer Urban Canopy Model (WRF-UCM) is implemented over the city of Hyderabad (17.3850° N, 78.4867° E) to simulate urban meteorological conditions during the summer and winter period of 2009 and 2019. The universal thermal climate index (UTCI) has been estimated using the model-derived atmospheric variables and a human biometeorology parameter to assess the linkages between the outdoor environment and thermal comfort. Results revealed that during summer, the city experiences nearly 50h of very strong thermal stress, whereas about 120h of slight cold stress are experienced during winter. The urban area in Hyderabad expanded from 5 to 15% during the study period, leading to a 2.5℃ (2.8 ℃) increase in land surface temperature, and a 1.2 (1.9 ℃) rise in air temperature at 2m height and 1.5 (2.5 ℃) UTCI during summer (winter) time. The analysis reveals that the maximum UTCI values were noticed over built-up areas compared to other land classes during daytime and nighttime. The results derived from the present study have shown that the performance of WRF-UCM-derived UTCI reasonably portrayed the significant impact of urbanization on thermal comfort over the city and provided useful insights with regard to urban comfort and welfare.

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, Pakistan, employing the comparison between machine and deep learning models. Three satellite indices, namely, NDVI, MNDWI, and NDBI, were derived from Landsat-8 data and utilized to classify four primary categories: Built-up Area, Water Bodies, Barren Land, and Vegetation. The main objective of this study is to evaluate and compare the effectiveness of comparison of machine and deep learning models. The machine learning models including Random Forest achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.90. It accurately classified 2.7% of the area as Built-up Area, 1.9% as Water Bodies, 54.8% as Barren Land, and 40.4% as Vegetation. While slightly less accurate, Decision Tree model provided reliable classifications. Deep learning models showed significant accuracy, of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The CNN model achieved an impressive overall accuracy of 97.3%, excelling in classifying Water Bodies with User and Producer Accuracy exceeding 99%. The RNN model, with an overall accuracy of 96.2%, demonstrated strong performance in categorizing Vegetation. These findings offer valuable insights into the potential applications of machine learning and deep learning models for perfect land classifications, with implications for environmental monitoring management and geospatial analysis. The rigorous validation and comparative analysis of these models contribute to advancing remote sensing techniques and their utilization in land classification tasks. This research presents a significant contribution to the field and underscores the importance of precise land classification in the context of sustainable land management and environmental conservation.

A NOVEL TRANSFER LEARNING BASED DEEP MODEL FOR LAND CLASSIFICATION

A systematic framework for comprehending the qualities and possibilities of different land portions is provided by land classification, which makes it easier to make well-informed decisions and implement sustainable land management techniques across a range of industries. LULC has numerous significant uses in a variety of fields such as Urban planning, agriculture, natural resource management, environmental assessment, infrastructure development, disaster management, Tourism and recreation, Transportation planning, water resource management, etc. Contemporary trends show deep learning technology has achieved very good results in land classification and thus the land classification has become very attractive prospect for research and development. However, having a stable model in terms of training and testing will be the need of an hour. In this proposed system, we have utilized the high performing deep learning model to address the uncertainty prediction in the model. We have used Bayesian model to tackle the uncertainty prediction in the model. Our proposed system is marginally compromised the training and validation accuracy; However, results have shown that the loss curve generated from training and testing a stable model is substantially important to ensure it’s learning rate stability as well as general confidence in real-time production of the deep learning model.

Fine-resolution estimation for urban surface water pollution susceptibility with multi-modal earth observation data

The sustainability and suitability of water resources are of great importance for maintaining urban populations. The landscapes and environment around urban waters have always been the main focus of maintaining water quality for sustainable water supplies. Early-stage field investigations recognized the influence of land use/land cover (LULC) on water quality. To extend the research scope in spatial and temporal dimensions, remote sensing techniques have been utilized to discover the relationships between LULC and water quality. However, these remote sensing datasets generally had a medium spatial resolution, making them unable to support the fine-detailed land classifications that are critical to explore the water quality in an urban area. Moreover, although more details regarding the land surface are available from the currently-generated high-resolution and very-high-resolution remote sensing images, this land surface information is too complex for the state-of-the-art deep learning approaches and benchmark datasets. This manuscript reports our efforts on developing a framework to explore the fine-resolution relationship between surface water pollution and LULC. To address the cost of computing time and limitations of well-labelled datasets, we employ a foundation model-enhanced approach for water extraction and water-surrounded LULC classification. We propose an estimator of surface water pollution susceptibility to main pollutants based on the surrounding LULCs. Selecting the Future City of Beijing as the study area, based on very-high-resolution remote sensing images, the experiment proved that our proposed approach could effectively map the susceptibility of surface water pollution caused by its surrounding land use and land cover. To our knowledge, the relationship of LULCs and water quality have not been investigated using 0.5 m spatial resolution data. We hope our work can provide a prospective fine-detailed water quality analysis in the community of water environment of remote sensing.

Machine learning versus deep learning in land system science: a decision-making framework for effective land classification

This review explores the comparative utility of machine learning (ML) and deep learning (DL) in land system science (LSS) classification tasks. Through a comprehensive assessment, the study reveals that while DL techniques have emerged with transformative potential, their application in LSS often faces challenges related to data availability, computational demands, model interpretability, and overfitting. In many instances, traditional ML models currently present more effective solutions, as illustrated in our decision-making framework. Integrative opportunities for enhancing classification accuracy include data integration from diverse sources, the development of advanced DL architectures, leveraging unsupervised learning, and infusing domain-specific knowledge. The research also emphasizes the need for regular model evaluation, the creation of diversified training datasets, and fostering interdisciplinary collaborations. Furthermore, while the promise of DL for future advancements in LSS is undeniable, present considerations often tip the balance in favor of ML models for many classification schemes. This review serves as a guide for researchers, emphasizing the importance of choosing the right computational tools in the evolving landscape of LSS, to achieve reliable and nuanced land-use change data.

The Progress/Status of Ecological Assessment on the Intensive Land Use in Selenge and Darkhan-Uul Province, Mongolia

Abstract. The study area, it includes Darkhan-Uul and Selenge provinces of Mongolia, is included in the most favourable natural-geographical areas, and the migration of people with livestock from the peripheral areas has led to an increase in the population, as well as a sharp increase in the number of grazing animals, resulting in the effects of natural and human activities. give an evaluation, develop the basis for the proper use of the land in the future. In Mongolia, the methods and principles of land evaluation differ depending on the general classification and purpose of land, so considering these characteristics, land evaluation is carried out by (1) the Department of Agriculture, (2) the Department of Urban Development, Industry and Mines, (3) the Department of Roads and Networks, (4) it is divided into types of land with forest reserves (Tserenbaljir, B. Naranchimeg, 2004). In the "instructions for land evaluation" issued by the United Nations Food and Agriculture Organization (FAO) in 1976, in the assessment of land quality, in addition to the main indicators of soil fertility and moisture, climate, land cover and use, chemical pollutions such as an alkaline acidity. In the ecological assessment study, data on land use, soil, vegetation, climate, natural conditions, resources, socio-economic, satellite and field studies were collected in numerical and tabular form. The Ecological assessment in intensive land use is divided into qualitative and quantitative assessment. The Qualitative assessment predicts ecological properties. The Quantitative methods use multi-species numerical methods to record ecological elements and calculate the overall percentage of ecological characteristics.

Land-Water-GHG-Food Nexus performance and physical-socio-economic-policy factors influencing rice cultivation in Central Thailand

Agriculture activity contributes to greenhouse gas (GHG) emissions through its utilization of land, water, and energy for food production. Hence, the interactions between land, water, and GHG emissions in agricultural production need to be comprehensively studied. The study aimed to assess the Land-Water-GHG-Food Nexus Index (LWGFNI) of rice cultivation across various land suitability classes in Central Thailand and determining the physical, socio-economic, and policy factors that can influence farmers' decisions to choose for cultivating rice instead of shifting to other crops. The results indicated that the highest LWGFNI score was 0.69 for the rice grown in the moderate suitability land class which revealed a lower use of land and water resources as well as GHG emissions compared to other levels of land suitability. The LWGFNI scores of major rice cultivation were greater compared to the second rice in all four-land suitability. The use of fertilizers had a crucial role in enhancing productivity levels and was a significant factor in the generation of GHG emissions. Hence, improving effective production should consider the appropriate use of fertilizer. The physical, socio-economic, and policy-related aspects that significantly influenced farmers' decisions on cultivation of rice included topography, water resources, inherited professions, price guarantee, and knowledge/training factors. The methodology used and results obtained can help policy makers to plan the use of water and land resources efficiently and appropriately with local resources based on land suitability class. The assessment results revealed the GHG hotspots and the strategies to mitigate GHG emissions associated with rice cultivation.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Detailed Land Use Classification in a Rare Earth Mining Area Using Hyperspectral Remote Sensing Data for Sustainable Agricultural Development

In China, ion-adsorbing rare earth minerals are mainly located in the southern hilly areas and are important strategic resources. Extensive long-term mining has severely damaged the land cover in mining areas, caused soil pollution and terrain fragmentation, disrupted the balance between mining and agriculture, severely restricted agricultural development, and affected ecological development. Precise and detailed classification of land use within mining areas is crucial for monitoring the sustainable development of agricultural ecology in these areas. In this study, we leverage the high spatial and high spectral resolution characteristics of the Zhuhai-1 (OHS) hyperspectral image datasets. We create four types of datasets based on spectral, vegetation, red edge, and texture characteristics. These datasets are optimized for multifaceted features, considering the complex land use scenario in rare earth mining areas. Additionally, we design seven optimal combination schemes for features. This is performed to examine the impact of different schemes on land use classification in rare earth mining areas and the accuracy of identifying agricultural land classes from broken blocks. The results show that (1) the inclusion of texture features has the most obvious effect on the overall classification accuracy; (2) the red edge feature has the worst effect on improving the overall accuracy of the surface classification; however, it has a prominent effect on the identification of agricultural lands such as farmland, orchards, and reclaimed vegetation; and (3), following the combination of various optimization features, the land use classification yielded the highest overall accuracy, at 88.16%. Furthermore, the comprehensive identification of various agricultural land classes, including farmland, orchards, and greenhouse vegetables, yielded the most desirable outcomes. The research results not only highlight the advantages of hyperspectral images for complex terrain classification and recognition but also address the previous limitations in the application of hyperspectral datasets over wide mining areas. Additionally, the results underscore the reliability of feature selection methods in reducing information redundancy and improving classification accuracy. The proposed feature selection combination, based on OHS hyperspectral datasets, offers technical support and guidance for the detailed classification of complex land use in mining areas and the accurate monitoring of agroecological environments.

The identification and efficiency evaluation of industrial parcels by integrating multi-source spatial data in Shenzhen, South China

ABSTRACT Industrial land status, encompassing quantity, distribution and efficiency, is pivotal in urban planning and sustainable development. However, obtaining detailed and up-to-date industrial land maps poses a significant challenge in many developing countries due to rapid urban expansion. Furthermore, the identification and efficiency assessment of industrial land at the parcel scale are rarely undertaken. To address these challenges, our study proposes an innovative approach to map industrial land, categorize its types and assess its efficiency at the parcel scale. We select Shenzhen, one of China’s largest industrial cities, as a case study to demonstrate the effectiveness of our method. Our results unveil several key findings: (1) Shenzhen’s urban area was meticulously segmented into 15,591 multi-size socio-economic parcels, of which 5,738 were identified as industrial parcels (IPs); (2) IPs were further categorized into three distinct types: office industrial parcels at 69.47%, manufacturing industrial parcels at (6.08%) and comprehensive industrial parcels at 24.45%; (3) Their efficiency was classified into four levels: low (20.30%), middle (35.73%), fine (35.97%) and high (8.00%). Efficient IPs were predominately concentrated in the southwest, whereas other IPs clustered in the northwest. Our findings can provide valuable insights into industrial land identification, classification and efficiency evaluation at parcel scale.