Advancements in High-Resolution Land Use Mapping

Determination of the spatio-temporal distribution of Land use and Land cover (LU/LC) is important to understand the dynamics of urbanization, agricultural abandonment, and industrialization. This study aims to create multi-temporal high-resolution LU/LC maps and analyze thematically extensive LU/LC changes using Geographic Object-Based Image Analysis (GEOBIA) and Landscape Metrics for the selected study region in the Istanbul metropolitan city of Turkey. HR SPOT 6/7 images acquired in 2009, 2013, and 2019 were used as main Earth Observation data to create LU/LC maps. Open-source geospatial data were also integrated into classification to better identify some LU/LC classes to increase total classification accuracy. Overall classification accuracy of 2009, 2013, and 2019 dated LU/LC maps are 87.45%, 88.16%, 90.74% respectively. Principal Component Analysis (PCA) and Pearson correlation were used to selecting the landscape metrics and evaluate the results. PCA resulted in three principal components and the total variance was found as 87.3%.

Abstract. The concept of plant functional types (PFTs) is shown to be beneficial in representing the complexity of plant characteristics in land use and climate change studies using regional climate models (RCMs). By representing land use and land cover (LULC) as functional traits, responses and effects of specific plant communities can be directly coupled to the lowest atmospheric layers. To meet the requirements of RCMs for realistic LULC distribution, we developed a PFT dataset for Europe (LANDMATE PFT Version 1.0; http://doi.org/10.26050/WDCC/LM_PFT_LandCov_EUR2015_v1.0, Reinhart et al., 2021b). The dataset is based on the high-resolution European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset and is further improved through the additional use of climate information. Within the LANDMATE – LAND surface Modifications and its feedbacks on local and regional cliMATE – PFT dataset, satellite-based LULC information and climate data are combined to create the representation of the diverse plant communities and their functions in the respective regional ecosystems while keeping the dataset most flexible for application in RCMs. Each LULC class of ESA-CCI is translated into PFT or PFT fractions including climate information by using the Holdridge life zone concept. Through consideration of regional climate data, the resulting PFT map for Europe is regionally customized. A thorough evaluation of the LANDMATE PFT dataset is done using a comprehensive ground truth database over the European continent. The assessment shows that the dominant LULC types, cropland and woodland, are well represented within the dataset, while uncertainties are found for some less represented LULC types. The LANDMATE PFT dataset provides a realistic, high-resolution LULC distribution for implementation in RCMs and is used as a basis for the Land Use and Climate Across Scales (LUCAS) Land Use Change (LUC) dataset which is available for use as LULC change input for RCM experiment set-ups focused on investigating LULC change impact.

Land use and land cover (LULC) change plays a key role in the process of land degradation and desertification in the Horqin Sandy Land, Inner Mongolia. This research presents a detailed and high-resolution (30 m) LULC change analysis over the past 16 years in Ongniud Banner, western part of the Horqin Sandy Land. The LULC classification was performed by combining multiple features calculated from the Landsat Archive products using the Support Vector Machine (SVM) based supervised classification approach. LULC maps with 17 secondary classes were produced for the year of 2000, 2009, and 2015 in the study area. The results showed that the multifeatures combination approach is crucial for improving the accuracy of the secondary-level LULC classification. The LULC change analyses over three different periods, 2000–2009, 2009–2015, and 2000–2015, identified significant changes as well as different trends of the secondary-level LULC in study area. Over the past 16 years, irrigated farming lands and salinized areas were expanded, whereas the waterbodies and sandy lands decreased. This implies increasing demand of water and indicates that the conservation of water resources is crucial for protecting the sensitive ecological zones in the Horqin Sandy Land.

Land use and land cover (LULC) in coastal areas is critical in shaping the ecological systems, regional economy, and livelihood of indigenous communities. This study analyzes LULC changes (LULCC) in Soc Trang Province, Vietnam Mekong Delta, from 2010 to 2020 and simulates future LULC for 2030 under four scenarios: natural growth (business as usual, BAU), climate change challenges, profit optimization, and adaptation strategies. Satellite-based LULC maps and geospatial datasets were integrated into a LULC simulation model based on a Markov Chain and Cellular Automata to predict LULC in 2030 under disparate scenarios. Simultaneously, this study also estimates economic values and ecosystem service values as proxies to evaluate benefits and trade-offs between the scenarios. The research findings reveal that the critical LULCC observed during 2010–2020 are transitions from triple rice crops to double rice crops, rice–shrimp to brackish aquaculture, and expansion of perennial plantations. These transitional trends will persist at a modest rate under the BAU scenario in 2030. The climate change challenge scenario will intervene up to 24.2% of the total area, with double rice crops reaching the most extensive area compared to other scenarios, about 106,047 ha. The profit optimization scenario will affect 16.03% of the total area, focusing on aquaculture expansion to the maximum shared proportion of 34% (approximately 57,000 ha). Adaptive solutions will emphasize reducing triple rice crops while expanding double rice crops and reviving rice–shrimp to different extents depending on development pathways. Economic evaluations show a growth trend across scenarios, with maximum returns under profit optimization. Yet, ecosystem service values notably highlight ecological trade-offs, raising concerns about balancing economic benefits and ecological trade-offs in land use planning. The research findings recommend a comprehensive and multitarget approach to land use planning that integrates ecosystem services into initial assessments to balance benefits and trade-offs in coastal areas commonly affected by LULCC. By adopting well-informed and strategic land use plans that minimize ecological and social impacts, local sustainability and resilience to climate change can be significantly enhanced.

Declared protected areas have ecologically important landscapes that must be conserved and protected. Status of protected areas could be monitored through land use and land cover (LULC) assessments. LULC offers baseline data for integrated land use planning and improvement of existing policies are therefore necessary to be conducted. This study was conducted to monitor the existing LULC of six islands within the Batanes Protected Landscapes and Seascapes (BPLS) through a machine learning (ML)-based random forest (RF) classifier using multi-sourced data such as Landsat imageries’ surface reflectance (SR), Landsat-derived land surface temperature (LST), and global ecosystem dynamic investigation (GEDI)-derived height (Ht) metrics and to determine the effects of the LST and Ht metrics to LULC classification. Four layer stacked images with different features were analyzed – including SR, SR-LST, SR-Ht, and SR-LST-Ht. The result of the LULC classification showed an accuracy based on Macro F1-score and Kappa (K) of 0.81 and 0.83, 0.83and 0.86, 0.86 and 0.89, and 0.93 and 0.94, for SR, SR-LST, SR-Ht, and SR-LST-Ht, respectively. When compared to the existing global-scale LULC, this study has higher accuracy than the GLAD and ESRI products, which have Macro F1-scores and K-values of 0.73 and 0.71, and 0.59 and 0.64, respectively. To conclude, the inclusion of LST and Ht information in addition to SR data in LULC classification can improve the accuracy by up to 12% and 11% based on Macro F1-score and K,respectively. The result of this study can serve as a reference for achieving improved and reliable LULC information that is necessary for monitoring fluctuations of the global earth’s resources and comprehensive LULC planning. In addition, the technique used in this study can serve as a reference in generating reliable LULC information that can aid in the sustainable implementation of policies, rules, and regulations intended for declared protected areas like BPLS.

Application of high resolution land use and land cover data for atmospheric modeling in the Houston–Galveston Metropolitan area: Part II: Air quality simulation results

The application of remote sensing (RS) and Geographic Information System (GIS) have become an effective tool, which is labor, cost and time effective to assess and widely used in detecting, monitoring environmental change on the earth surface. Moreover, it can be used to study a pattern of land use and land cover (LULC) in the past, present and future. For this study, Landsat imageries were used as a data to deal with the assessment of land use and land cover changes (LULCC) before and after 3 flooded periods in 1999, 2006 and 2013. The images were used to create a false color composite and classified by supervised classification process, it can be identified as 9 LULC types, which consist of paddy field, field crop, para rubber, orchard, aquaculture, forest, mangrove, urban and built-up area, including water body. According to the result of this study, LULC types which mainly cover in the watershed are forest and orchard. After that, LULC were reclassified into 4 main classes for change detection, comprising of agricultural, forest, urban and built-up, and water bodies. The results showed LULC transformation mainly occur among agricultural land into the urban built-up area in the period 1999, 2006, and 2013. As a result, this study found that the socioeconomics factor plays an important role in LULC in Chanthaburi watershed. The results of this study can be used as data for making decision and planning LULC management, also in disaster response planning and flood risk management.

The goal of this study was to understand land use and land cover (LULC) changes within the lower uMfolozi floodplain system, South Africa, and relate those changes to wetland loss. Changes in LULC were assessed using a geographic object-based image analysis (GEOBIA) algorithm to classify multi-date Landsat images into eight cover types over a period of 20 years, between 1997 and 2017. Post-classification accuracy assessment of all map-outputs was conducted by compiling confusion matrixes and calculating producer, user, and global accuracies and kappa coefficients (K) for each map-output. Levels of accuracy for all map-outputs were within acceptable limits, ranging between 79% and 88% (K = 0.76 and 0.86, respectively). Thereafter, paired t-tests were applied to determine whether the changes in LULC over the study period were significant. Results of this investigation showed a significant (p-value, < 0.01) conversion of wetland to cultivation, by 14%. This finding is important because it demonstrates that in this environment, human agency is one of the major drivers of a persistent decrease in the wetland ecosystem. The major insight from this observation is that there is an urgent need to formulate and implement objectively informed interventions to enhance the sustainability of the uMfolozi floodplain system and that of others elsewhere.

Analyzing land use and land cover (LULC) is crucial for understanding community development and evaluating changes in the Anthropocene era. Traditional LULC mapping struggles with challenges like capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of LULC change descriptions, this study applies a blend of advanced techniques. Specifically, it utilizes Landsat-8 satellite imagery with a 30- meter multitemporal resolution, alongside the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, a random forest (RF) algorithm is integrated into the study. This research aims to generate sustainable LULC maps for the Samin Watershed for the years 2014 and 2023. A novel dual composite RF approach based on LULC classification is used to create the final LULC classification map, leveraging the RF-50 and RF-100 tree models. Both RF models use seven input bands (B1 to B7) as datasets for LULC classification.

Distribution of suitable habitat of Firmiana danxiaensis H.H.Hsue and H.S.Kiu in China: An integrated analysis based on changes in climate and high forest thematic resolution land use

Soil erosion is a critical global challenge that degrades land and water resources, leading to reduced soil fertility, pollution of water bodies, and sedimentation in hydraulic structures and reservoirs. In Ethiopia, where agriculture forms the backbone of the economy, unplanned LULC changes have intensified soil erosion, posing a significant threat to food security and sustainable development. In the Holota watershed of Ethiopia, rapid population growth and urbanization have accelerated unplanned land use and land cover (LULC) changes, significantly affecting soil erosion patterns. This study aims to assess the spatiotemporal changes in LULC and their impact on soil erosion from 2000 to 2050. Using Landsat imagery from 2000, 2010, and 2020, supervised classification with the maximum likelihood algorithm was applied in Google Earth Engine (GEE) to map five LULC classes: forest, cropland, built-up areas, shrubland, and grassland. The future LULC for 2050 was predicted using the CA–Markov chain model. Soil erosion for 2020 and 2050 LULC maps was estimated using the Revised Universal Soil Loss Equation (RUSLE). Results indicate that annual soil loss in the watershed was 13.3 t ha − 1 yr − 1 in 2020, increasing to 15.9 t ha − 1 yr − 1 by 2050. Cropland, built-up areas, and grassland are expected to be the major contributors to future soil erosion, while forest and shrubland are likely to play a mitigating role. The novelty of this research lies in its integration of cutting-edge remote sensing technologies, such as GEE and the CA-Markov model, to predict the combined impact of LULC changes on soil erosion in a data-scarce region, providing actionable insights for conservation planning in Ethiopian highlands. These findings offer essential guidance for conservation planners to implement sustainable land management practices aimed at reducing soil erosion, including promoting forest restoration, adopting contour farming, and enforcing land use regulations to limit the expansion of cropland and built-up areas in erosion-prone zones.

Land Use and Land Cover (LULC) maps are important tools for environmental planning and social-ecological modeling, as they provide critical information for evaluating risks, managing natural resources, and facilitating effective decision-making. This study aimed to generate a very high spatial resolution (0.5 m) and detailed (21 classes) LULC map for the greater Mariño watershed (Peru) in 2019, using the MORINGA processing chain. This new method for LULC mapping consisted in a supervised object-based LULC classification, using the random forest algorithm along with multi-sensor satellite imagery from which spectral and textural predictors were derived (a very high spatial resolution Pléiades image and a time serie of high spatial resolution Sentinel-2 images). The random forest classifier showed a very good performance and the LULC map was further improved through additional post-treatment steps that included cross-checking with external GIS data sources and manual correction using photointerpretation, resulting in a more accurate and reliable map. The final LULC provides new information for environmental management and monitoring in the greater Mariño watershed. With this study we contribute to the efforts to develop standardized and replicable methodologies for high-resolution and high-accuracy LULC mapping, which is crucial for informed decision-making and conservation strategies.

Modeling the effect of land use and climate change on water resources and soil erosion in a tropical West African catch-ment (Dano, Burkina Faso) using SHETRAN

This study investigates the hydrological consequences of Land Use and Land Cover (LULC) transformations within the Sam Ngao Watershed (SNgW) from 2000 to 2020. Utilizing the Soil and Water Assessment Tool (SWAT), the research simulates watershed hydrological responses to observed LULC dynamics. To forecast future hydrological conditions, LULC scenarios for 2040 and 2060 were generated using a hybrid Cellular Automata-Markov Chain (CA-Markov) modeling approach. A hybrid classification methodology enhanced the accuracy of LULC mapping from Landsat imagery, integrating multiple classification techniques. Results reveal that LULC alterations between 2000 and 2020 significantly influenced the watershed’s hydrological regime, including declines in dry season flow (7.36%), groundwater discharge (25.43%), and evapotranspiration rates (7.63%), and increases in average annual streamflow (9.85%), wet season streamflow (12.85%), and surface runoff (33.21%). These shifts are primarily attributed to agricultural expansion and deforestation. Projected LULC changes for 2040–2060 indicate a potential reversal in trends, with increases in dry season flow, groundwater recharge, and evapotranspiration, accompanied by decreases in annual and wet season streamflow as well as surface runoff. Hydrological impacts were notably heterogeneous across sub-watersheds, reflecting the spatially uneven distribution of LULC changes. These findings offer valuable insights for decision-makers, water resource managers, and local stakeholders toward creating adaptive strategies for sustainable water resource management in the SNgW and analogous catchments. The study supports international efforts aligned with Sustainable Development Goal 6 (SDG 6) to secure universal access to clean water and sanitation through sustainable management, and with SDG 11 to create sustainable cities and communities through resilient infrastructure, inclusive urban planning, and climate-adaptive water management systems. The outcomes also provide a robust scientific foundation for researchers and policy developers engaged in hydrology, watershed management, and national land use planning frameworks.

Multi-temporal Land use and Land cover (LULC) monitoring is a crucial parameter for assessing an area’s landscape ecology regime. LULC changes can be effectively used to describe dynamics of both urban or rural environments and vegetation patterns as an important indicator of ecological environments. In this context, spatial land use properties can be quantified by using a set of landscape metrics. Landscape metrics capture inherent spatial structure of the environment and are used to enhance interpretation of spatial pattern of the landscape. This study aims to monitor diachronically the LULC regime of the island of Crete, Greece with the use of Landsat satellite imageries (Landsat 5, Landsat-7 and Landsat-8) in terms of soil erosion. For this reason, radiometric and atmospheric corrections are applied to all satellite products and unsupervised classification algorithms are used to develop detail LULC maps of the island. The LULC classes are developed by generalizing basic CORINE classes. Following, various landscape metrics are applied to estimate the temporal changes in LULC patterns of the island. The results denote that the diachronic research of spatial patterns evolution can effectively assist to the investigation of the structure, function and landscape pattern changes.