Urban Land Use Cover Research Articles

Projection of land use and land cover changes based on land change modeler and integrating both land use land cover and climate change on the hydrological response of Big Creek Lake Watershed, South Alabama

Changing land use/land cover (LULC) and climate substantially affect the hydrological components of a watershed. This study explored the future impact of the hydrological responses due to the changing LULC and climate on the Big Creek Lake watershed in Alabama, USA, from 2021 to 2050 using the Soil and Water Assessment Tool (SWAT). Five climate model datasets were used under the moderate scenario (Representative Concentrative Pathways 4.5) and the extreme scenario (Representative Concentrative Pathways 8.5), and the datasets were downscaled and bias-corrected. In addition, changing the LULC of five categories was predicted by Cellular Automata Markov (CA- Markov). With these data combined with the elevation (Digital Elevation Model), soils, and weather data, the SWAT model was calibrated and validated for the studied watershed to quantify how climate change will affect streamflow, nitrogen, and phosphorus. Our results indicate streamflow will increase due to the 50-acre increase in urban LULC. As streamflow increases, the percolation, surface runoff, lateral flow, groundwater flow, and water yield will also increase because the streamflow impacts these hydrological components. Moreover, the increase rate in streamflow is the same for all the components for January, February, and March. Therefore, there is a strong correlation between these months. On the contrary, evaporation will be high in May, June, and July because of the increasing temperature and streamflow. However, the changes in the water hydrological parameters and total nitrogen and phosphorus will be more intense in RCP8.5 than in RCP4.5.

Land Use/Cover Dynamics and its Implication on the Sustainability of Urban Agriculture in Selected Urban Centers of Ethiopia

Abstract Analysis of urban Land Use/Land Cover (LULC) dynamics in light of urban agriculture (UA) helps to understand its implication for UA practice and in turn making the necessary interventions. Therefore, the main objective of this study was to examine the LULC dynamics in selected urban centers of Ethiopia (Addis Ababa, Bahir Dar, Adama, and Hawassa cities) for the last seventeen years (2006–2022). SPOT 5 satellite imagery for the year 2006 and 2016 and Sentinel image for the year 2022 were analyzed. In addition, data from key informant interviews, focus group discussions, and field observations were used to triangulate LULC analyses information and to identify the main causes of LULC dynamics in the studied urban centers. The findings reveled that there were rapid expansion of urban built-up areas at the expense of other urban LULC types mainly of peri urban horticultural lands for the last 17 years (2006‒2022) in Addis Ababa, Hawassa, Bahir Dar, and Adama cities of Ethiopia. This has adverse impact on the sustainability of UA. Rapid urban population growth mainly because of high rural urban migration, expansion of squatter settlement, and increment of investment were the main driving forces of LULC dynamics. Based on the findings of this study (changes in LULC and driving factors) the studied cities administrations or authorities need to develop sustainable development plans by considering UA.

Exploring the relationship between land use/land cover and apparent temperature in China (1996–2020): implications for urban planning

In recent decades, rising air temperatures (AT) and apparent temperatures (AP) have posed growing health risks. In the context of China's rapid urbanization and global climate change, it is crucial to understand the impact of urban land use/land cover (LULC) changes on AP. This study investigates the spatial distribution and long-term variation patterns of AT and AP, using data from 834 meteorological stations across China from 1996 to 2020. It also explores the relationship between AT, AP, and LULC in the urban core areas of 30 major cities. Study reveals that AT and AP exhibit overall high spatial similarity, albeit with greater spatial variance in AP. Notably, regions with significant disparities between the two have been identified. Furthermore, it's observed that the spatial range of high AP change rates is wider than that of AT. Moreover, the study suggests a potential bivariate quadratic function relationship between ΔT (the difference between AT and AP) and Wa_ratio and Ar_ratio, indicating the presence of a Least Suitable Curve (LSC), Wa_ratio=0.263(±0.0269)-0.437(±0.0417)×Ar_ratio\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{W}}_{{\ ext{a}}} \\_{\ ext{ratio}} = {0}{{.263(}} \\pm {0}{{.0269)}} - {0}{{.437(}} \\pm {0}{{.0417)}} \ imes {\ ext{Ar}}\\_{\ ext{ratio}}$$\\end{document}. Urban LULC planning should carefully avoid intersecting with this curve. These findings can provide valuable insights for urban LULC planning, ultimately enhancing the thermal comfort of urban residents.

Analysis of land use/land cover changes and driving forces during the period 1992–2022: a case study of Jinan City, China

Abstract This research aims to quantify the spatial pattern of urban land use/land cover (LULC) change while considering environmental effects. This paper integrates historical Landsat imagery, The Environment for Visualizing Images (ENVI), geographical information system (GIS), and socioeconomic data to determine the spatial–temporal urban LULC dynamics and the conversion of LULC in response to the rapid urbanization from 1992 to 2022. Principle component analysis and multiple linear regression are used to determine and model the relationship between the socioeconomic factors and the changes for identifying the driving forces. The results indicate that impervious surfaces have exponentially increased, expanding more than two times from 2,348 to 4,795 km2, in contrast to bare lands, which drastically declined by 95%, from 1,888 to 87 km2. Water bodies have always been relatively fewer, at approximately 100 km2. In addition, the majority of farmland in Jinan City is concentrated in the northern region with a steady area in the range of 2,100–2,900 km2, while the majority of woodland located in the southern region declined from 3,774.52 km2 (37%) to 3,088.28 km2 (30%). Economic development, population growth, and climate change are the primary factors that have an obvious impact on LULC changes.

Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia

Effective urban planning and management rely on accurate land cover mapping, which can be achieved through the combination of remote sensing data and machine learning algorithms. This study aimed to explore and demonstrate the potential benefits of integrating Sentinel-1 SAR and Sentinel-2 MSI satellite imagery for urban land cover classification in Gondar city, Ethiopia. Synthetic Aperture Radar (SAR) data from Sentinel-1A and Multispectral Instrument (MSI) data from Sentinel-2B for the year 2023 were utilized for this research work. Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms were utilized for the classification process. Google Earth Engine (GEE) was used for the processing, classification, and validation of the remote sensing data. The findings of the research provided valuable insights into the performance evaluation of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for image classification using different datasets, namely Sentinel 2B Multispectral Instrument (MSI) and Sentinel 1A Synthetic Aperture Radar (SAR) data. When applied to the Sentinel 2B MSI dataset, both SVM and RF achieved an overall accuracy (OA) of 0.69, with a moderate level of agreement indicated by the Kappa score of 0.357. For the Sentinel 1A SAR data, SVM maintained the same OA of 0.69 but showed an improved Kappa score of 0.67, indicating its suitability for SAR image classification. In contrast, RF achieved a slightly lower OA of 0.66 with Sentinel 1A SAR data. However, when the datasets of Sentinel 2B MSI and Sentinel 1A SAR were combined, SVM achieved an impressive OA of 0.91 with a high Kappa score of 0.80, while RF achieved an OA of 0.81 with a Kappa score of 0.809. These findings highlight the potential of fusing satellite data from multiple sources to enhance the accuracy and effectiveness of image classification algorithms, making them valuable tools for various applications, including land use mapping and environmental monitoring.

Spatial-temporal patterns of urban expansion by land use/ land cover transfer in China

Urbanization has accelerated in China, resulting in a noticeable conflict between urban expansion and land use/land cover (LULC). This issue can exacerbate ecological and developmental problems in urban neighborhoods, so it is crucial to explore the relationship between long-term urban expansion and LULC spatial and temporal transitions. We first defined urban built-up areas in terms of human economic activity and then used spatial measures to examine urban expansion and spatio-temporal changes in 25 LULC classes by analyzing 369 cities across China. Our findings indicate that China’s urban built-up areas expanded 9.1 times between 1995 and 2020, with the fastest growth rate occurring between 1995 and 2005 at about 16%. We observed a negative association between China’s urban built-up area and dry farming land, forests, and grassland in that order, with grassland exhibiting the weakest correlation. The negative correlation between urban built-up areas and paddy fields became positive after 2010, while the positive correlation with reservoirs and canals became negative after 2015. This study utilized spatial measures to demonstrate the spatial heterogeneity of urban expansion in China and explored the extent to which different LULC area changes explained urban expansion. We provide a reference for decision-making efforts on land management and sustainable use in urban areas.

Spatiotemporal Analysis and Prediction of Urban Land Use/Land Cover Changes Using a Cellular Automata and Novel Patch-Generating Land Use Simulation Model: A Study of Zhejiang Province, China

Urban land use/land cover (LULC) monitoring and prediction are vital for understanding the spatiotemporal change dynamics of future land uses. They provide the necessary data for effectively planning and managing natural land resources. In this study, we analyzed and simulated the changes in urban LULC within Zhejiang Province, a region in China experiencing rapid urbanization. By exploring the historical change dynamics of the region, we observed substantial transformations in the extent of built-up areas, forests, and agricultural land from 1995 to 2020. Specifically, the study area witnessed the expansion in urban built-up areas by approximately 6126.93 km2, while forests and agricultural land witnessed decreases of 3252.47 km2 and 2885.13 km2, respectively. To predict the study area’s future LULC, a cellular automata (CA) model was utilized in combination with an advanced patch-generating land use simulation (PLUS) model. This integrated approach allowed for multiple land use predictions based on different scenarios. Under the baseline scenario (BLS), it was projected that the area of urban expansion in Zhejiang Province would be approximately 4501.62 km2. However, under the scenario of cultivated land and ecological protection, i.e., CLPS and EPS, urban growth was observed to be 538.64 km2 and 1776.16 km2, respectively. These findings indicate that the extent of built-up area development in Zhejiang Province is significantly reduced when the CLPS and EPS are implemented in comparison to the BLS. Therefore, policy interventions are crucial to protect agricultural land and conserve ecological areas. This research provides the scientific data needed for proper planning and serves as reference data for other regions with similar rapid urbanization.

Urban land use land cover classification based on GF-6 satellite imagery and multi-feature optimization

Urban land use/land cover (LULC) classification has long been a hotspot for remote sensing applications. With high spatio-temporal resolution and multispectral, the recently launched GF-6 satellite provides ideal open imagery for LULC mapping. In this study, we utilized multitemporal GF-6 images to generate six types of land features, including spectral bands, texture features, built-up, waterbody, vegetation, and red-edge indices. The minimum Redundancy Maximum Relevance (mRMR) algorithm was employed to optimize feature selection. Subsequently, Random Forest (RF) and Extreme Gradient Boosting (XGBT) were assessed using different feature selections. Besides, various feature configurations were designed for LULC classification and comparison. The results indicate that the mRMR-based RF method achieved the highest overall accuracy of 91.37%. The temporal red-edge indices were important features for urban LULC classification and contributed mainly to grassland and cropland. These results supplement existing classification methods and assist in improving LULC mapping in urban areas with complex landscapes.

Climate Change Risk of Urban Growth and Land Use/Land Cover Conversion: An In-Depth Review of the Recent Research in Iran

This research is the first literature review of the past three decades’ studies on the effects of urban development and land use/land cover (LULC) change on Iran’s climate change. For this purpose, 67 articles were found, evaluated, and classified according to the spatial and temporal scale, case study, period, data type, climatic factor, methodology, and meteorological data. Moreover, the reviewed literature methodologies were classified according to the purpose, method, and data source. According to the spatial-scale results, national- and city-level studies had the lowest and highest numbers, respectively. Tehran was the most case studies because Tehran is Iran’s capital and the largest metropolitan city. In terms of the temporal scale, studies predicting future changes (urban development and climate change) included 5% of the total literature. Satellite images were the most applied data in the reviewed literature (58%). Overall, 79% of the studies used temperature-related factors to explain the climatic impacts of urban growth and LULC conversion. Spatial modeling with 52% publications was the most used method, while numerical modeling with 12% studies was the least used method. This review showed broad study gaps in applying numerical models, neighborhood scales, urban micro-scale parameters, and long-term projections forecasts due to rapid urban development in Iran compared to the rest of the world. Therefore, our synthesis will assist researchers in facilitating better design for future studies in Iran and similar countries.

Spatial expansion and patterns of land use/land cover changes around Accra, Ghana – Emerging insights from Awutu Senya East Municipal Area

For most cities in the developing world, rapid and uncontrolled expansion at their fringes is a regular feature. In many cases these rapidly growing peri-urban areas are carved out as new local government units to function separately from the expanding city. Hence, the hitherto agricultural areas are transformed into residential and other urban uses. The overall result of such dynamic spatial and socio-political processes is the rapid change in land use/land cover (LULC). In this study, we assess, measure and explain LULC changes in Accra’s western peri-urban areas with the Awutu Senya East Municipal area (ASEMA) as the spatial context. Located on the west boundary of the Greater Accra Metropolitan Area (GAMA), the ASEMA is affected by the rapid expansion of GAMA. Adopting GIS and Remote Sensing methods, the study analyses the rate of urban expansion and LULC changes spanning 2014–2020. The study revealed that the urbanised areas have more than doubled representing a 152% change in the urban expanse over the six-year period under investigation. While this could be attributed to the rapid growth and spill-over from GAMA, the study also reveals socio-political and economic factors contributing to rapid changes in LULC in ASEMA. Specific boundary management strategies have been proposed to control the rapid expansion around the Municipality and also help address socio-spatial conflicts over boundaries.

Update of land use/land cover and soil texture for Brazil: Impact on WRF modeling results over São Paulo

Land Use/Land Cover (LULC) and soil texture play a key role in meteorological models because they determine the vegetation and soil proprieties that interfere in the exchange of energy, moisture, and momentum between the land surface and the atmosphere. Additionally, LULC and soil texture are relevant input datasets in meteorological models affecting their results and future applicability as in weather researches and air quality modeling. Brazil has a complex and heterogeneous LU, and it has faced significant LULC changes in the past years. Therefore, this paper aims to update the LULC, using the national product MapBiomas, and soil texture data, by SoilGrids, to replace the default input data in the Weather Research and Forecast (WRF) model for São Paulo, Brazil. Aiming to evaluate the impact of those input data on WRF simulations, five cases were simulated using WRF v4.1.3 with 1 km of grid resolution, and combinations of “Default” and “Updated” input data. Sixty-days simulations from March 15th to May 15th of 2015, covering the transition of wet to dry season, were performed and evaluated with observational data over São Paulo State. The results showed significant differences in the classifications of LULC and soil texture in the entire domain between the default and updated data. The updated data is more realistic and coherent with local characteristics, being more representative, as an example over Santos city area being correctly classified as urban and built-in updated LULC and not water, as in the default. The comparison between the modeled results with observations data has shown a similar behavior for temperature and humidity for the five cases at the monitoring stations grid cells because the LULC changes were between classes with similar land parameters, such as albedo, roughness length, and soils moisture, although the Default classes are not accurate. However, the latent and sensible heat fluxes were ways more sensitive to the LULC/soil texture changes in the WRF model. Additionally, reasonable differences were observed over the entire modeling domain for these two variables. The updated land surface data provoked low temperatures at 9h and 17h UTC, less humidity at 9h UTC, and more humidity at 17h UTC, especially in the north part of the modeling domain, the area which has faced more LULC and soil changes. The PBL height was also affected by the updated data, probably caused by the impact at heat flux over the domain, causing a variation from 30% to 70% over the modeled grid cell, which may have a higher impact on air quality modeling. Thus, it is recommended to update the land surface data for Brazil to avoid misclassification of LULC and soil texture, even if the comparison at monitoring stations has shown similar behavior between the default and updated land surface data. Additionally, updates in the land parameter inside the model are required to represent each LULC/soil class better.

The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries

The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.

Mapping and assessing ecosystems and their services: a comparative approach to ecosystem service supply in Suriname and French Guiana

ABSTRACT Current environmental resource management policies acknowledge the need to manage and conserve biodiversity. Sustaining good ecosystem conditions and ecosystem services (ES) is imperative at and across multiple spatial scales. The ES concept is a valuable tool to communicate the benefits that nature provides to people. In the Guiana Shield, neighbouring countries share landscapes and ecosystems, and therefore also the services they supply. This study presents the first spatial ES assessments at territorial level for Suriname and French Guiana. Expert-based ES supply matrices were used and analysed in combination with Land Use/Land Cover (LULC) data to compile ES capacity maps for the two territories. In comparison, both ES supply matrices showed a high degree of similarity – forest ecosystems scored the highest ES capacities, followed by aquatic and marine ecosystems. Agricultural and urban land cover showed weak to moderate capacities for ES supply. A statistical analysis revealed a 30% difference of the two matrix assessments. Expert scores given for ES in Suriname exceeded those in French Guiana, especially for urban LULC and planted forests. Sociodemographic factors such as age, gender and institutional environment were analysed to explain this difference. The diverging scores can also be attributed to the distribution and the degree of similarity of the different LULC types and, hence, ES capacities and different governance and institutional contexts of the assessments. Comparative evaluations are essential to understand the differences in perception of ES supply capacities and to underpin future knowledge-based bilateral conservation policies and funding decisions by governments and managers.

Detection of urban LULC changes and its effect on soil organic carbon stocks: A case study of Béni Mellal City (Morocco)

Increasing population and economic development in Beni Mellal City (Morocco) caused rapid urbanization leading to the loss of fertile agricultural lands. So, the present study aimed to identify changes in the land use/land cover (LULC) and their potential effects on soil organic carbon (SOC) stocks in Beni Mellal City. Geospatial techniques and CA–Markov model were employed to evaluate and to predict LULC patterns based on Landsat images for the years 1985, 2000, and 2018. Forty samples of topsoil taken at all LULC classes have been analyzed to provide SOC contents and soil texture. The LULC results revealed that arboriculture and forest lands mainly tend to be more resistant to degradation, while the estimated built-up surface changes of 10% and 15% of the total study area during, respectively, 1985–2018 and 2050 are being done to the detriment of arable and bare lands. The mean SOC stocks under all land use were about 42.29–278.54 kg m−2 by 2018, and varied following the order: forest > arable fields > barren land > arboriculture. This indicated that land use and management and consequently the vegetation types impacted the SOC distribution characteristics in the topsoil in the studied area. The urbanization linked to population growth and economic development also had a significant impact on the LULC change and tended to reduce the agricultural field surface and, implicitly, the SOC sequestration. The information from this study could be used by environmentalists and decision makers for better land use planning

Monitoring and forecasting spatio-temporal LULC for Akure rainforest habitat in Nigeria

Abstract For several decades, Nigerian cities have been experiencing a decline in their biodiversity resulting from rapid land use land cover (LULC) changes. Anticipating short/long-term consequences, this study hypothesised the effects of LULC variables in Akure, a developing tropical rainforest city in south-west Nigeria. A differentiated trend of urban LULC was determined over a period covering 1999–2019. The study showed the net change for bare land, built-up area, cultivated land, forest cover and grassland over the two decades to be −292.68 km2, +325.79 km2, +88.65 km2, +8.62 km2 and −131.38 km2, respectively. With a projected population increase of about 46.85%, the study identified that the built-up land cover increased from 1.98% to 48.61%. The change detection analysis revealed an upsurge in built area class. The expansion indicated a significant inverse correlation with the bare land class (50.97% to 8.66%) and grassland class (36.33% to 17.94%) over the study period. The study observed that the land consumption rate (in hectares) steadily increased by 0.00505, 0.00362 and 0.0687, in the year 1999, 2009 and 2019, respectively. This rate of increase is higher than studies conducted in more populated cities. The Cellular Automata (CA) Markovian analysis predicted a 37.92% growth of the study area will be the built-up area in the next two decades (2039). The 20-year prediction for Akure built-up area is within range when compared to CA Markov prediction for other cities across the globe. The findings of this study will guide future planning for rational LULC evaluation.

Effects of using different urban parametrization schemes and land-cover datasets on the accuracy of WRF model over the City of Ottawa

In the face of rapid urbanization and global warming, it is important to acquire a better understanding of urban climate and land-atmosphere interactions operating therein. The Weather Research and Forecasting (WRF) model is a limited area model that has been used to study urban microclimate in many cities across the globe. However, such a study is lacking for the Canada's capital city: Ottawa. In this article, the WRF model is set-up at 1 km spatial resolution over the Ottawa region and its sensitivity towards the use of different urban parametrization schemes and land-cover datasets is investigated from 01 June to 31 August 2018 which includes an extreme heat weather event spanning from June 30 to July 6. WRF-simulations are performed using the Noah land surface model with two urban parametrization schemes of different complexity, i.e., a simple bulk urban parametrization and a more advanced multilayer urban canopy model (UCM). Both WRF-simulations used the default land use-land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) available in WRF. Between these two WRF-simulations, the simulation with the multilayer UCM is found to be more accurate than the bulk scheme simulation in terms of modeled near-surface wind-speed, relative humidity, and total precipitation compared to observations recorded at one urban weather gauging station located within the city limits. Finally, a third WRF-simulation is performed with the multilayer UCM but using a higher resolution 30 m land use-land cover data product for the urban domain. This third WRF-experiment improved further the near-surface wind speed, relative humidity, and total precipitation correspondence to observations within the city. It is worthy to mention that modeled total precipitation is found to be sensitive to both urban parametrization schemes and urban land use-land cover data sets.

Modeling Seasonal Variation in Urban Weather in Sub-Tropical Region of Delhi

Complexity and heterogeneity of urban areas lead to difficulty in urban weather simulations and climate modeling. Diversity and size of urban areas necessitate to downscale global climate models to urban scale (~ hundreds of meters) and to enhance urban parameterization in the models to realistically simulate urban weather conditions. Hence, in this study, a methodology has been developed to generate multi-class urban land use land cover (LULC) by employing Resourcesat-2 LISS IV data. Weather Research and Forecast (WRF) model which is also a mesoscale numerical weather prediction and regional climate model was utilized to downscale the meteorological parameters up to 0.5 km grid resolution. Multi-class urban LULC prepared with improved urban parameters and updated Land Surface Parameters (LSPs) was ingested in model for Delhi to evaluate the model performance in three dominant seasons, i.e., summer, monsoon and winter. Evaluation of model performance with ground observation data revealed that multi-class urban LULC along with updated LSPs provided improved RMSE values of 2.31° C, 1.79 m/s and 0.94 mbar as compared to ingestion of multi-class urban LULC only (RMSE values of 3.42° C, 3.72 m/s and 1.58 mbar) for temperature at 2 m, wind speed and surface pressure, respectively. Temperature is found to be highest in summer season (38.58° C) and lowest in winter season while relative humidity is highest in monsoon season (~ 88%) and lowest in summer season (~ 30%). The study highlights the importance of ingestion of updated LSPs along with updated multi-class urban LULC for enhanced model performance.

Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification

Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In this study, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale (MS) supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR orthomosaic and digital surface model (DSM). The integrated approach commenced through a preliminary MS image segmentation parameter selection, followed by the application of three supervised ML models, namely, random forest (RF), support vector machine (SVM), and decision tree (DT). These models were implemented at the optimal MS levels to identify preliminary information, such as the optimal segmentation level(s) and relevant features, for extracting 12 land use/land cover (LULC) urban classes from the fused datasets. Using the information obtained from the first phase of the analysis, detailed MS classification was iteratively conducted to improve the classification accuracy and derive the final urban LULC maps. Two UAV-based datasets were used to develop and assess the effectiveness of the proposed framework. The hierarchical classification of the pilot study area showed that the RF was superior with an overall accuracy (OA) of 94.40% and a kappa coefficient (K) of 0.938, followed by SVM (OA = 92.50% and K = 0.917) and DT (OA = 91.60% and K = 0.908). The classification results of the second dataset revealed that SVM was superior with an OA of 94.45% and K of 0.938, followed by RF (OA = 92.46% and K = 0.916) and DT (OA = 90.46% and K = 0.893). The proposed framework exhibited an excellent potential for the detailed mapping of heterogeneous urban landscapes from the fusion of UHSR orthophoto and DSM images using various ML models.

Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images

Geographic object-based image analysis (GEOBIA) has emerged as an effective and evolving paradigm for analyzing very high resolution (VHR) images as it demonstrates preeminence over the traditional pixel-wise methods and enables the utilization of diverse spectral, geometrical, and textural information to for image classification. Among feature selection (FS) methods, metaheuristic FS techniques have recently demonstrated effective performance in the dimensionality reduction of GEOBIA features. In this study, an artificial neural network (ANN) was integrated with particle swarm optimization (PSO) to enhance the learning process and more effectively determine the most significant features and their importance using WorldView-3 (WV-3) satellite data. First, multi-resolution image segmentation parameters were tuned using Taguchi optimization technique and unsupervised segmentation quality measure. Second, the proposed ANN–PSO was compared with PSO under 100 iterations. The ANN–PSO integration achieved lower root mean square error (RMSE) in all the iterations. Third, state-of-the-art extreme gradient boosting (Xgboost) image classifier was used to derive the land use/land cover (LULC) map of the first study area and assess the transferability of the selected features on the second and third regions. The Xgboost classifier obtained 91.68%, 89.54%, and 89.33% overall accuracies for the first, second, and third sites, respectively. ANN contributed to an intelligent approach for identifying which features are more likely to be relevant and discriminate the land cover types. The proposed integrated FS is a promising approach and an efficient tool for determining significant features and enhancing the detection of urban LULC classes from WV-3 data.

Evidence for the effects of land use on freshwater ecosystems in New Zealand

ABSTRACT To meet the challenges of preventing and reversing adverse effects of land use on ecosystems, management actions need to be founded on strong evidence. We used the pressure-state-impact (PSI) framework to assess evidence of land-use effects on New Zealand freshwater ecosystems. The evidence consisted of published quantitative and categorical associations linking land-use pressures to state changes and ecological impacts in rivers, lakes and aquifers. There was substantial evidence of land-use effects, particularly where land use/land cover (LULC) classes were used as pressure variables. Proportions of catchment area in urban and pastoral LULC were consistently, positively correlated with contaminant levels in water bodies and negatively correlated with ecological-health indicators. Other consistent PSI associations included positive correlations between cattle stocking rates and river contaminant levels, increased fine sediment and decreased ecological-health scores in rivers following forest harvest, and increased river contaminant levels at sites with stock access. Despite these consistent associations, the evidence base has four general shortcomings that should be addressed: (1) inadequate integration of data and models that link land use and contaminant loss to state changes and impacts in freshwater ecosystems; (2) weak inferences based on LULC; (3) reliance on categorical PSI associations; (4) gaps in reported PSI associations.