Land Use Classification Research Articles

Monthly sampling reveals seasonal fine sediment fluctuations and riverine invertebrate community responses

Managing the impacts of anthropogenically enhanced deposited fine sediment levels in lotic ecosystems requires understanding of how catchment land-use changes have altered the natural sediment regime (erosion, transport, deposition) of rivers. Unfortunately, no existing studies have employed an appropriate sampling frequency over a period encompassing the full range of seasonal flow conditions expected to influence in-stream sediment dynamics. We determined the short-term (monthly) dynamics of deposited fine sediment and invertebrate communities over 12-months in 15 fourth- and fifth-order rivers draining catchments of low, medium and high land-use intensity in Southland, New Zealand to determine when and where fine sediment threatens stream health. We compared the Quorer resuspension method (suspendable inorganic sediment, SIS) and the in-stream visual sediment cover assessment method, and evaluated the effectiveness of four commonly-used invertebrate stream health metrics against their newly developed sediment-specific counterparts. Monthly variability in SIS was substantial across all land-use categories, but became more pronounced as land-use intensity increased. All 15 sites experienced a prolonged period of relatively stable flow which coincided with the largest short-term increase in SIS at 14 of the 15 sites. However, variability in SIS was not mirrored in macroinvertebrate metrics. These findings suggest that controlling inputs of fine sediment to rivers and streams will be most effective when targeted at periods of prolonged stable flow, particularly within high land-use intensity catchments. The resuspension method consistently outperformed visual estimates when considering its relationship with macroinvertebrate metrics, while sediment-specific metrics demonstrated a stronger association with fine sediment than commonly employed metrics e.g. (%EPT). We conclude that restoration/mitigation practices cannot be based solely on short-term, or even long-term, reductions in fine sediment, or on physical measures alone, but should be based on long-term recoveries of sediment-impacted invertebrate communities using concurrent measurements of both biotic and abiotic conditions.

Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning

Land use cannot be simply understood as land cover. The same land may carry different functions, such as production, living, and ecological applications; the dominant function of land will affect and restrict other uses. Disorderly urbanization and industrialization have led to an intensification of conflicts among the production, living, and ecological functions of land, which is a major constraint on regional sustainable development. This paper took the perspective of land-use function and used multi-source data such as Sentinel remote-sensing imagery, VIIRS night-time light data, and POIs to classify land-use functions on a large scale in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. The specific research process was as follows. Firstly, the BTH region was multi-scale-segmented based on Sentinel remote-sensing data. Then, the spectral, texture, shape, and socio-economic features of each small area after segmentation were extracted. Moreover, a PLES land-use classification system oriented towards land-use function was established, and a series of representative samples were selected. Subsequently, a random forest model was trained using these samples; then, the trained model was used for the large-scale analysis of land use in the entire BTH region. Finally, the spatial distribution patterns and temporal–spatial evolution characteristics of PLES in the BTH region from 2016 to 2021 were analyzed from the macro level to the micro level.

ENDAU ROMPIN NATIONAL PARK LAND USE LAND COVER CHANGES USING REMOTE SENSING APPROACH

Identification of land use and land cover in forest areas can be challenging due to various land cover types within a forest can be similar, making it hard to differentiate between them using remote sensing techniques. We hypothesized that random forest classification (RF) would outperform maximum likelihood (ML) in the classification of land use and land cover (LULC) in forest areas compared to maximum likelihood (ML). To verify this hypothesis, we conducted a comparative analysis, assessing the accuracy of RF and ML in the classification of LULC within the Taman Negara Endau-Rompin (TNER) region, utilizing Landsat 8 imagery. An accuracy assessment demonstrated that the RF classifier (overall accuracy: 87% kappa coefficient: 0.778, performed better than ML classifying land cover (overall accuracy 77% with kappa accuracy: 0.473) Our results suggest that both methods are able to classify land cover of forest area, but RF is more accurate than ML. From the classification result of RF classification, we calculate the land cover changes of TNER from 2013 to 2022. results showed that there are small changes of forest area were found in TNER. The total forest area decreases from 163250.089 ha to 144765.46 ha during 2013 to 2022. This finding suggests that the effectiveness of the protected area in mitigating deforestation in its surrounding regions may be somewhat limited, as indicated by the observed minor changes.

Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City

Rapid urbanization threatens the ecological environment and quality of life by significantly altering land use and land cover (LULC) and heat distribution. One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range <30 °C decreased by 67.09%. In addition, the LST varied significantly from one category of land use to another. The average LST of built-up land and unused land was higher than the average LST of other land use types in all years, while the average LST of grassland, forest land, and water bodies was significantly lower. Finally, the results of the GWR model showed that R2 and adjusted R2 of the GWR were 0.75 and 0.73, obviously larger than the 0.58 of the OLS models. The GWR model’s higher R2 and adjusted R2 compared to the OLS model indicates that the relationship between LST and the influencing factors underlying the model may exhibit spatial non-stationarity, and the GWR model performs better than the OLS model. The results of both OLS and GWR models show that the normalized difference vegetation index (NDVI) and slope were negatively correlated with LST, while the urban index (UI) and normalized difference built-up index (NDBI) were positively correlated with LST. The findings of the study indicate that increasing green spaces and limiting the unplanned expansion of urban areas are effective measures to mitigate the UHIs in the study area. The results of the study may provide valuable insights into the spatiotemporal characteristics of the UHI and its drivers. Understanding the spatiotemporal characteristics of the UHI can help urban planners, policymakers, and scientists develop more effective urban cooling strategies and improve the urban thermal environment.

Impact of segmentation algorithms on multisensor LULC classification in a semiarid Mediterranean area

Geographic object-based image analysis methods usually provide better results than pixel-based methods for classifying land use and land cover from high and medium resolution satellite imagery. This study compares the results of Random Forest (RF) and Multilayer Perceptron (MLP) when used to classify the segments obtained on an RGB+NIR Sentinel-2 image using three different segmentation algorithms, Multiresolution (MR), Region Growing (RG), and Mean-Shift (MS). The hyperparameters of these algorithms were optimised minimising the intra-object heterogeneity and maximizing the inter-object heterogeneity, integrating them in an optimization loop. Geometric and two different centrality and dispersion statistics were computed from some Sentinel-1, Sentinel-2 and LiDAR variables over the segments, and used as features to classify the datasets. The highest segment cross-validation accuracies were obtained with RF using MR segments: 0.9048 (k=0.8905), while the highest accuracies calculated with test pixels were obtained with MLP using MR segments: 0.9447 (k=0.9303), both with the mean and standard deviation of the feature set. Although the overall accuracy is quite high, there are problems with some classes in the confusion matrix and, significant misclassification appear when a qualitative analysis of the final maps is performed, indicating that the accuracy metrics may be overestimated and that a qualitative analysis of the results may also be necessary.

Spectral Patterns of Pixels and Objects of the Forest Phytophysiognomies in the Anauá National Forest, Roraima State, Brazil

Forest phytophysiognomies have specific spatial patterns that can be mapped or translated into spectral patterns of vegetation. Regions of spectral similarity can be classified by reference to color, tonality or intensity of brightness, reflectance, texture, size, shape, neighborhood influence, etc. We evaluated the power of accuracy of supervised classification algorithms via per-pixel (maximum likelihood) and geographic object-based image analysis (GEOBIA) for distinguishing spectral patterns of the vegetation in the northern Brazilian Amazon. A total of 280 training samples (70%) and 120 validation samples (30%) of each of the 11 vegetation cover and land-use classes (N = 4400) were classified based on differences in their visible (RGB), near-infrared (NIR), and medium infrared (SWIR 1 or MIR) Landsat 8 (OLI) bands. Classification by pixels achieved a greater accuracy (Kappa = 0.75%) than GEOBIA (Kappa = 0.72%). GEOBIA, however, offers a greater plasticity and the possibility of calibrating the spectral rules associated with vegetation indices and spatial parameters. We conclude that both methods enabled precision spectral separations (0.45–1.65 μm), contributing to the distinctions between forest phytophysiognomies and land uses—strategic factors in the planning and management of natural resources in protected areas in the Amazon region.

Improved Understanding of Trade-Offs and Synergies in Ecosystem Services via Fine Land-Use Classification and Multi-Scale Analysis in the Arid Region of Northwest China

Ecosystem services (ESs) serve as a fundamental cornerstone for upholding global biodiversity and promoting human well-being. ESs trade-off and synergy are supposed to be significantly affected by climate change (CC) and land use/cover change (LULC). However, the limited availability of finely classified future land-use data and integrated landscape change models incorporating climate change scenarios has hindered our understanding of the trade-off and synergistic patterns and controls of ESs at multiple scales, particularly in arid areas. Here, a future multi-scenario ESs trade-off/collaborative assessment framework (SD-PLUS-InVEST model) for multi-scale conversion and refined land-use classification was developed by coupling the patch-generated land-use simulation (PLUS) model, system dynamics (SD) model, InVEST model, geographically weighted regression (GWR) model, optimal parameter geographical detector (OPGD) model, and structural equation model (SEM). The four ESs, namely carbon storage (CS), habitat quality (HQ), water conservation (WC), and soil conservation (SC), were assessed. Further, multi-scale ESs were evaluated under different climate change and development scenarios (i.e., the SSP1-2.6 and ecological protection scenario, SSP1-2.6-EP; SSP2-4.5 and natural development scenario, SSP2-4.5-ND; SSP5-8.5 and economic growth scenario, SSP5-8.5-EG). The results demonstrated that the arid region of northwest China (ANWC) was experiencing a significant and continuous warming trend accompanied by increased humidity. There will be a significant decrease in the areas occupied by paddy fields, natural forests, and permanent glaciers among the 24 LULC types. Conversely, there will be a substantial increase in dry land, high-coverage grassland, and urban construction land areas. According to the SSP1-2.6-EP, SSP2-4.5-ND, and SSP5-8.5-EG scenarios, the comprehensive land-use dynamic degrees were estimated to reach 2.58%, 4.08%, and 4.74%, respectively. The LULC resulting from CC exacerbates the differences in the four ESs of ANWC. In particular, CS and HQ experience significant reductions in 2100. Conversely, WC and SC show notable increases during the same period. The changes in CS, HQ, WC, and SC reach 11.36 × 108 m3, 1735.25 × 108 t, −1.29 × 108 t, and −0.009, respectively. The four ESs of CS, HQ, WC, and SC in ANWC display a synergistic relationship. This synergy is influenced by the heterogeneous spatial distribution of CS, HQ, WC, and SC, with the strongest synergy observed between CS and HQ and the weakest between CS and WC. Interestingly, the distribution differences in ESs synergy were amplified at watershed, county, and grid scales in mountainous areas, with the most significant detection differentiation occurring at the grid scale. Furthermore, the detection of spatial heterogeneity in the four ESs can be attributed to various factors. These factors include the drought index (q = 0.378), annual average precipitation (q = 0.375), economic density (q = 0.095), vegetation coverage (q = 0.262), and soil bulk density (q = 0.077). Our results highlight the importance of CC in influencing ESs. The spatial variations in ESs trade-offs and coordination at different scales, particularly the pronounced differences observed in mountainous areas, underscore the need to prioritize the conservation of arid mountainous regions in terms of future policy making.

Accuracy Assessment of different classifiers for Sustainable Development in Landuse and Landcover mapping using Sentinel SAR and Landsat-8 data

Sentinel satellites make use of Synthetic Aperture Radar (SAR) which produces images with backscattered signals at fine spatial resolution from 10 m to 50 m. This study is mainly focused on evaluating and assessing the accuracy of various supervised classifiers like Random Forest classifier, Minimum Distance to mean classifier, KDTree KNN classifier, and Maximum Likelihood classifier for landuse / landcover mapping in Maduranthakam Taluk, Kancheepuram district, Tamilnadu, India. These classifiers are widely used for classifying the Sentinel SAR images. The SAR images were processed using speckle and terrain correction and converted to backscattered energy. The training datasets for the landcover classes, such as vegetation, waterbodies, settlement, and barren land, were collected from Google Earth images in high-resolution mode. These collected training datasets were given as input for the various classifiers during the classification. The obtained classified output results of various classifiers were analyzed and compared using the overall classification accuracy. The overall accuracy achieved by the Random Forest classifier for the polarization VV and VH was 92.86%, whereas the classified accuracy of various classifiers such as KDTree KNN, Minimum distance to mean, and Maximum Likelihood are found to be 81.68%, 83.17%, and 85.64% respectively. The random forest classifier yields a higher classification accuracy value due to its greater stability in allocating the pixels to the right landuse class. In order to compare and validate the results with sentinel data, the random classifier is applied with optical Landsat-8 satellite data. The classification accuracy obtained for Landsat-8 data is 84.61%. It is clearly proved that the random forest classifier with sentinel data gives the best classification accuracy results due to its high spatial resolution and spectral sensitivity. Thus accurate landuse and landcover mapping promote sustainable development by supporting decision-making at local, regional, and national levels.

Accurate classification of land use and land cover using a boundary-specific two-level learning approach augmented with auxiliary features in Google Earth Engine.

Land use land cover (LULC) classification using remote sensing images is a valuable resource in various fields such as climate change, urban development, and land degradation monitoring. The city of Madurai in India is known for its diverse geographical elements and rich heritage, which includes the cultural sport of "Jallikattu": whose main competitor, the zebusare deeply affected by the conversion of their waterbodies and pastures into concrete jungles. Hence, monitoring land degradation is vital in preserving the geography and cultural heritage of the study area, Madurai. The "Landsat 8 Operational Land Imager tier_2 collection_2 Level_2 Surface Reflectance" image was taken for this study. The LULC classification is performed based on the following classes: forest, agriculture, urban, water bodies, uncultivated land, and bare land. The objective of the study is to incorporate auxiliary features to spectral and textural features along with a simple non-iterative clustering (SNIC) segmentation algorithm and implement a boundary-specific two-level learning approach based on support vector machines (SVM) and k nearest neighbors (kNN) classification algorithms. The overall accuracy (OA) of 95.78% and 0 .94 Kappa score (K) were obtained using a boundary-specific two-level model augmented with auxiliary feature and SNIC algorithm in comparison to PB, OB, and OBS, which achieve OA (K) of 81% (0.76), 91% (0.89), and 94.42% (0.92), respectively. The results demonstrate a notable enhancement in overall classification accuracy when augmenting the features and refining classification decisions using a boundary-specific two-level learning approach.

Effects of land use patterns on PM10 concentrations in urban and suburban areas. A European scale analysis

Landscape structure, the spatial characteristic of land cover, is a key factor in air quality. In this study, we investigate the connection between different land-use category areas taken from the 2018 Urban Atlas and the European Environmental Agency's monthly average coarse particulate matter (PM10) concentrations. We identified the main PM10 emission-source land-use categories that restrict the transmission of PM10 and therefore reduce PM10 concentrations in urban and suburban areas. According to our results, water, forest and urban park areas inside differently sized buffer zones surrounding PM10 monitoring stations have an obvious clearing effect on PM10 concentrations throughout almost all seasons, while there is a positive correlation between areas of vacant land, railways and mine, dump and construction sites and monthly PM10 concentrations. In addition, the strong effect of the correlation between built-up areas, industrial areas and roads and monthly average PM10 concentrations changes seasonally. Air pollution from motor vehicles shows a significant positive statistical relationship with PM10 concentrations only in the summer, but in contrast, during the heating (cold) period, because of motor vehicle driving (secondary) winds, traffic corridors have a significant decreasing effect on PM10 concentrations. By understanding the effect of different land use/land cover patterns on PM10 concentrations and variability, we can derive precise spatial urban planning strategies that are adaptable for health care.

Object-Based Image Analysis (OBIA) and Machine Learning (ML) Applied to Tropical Forest Mapping Using Sentinel-2

The purpose of this research was to distinguish and estimate natural forest areas at Paraná, Brazil. Forest plantations (Silviculture) and natural forests have high annual vegetative vigor, as well as agricultural areas in the periods of agricultural harvests, which can bring classification errors between these classes of Land Use and Land Cover (LULC), these classes have similar spectral signatures, but have a distinct texture that can be separated in the supervised classification process, with the joining of object and pixel-to-pixel classification method approaches. Thus, image segmentation techniques through Object-Based Image Analysis (OBIA) and Machine Learning (ML) made forest mapping possible over a large territorial extension. The Google Earth Engine (GEE) platform was used to calculate the vegetation indices (VIs) and Spectral Mixture Analysis (SMA) fraction spectral from Sentinel-2 images, and the creation of homogeneous spectrally shaped regions under supervised classification of phytoecological regions and mesoregions. The overall precision obtained in the mappings resulted in 0.94 Kappa Index (KI) and 96% of Overall Accuracy (OA), which indicates a high performance in large-scale forest mapping. The proposed dataset, source codes and trained models are available on Github (https://github.com/Cechim/simepar-brazil/), creating opportunities for further ad vances in the field.

Detection of Land Use/Land Cover Change and Land Surface Temperature in the Eastern Part of Batna City (North East Algeria) Using Remote Sensing Data and GIS


 
 
 The present paper aims to evaluate the accuracy of classifying Land Use /Land Cover (LULC) types and assesses the trends of their changes in the Eastern Part of Batna City (Northernf Algeria) using remote sensing and GIS. The accuracy of image Land Satellite (Land Sat ) was evaluated using the supervised classification technique, it’s applied in multi spectral and multi temporal satellite data acquired in 2000,2010,2022 and assessed with GOOGLE EARTH PRO and IMAGERY Land Use and topographical map. The second part focused on extraction of LST in three phases and explored the relationship between two land cover indices (NDVI, NDBI) and LST.LU/LC detected, quantified, and stati- cally analyzed, the result indicate that from 2000-2022 the built-up areas increased by 0.34% (6.638km2), the forest area increased by 1.8% (35.144km2), agricultural land cover increased by 1.12% (21.867km2), while bare land decreased by 2.17% (42.368km2). The conversions of areas from bare land to urban land represent the most significant Land Cover changes. The accuracy assessment and correlation coefficient R2 analysis in this study affirms the previous research findings. Even a single land use unit like built-up area, bare land and vegetation also create differences in LST (R2 of NDBI vs. LST ranges from 0.64 to 0.79; NDVI vs. LST ranges from -0.73 to -0.82). With the change of the LU/LC style, its imprint is reflected on the LST. Therefore, immediate reflection on new urbanism must be adopted, initiated and implemented to stop the warming that contributes to climate change in the study area.
 
 

Electrical conductivity as a reliable indicator for assessing land use effects on stream N2O concentration

While it is widely acknowledged that different categories of terrestrial land use significantly affect N2O emissions from streams and rivers, a suitable indicator to comprehensively reflect such influences is still lacking. This study examined the effectiveness of stream environmental and microbial factors in reflecting the influence of land use types on stream N2O concentrations, taking into account both endogenous N2O production and exogenous N2O inputs. Our results showed that human-disturbed (urban and agricultural) streams had nearly four times higher excess N2O concentrations (ΔN2O) than forest streams (5.01 versus 1.32 nmol/L N2O). By combining our observations with previous studies, we identify electrical conductivity as a significant and even the strongest predictor of (excess) N2O concentration and flux in streams across different land uses, surpassing the predictive power of nitrogen content. Elevated ΔN2O was observed at higher conductivities (r = 0.69, p < 0.01), which coincided with higher carbon and nutrient levels across different types of land use. Also, conductivity was positively correlated with the abundance of N2O-producing microbes (p < 0.05), including both nitrifying and denitrifying microbes. Denitrifiers adapted to human disturbance and the genetic potential for net N2O production, as evidenced by the positive correlation between conductivity and nir:nosZ ratio, were enhanced under high-conductivity conditions (p < 0.05). Furthermore, electrical conductivity had great potential to qualitatively indicate the magnitude of exogenous N2O input across different land uses. Overall, our results highlight the value of electrical conductivity as a reliable indicator for assessing the effect of land use types on stream N2O concentrations. This opens opportunities for the development of simple field-based assessments of riverine N2O emissions across regional landscapes.

Land Use and Land Cover Change Detection Using the Random Forest Approach: The Case of The Upper Blue Nile River Basin, Ethiopia.

Monitoring land use change dynamics is critical for tackling food security, climate change, and biodiversity loss on a global scale. This study is designed to classify land use and land cover in the upper Blue Nile River Basin (BNRB) using a random forest (RF) algorithm. The Landsat images for Landsat 45, Landsat 7, and Landsat 8 are used for classification purposes. The study area is classified into seven land use/land cover classes: cultivated lands, bare lands, built-ups, forests, grazing lands, shrublands, and waterbodies. The accuracy of classified images is 83%, 85%, and 91% using the Kappa index of agreements. From 1983 to 2022 periods, cultivated lands and built-up areas increased by 47541 and 1777 km2, respectively, at the expense of grazing lands, shrublands, and forests. Furthermore, the area of water bodies has increased by 662 km2 due to the construction of small and large-scale irrigation and hydroelectric power generation dams. The main factors that determine agricultural land expansion are related to population growth. Therefore, land use and land cover change detection using a random forest is an important technique for multispectral satellite data classification to understand the optimal use of natural resources, conservation practices, and decision-making for sustainable development.

Global urban high-resolution land-use mapping: From benchmarks to multi-megacity applications

Timely and reliable land-use maps are of great importance in urban planning and environmental monitoring. With the rich spatial structure information, very high resolution (VHR) imagery is an important data source for identifying complex urban land use. However, the existing scene datasets and land-use mapping products based on VHR images have the following three problems: 1) accurate geographic boundaries of urban land parcels are lacking; 2) the category systems are inconsistent with the definitions in urban land use; and 3) it is difficult to achieve efficient and fully automated mapping in multiple cities. To tackle these problems, the GlobalUrbanNet-based automatic multi-city mapping and analysis (GAMMA) framework is proposed in this article. The GAMMA framework is made up of the GlobalUrbanNet (GUN) dataset, the multi-city fully automatic urban land-use mapping (AutoULUM) method, and the analysis of urban development patterns. Specifically, the large-scale 42-category fine-grained VHR urban land-use dataset—the GUN dataset—was constructed to deal with the above global urban land-use mapping problems, which contains 1,846,151 samples and 42 land-use categories covering six continents. The GUN dataset samples with land-use semantics and parcel boundaries were generated automatically based on the open-source area of interest (AOI) data from OpenStreetMap (OSM). In addition, the AutoULUM method is proposed to automate the process of OSM road network rectification and land parcel generation. On this basis, efficient and complete multi-city land-use maps can be produced using the GUN-pretrained scene classification models. To establish a benchmark for urban land-use classification, the representative urban land-use classification methods were evaluated on the GUN dataset. For further application, eight megacities from six continents were selected for automatic land-use mapping and analysis, i.e., Shanghai, Wuhan, and Chengdu in Asia, Helsinki in Europe, Nairobi in Africa, New York in North America, Rio de Janeiro in South America, and Sydney in Oceania. The results show that the models trained on the proposed GUN dataset have good generalizability in global urban areas, the AutoULUM method achieves efficient and fully automatic land-use mapping, and the GAMMA framework will help boost the coordinated development of multiple cities around the world.

Land Use and Land Cover Changes in Kabul, Afghanistan Focusing on the Drivers Impacting Urban Dynamics during Five Decades 1973–2020

This study delves into the patterns of urban expansion in Kabul, using Landsat and Sentinel satellite imagery as primary tools for analysis. We classified land use and land cover (LULC) into five distinct categories: water bodies, vegetation, barren land, barren rocky terrain, and buildings. The necessary data processing and analysis was conducted using ERDAS Imagine v.2015 and ArcGIS 10.8 software. Our main objective was to scrutinize changes in LULC across five discrete decades. Additionally, we traced the long-term evolution of built-up areas in Kabul from 1973 to 2020. The classified satellite images revealed significant changes across all categories. For instance, the area of built-up land reduced from 29.91% in 2013 to 23.84% in 2020, while barren land saw a decrease from 33.3% to 28.4% over the same period. Conversely, the proportion of barren rocky terrain exhibited an increase from 22.89% in 2013 to 29.97% in 2020. Minor yet notable shifts were observed in the categories of water bodies and vegetated land use. The percentage of water bodies shrank from 2.51% in 2003 to 1.30% in 2013, and the extent of vegetated land use showed a decline from 13.61% in 2003 to 12.6% in 2013. Our study unveiled evolving land use patterns over time, with specific periods recording an increase in barren land and a slight rise in vegetated areas. These findings underscored the dynamic transformation of Kabul’s urban landscape over the years, with significant implications for urban planning and sustainability.

Relationship between spatial pattern and function of urban land use in Changchun, China.

The urban spatial structure in this study refers to the combination of different categories of land use, and the purpose of the study is to reveal the intrinsic correlation characteristics between urban land use structural combination forms and urban functions. Through the integration of land and population maps and other multi-source data, with the help of exploratory spatial data analysis and other models, this research deals with the land use spatial structure characteristics of Changchun city and its coordination relationship with urban functions. Main conclusions of the study are as follows. The overall density of the land use in the central urban area of Changchun shows patterns of the core being higher than the periphery, the large-scale agglomeration being significant and the small-scale relatively scattered, and the pattern of the mixed land use function index has obvious differentiation characteristics. The study shows that, in the context of the spatial pattern, the overall coupling coordination degree of the land use structure index and the urban function index shows a trend of a gradual decrease, from the core to the periphery. In the context of category differences, the coupling coordination of the land use structure with the population distribution and the Baidu thermal distribution is relatively high, and the coupling coordination with various service facilities is relatively low. Finally, in the context of scale differences, all types of coupling coordination degrees have significant sensitivity to the spatial scales. A large scale significantly reflects the overall decrease in the coupling coordination degrees from the core to the periphery, while a small scale shows the polycentric pattern characteristics of the urban spatial structure.

BUILT-UP AREA DYNAMICS IN “PRE- AND DURING BOKO HARAM CONFLICTS” IN MONGUNO LOCAL GOVERNMENT AREA OF BORNO STATE, NIGERIA

Abstract. The Boko Haram insurgency has had a significant impact on the Monguno Local Government Area (LGA) in Borno State, Nigeria, for more than ten years. This study analysed how the Boko Haram insurgency affected the built-up areas of Monguno LGA. The focus of this study was to map, analyse, and detect the spectral and spatial changes of the earth's surface using remotely sensed images and geospatial techniques, focusing in particular on the built-up areas in the study area, in order to provide sufficient information on the status of built-up areas for effective planning and good governance. Employing a combination of the pixel-based Supervised Maximum Likelihood classification algorithm and the Object Based Image analysis, the study used Landsat 7 ETM+ satellite imageries for the years 2004 and 2007 and Landsat 8 OLT/TIRS imageries for the years 2014 and 2021 to determine the rate of change in the built-up areas over a period of seventeen (17) years (from 2004 to 2021). The classified Land Use and Land Cover (LULC) maps were grouped into four classes: water body, built-up areas, vegetation, and bare land, even though the study was more concerned about the changes in the built-up area. The results showed that from 2004, the built-up area occupied 0.12% with a total land area of 2.00km2 and increased in 2007 by 0.21%. From 2007 to 2014 the built-up area was seen to have increased by 0.31% with a built-up area of 6.00km2. Similarly, there was an increase in the built-up area from 0.31% in 2014 to 0.63% in 2021. Generally, the built-up area has increased by 0.51% from 2004–2021 and the largest percentage increase was noticed from 2014 to 2021 where there was an increase of 0.31% in the built-up area. This increase signifies that there has been an inflow of people into Monguno from neighbouring LGA. It was recommended that future research should incorporate other parameters such as population, literacy level, and socioeconomic well-being of the people.

Techniques and Challenges of the Machine Learning Method for Land Use/Land Cover (LU/LC) Classification in Remote Sensing Using the Google Earth Engine

In order to accurately observe the globe, land use and land cover are crucial. Due to the proliferation of several global modifications associated with the existence of the planet, land use/land cover (LU/LC) classification is now regarded as a topic of highest significance in the natural environment and an important field to be researched by researchers. Google Earth provides satellite image dataset which contains high-resolution images; these images are used to analyze the land area. In order to address the dearth of review articles throughout the land use/land cover classification phase, we proposed a full evaluation, which might help researchers continue their work. Therefore, the purpose of this study is to investigate the methodical steps involved in classifying land use and land cover utilizing the Google Earth platform. The most widely used techniques researchers employ to achieve LU/LC classification using Google Earth Engine are examined in this work. The classification of land use and land cover for a specific region using time series was covered in this study, along with the many types of land use and land cover classes and the approach employed by Google Earth. The limits of the GEE tool and difficulties encountered during the process of classifying land use and cover have also been covered in this survey document. The importance of this review rests in inspiring future scholars to tackle the LU/LC analysis problem successfully, and this study offers researchers a road map for assessing land use/land cover classification.

Land-use pattern and landscape structure impact butterfly diversity and abundance in organic agroecosystems

ContextAgricultural intensification and land-use patterns impact biodiversity and reduce ecosystem functions. Organic farming is often seen as a more biodiversity friendly way of farming. However, not all organic farming is equally supportive for biodiversity, structural diversity of the farms has a great impact and the often high intertwinement of organic and conventional farmland causes spill-over problems.ObjectiveOn organic farmland in central Italy, we analysed butterfly diversity and community structures in different land-use categories across a heterogeneous landscape. We aim to analyse the impact of the different land-use structures such as hedges and forest edges on this farmland, as well as the influence of adjoining conventionally farmed areas.MethodsWe studied butterflies on organic farmland including olive and hazelnut plantations, as well as meadows and fallow land, surrounded by hedges and forests. Hereby we considered the effects of land-use on the studied site and the adjoining land, e.g., semi-natural habitats (as hedges or forest edges), organic or conventional farmland. We counted butterflies along line transects and recorded habitat parameters for each transect. All encountered butterflies were classified according their ecological demands, behaviour, and life-history.ResultsWe found highest mean butterfly species richness in fallow land and lowest in hazelnut plantations. Species community structures differed hardly between plantations and meadows, but considerably compared to fallow land. Butterfly abundances are reduced for olive plantations adjacent to agricultural fields treated with pesticides, compared to such surrounded by organic fields. Habitat para-meters such as the number of flowering plant species and shade impact butterfly diversity and abundance. Forest edges producing considerable shadow to the transects had a significantly negative effect on diversity and abundance, while the influence of hedges was positive.ConclusionsBased on our findings, we conclude that also organic farmland has different degrees of biodiversity depending on a variety of land-use pattern. In particular, habitat heterogeneity and a high diversity of flowers positively influence butterfly diversity. The intensive mixing of organic and conventional agriculture must be seen critical for conservation as negative effects might spill over to organic fields, but might be compensable by landscape structures like hedges.