Land Use Cover Data Research Articles

Assessing watershed vulnerability to erosion and sedimentation: Integrating DEM and LULC data in Guyana's diverse landscapes

This study assesses watershed vulnerability to erosion and sedimentation in Guyana using Digital Elevation Model (DEM) and Land Use/Land Cover (LULC) classification. It aims to assess erosion risk by examining rainfall erosivity, soil types, and spatial variability. The integration of DEM-derived slope data with LULC and rainfall erosivity shows a strong correlation between high rainfall (20,809.02–31,262.35 mm) and erosivity (21,433.29–32,200.22 MJ mm ha−1 h−1 yr−1), indicating increased erosion potential, particularly in southwestern regions. Soil analysis reveals Plinthic Acrisols, with high clay content, are more prone to erosion, whereas Orthic Ferralsols are less vulnerable. Slope analysis suggests flat terrain (89.16 %) has low erosion risk, but steeper slopes require interventions. Conservation strategies like reforestation, sustainable agriculture, contour plowing, and terracing are recommended to reduce erosion impacts. This integrated assessment provides insights for prioritizing soil conservation and adaptive management to address erosion risks in Guyana.

Sensitivity Analysis and Parameterization of Gridded and Lumped Models Representation for Heterogeneous Land Use and Land Cover

Hydrological processes can be highly influenced by changes in land use land cover (LULC), which can make hydrological modelling also very sensitive to land cover characterization. Therefore, obtaining up-to-date LULC data is a crucial process in hydrological modelling, and as such, different sources of LULC data raises questions on their quality and applicability. This is especially true with new data sources, such as citizen science-based land cover maps. Therefore, this research aims to explore the influence of LULC data sources on hydrological models via their parameterization and by performing sensitivity analyses. Kiffissos catchment, in Greece, a poorly gauged and highly urbanized basin including the city of Athens, is the case study area. In total, 12 continuous hydrological models were developed by mainly varying their structure and parametrization (lumped and gridded) and using three LULC datasets: coordination of information on the environment (CORINE), Urban Atlas and Scent (citizen-based). It was found that excess precipitation is negligibly contributed to by soil saturation and is dominated by the runoff over impervious areas. Therefore, imperviousness was the main parameter influencing both sensitivity to land cover and parameterization. Lastly, although the parametrization as lumped and gridded models affected the representation of hydrological processes in pervious areas, it was not relevant in terms of excess precipitation.

Evaluation of driving effects of carbon storage change in the source of the Yellow River: A perspective with CMIP6 future development scenarios

Understanding how future climate scenarios impact land use/cover (LUC) and carbon storage (CS) is crucial for achieving carbon neutrality. However, research often overlooks the spatiotemporal impacts of future climate and socioeconomic changes on CS. This study integrates system dynamic (SD), patch-generating land use simulation (PLUS), the integrated valuation of ecosystem services and tradeoffs (InVEST) model, and the geographical detector to assess the LUC and CS evolution in the source of the Yellow River (SYR) from 2020 to 2060. Utilizing carbon density and LUC data, we explored the influence of natural and socioeconomic factors on CS under five shared socioeconomic pathways and representative concentration pathways (SSP-RCPs) scenarios. Our findings demonstrate that: (1) Ecological land, including woodland, grassland, and wetland, expanded more under SSP126 compared to SSP245, with SSP345, SSP460, and SSP585 showing a trend of degradation tied to deeper economic contribution. (2) By 2060, CS in terrestrial ecosystem under SSP126, SSP245, SSP345, SSP460, and SSP585 were 702.33 × 106 t, 700.33 × 106 t, 697.22 × 106 t, 696.03 × 106 t, and 691.21 × 106 t, respectively. This represents changes of 3.69 × 106 t, 1.69 × 106 t, −1.49 × 106 t, −2.68 × 106 t, and −7.43 × 106 t compared to 2020. (3) Soil type predominantly influenced the spatial differentiation of CS, with significant interactions with precipitation. This research provides new insights into land redistribution, economic strategies, and achieving carbon neutrality.

Forest Encroachment and Agriculture Land Abandonment in the Context of Land Cover Change in Kailali

Land use land cover (LULC) area has been changing for many decades in the Kailali district but has little been researched. The study aims to examine the spatial and temporal changes of LULC from 1978 to 2021, carrying out major driving factors of forest cover change regarding forest encroachment and abandonment. The researcher employed a mixed-method approach to accomplish this study. Field observation, household survey with 45 participants, Key Informant Interviews (KII) with three participants, and five Focus Group Discussions (FGD) were used as research tools to collect the data. Moreover, the researcher used books, journal articles, official reports, and related previous empirical studies as secondary data [Land Resource Mapping Project (LRMP) data were used to screen LULC status of 1978 and 1990, and digitization on Google Earth Pro to prepare LULC data for 2000 and 2021]. The findings of the study revealed that the forest cover area of the Kailali district was in a decreasing trend [ for instance in 1978 the area of forest cover was 75.38%, in 1990 it was 66.51% in 2000, and was 65% in 2021]. In contrast to forest cover, the agricultural [settlements and infrastructures] land area was on an increasing trend which was followed by 21.77%, 30.28%, 31.75%, and 32.25% in each period. The study also found that the changing rate of forest cover and the agricultural land have been decreasing. Further, the research found the major drivers of forest cover change were encroachment and agricultural land abandonment. In addition, population growth, migration, poverty, development activities, natural hazards, resettlement, public utilities, weak implications of policy, and illegal activities were also the major causes of forest cover change. The research implies that both forest encroachment and land abandonment should be controlled, and are to be considered by the stakeholders.

A fuzzy analytical hierarchy process -GIS approach to flood susceptibility mapping in NEOM, Saudi Arabia

The Kingdom of Saudi Arabia is undergoing massive and rapid urbanization as part of Vision 2030. This includes development projects along Saudi Arabia’s coastline across the Red Sea. Coastal areas, especially the ones along Saudi’s western regions are susceptible to natural disasters such as flooding. NEOM, a futuristic city currently being developed in the northwest of Saudi Arabia, exemplifies a potential flooding hazard due to its geographic location and proposed urbanization plans. This research aims to enhance flood hazard assessment in NEOM by applying the Fuzzy Analytical Hierarchy Process (FAHP) in combination with Geographic Information System (GIS). Acknowledging traditional limitations related to data availability and parameter selection consensus, the study carefully selects parameters such as drainage density, elevation, slope, rainfall, land use/land cover (LULC), soil type, normalized difference vegetation index (NDVI), and topographic wetness index (TWI). The 30 m DEM was used to derive Drainage Density, Slope, and TWI while LULC data helped assess land cover changes. Rainfall data and soil type information are integrated to evaluate their impact on flood susceptibility. NDVI is employed to analyze vegetation cover. Utilizing ArcGIS Pro’s weighted overlay model, the criteria were combined to generate the final flood susceptibility map. The research outcomes manifest in a flood susceptibility map categorizing areas into seven distinct susceptibility classes, ranging from ‘very low’ to ‘very high.’ A quantitative breakdown in a summary table provides insights into the proportional distribution of flood risk. Results indicate a significant portion of NEOM falls within varying degrees of moderate susceptibility range with relatively limited distribution of flood susceptibility on the extremes, equating to areas with ‘low to moderate’ susceptibility is 4,322.8 km2, areas with ‘moderate’ susceptibility is 5,109.69 km2, areas with ‘moderate to high’ is 4,081.39 km2. The flood susceptibility map developed in this study can shed insights on potential optimum areas for flood mitigation measures (i.e., optimum locations for establishing stormwater collection points).

The promotion of sustainable land use planning for the enhancement of ecosystem service capacity: Based on the FLUS-INVEST-RUSLE-CASA model.

Land Use/Land Cover (LULC) is one of the most significant human variables influencing the efficiency of Ecosystem Services (ESs) in terrestrial ecosystems. Theoretical and technical assistance for regional sustainable land use planning and management, as well as ecosystem conservation and restoration, is provided by investigating the influence of changes in the LULC pattern on the efficiency of ESs. This research focuses on the interactions between socioeconomic activities and natural ecological processes in the Three Gorges Reservoir Area (TGRA). We use LULC data from the TGRA for the years 1990, 2000, 2010, and 2020. The study includes the analysis and calculation of the spatiotemporal evolution features of the current LULC pattern and the efficiency of ESs, including their spatiotemporal distribution. Considering the TGRA's national development orientation and guidance, three potential LULC patterns are constructed under various develop-ment scenarios. To calculate the efficiency of ESs, the GeoSOS-FLUS future LULC simulation model is linked, and several methodologies such as INVEST, RUSLE, and CASA are used. The goal is to investigate the influence of future changes in LULC patterns on ESs efficiency. The findings show the following: (1) From 1990 to 2020, the values of water conservation services in the TGRA decreased and subsequently increased. High-value areas are primarily located in the reservoir's centre and eastern sections, whereas low-value areas are mostly found in the western section. Soil conservation service values initially declined and later climbed. The TGRA's carbon storage services have in-creased yearly, from 552.64 g/m2 in 2000 to 615.92 g/m2 in 2020. (2) In the ecological protection scenario, carbon storage and soil erosion increased compared to the ecosystem services in 2020. The ecological system service benefits are greater when compared to the natural development scenario. (3) The four ESs show positive spatial correlations across all three scenarios, and local spatial au-tocorrelation analysis findings demonstrate that carbon storage, water yield, and habitat quality have comparable spatial distributions across all three scenarios. To some extent, high-value areas for water conservation, soil retention, carbon storage, and habitat quality overlap.

Impacts of land use and cover changes on ecosystem service values from 1992 to 2052 in Gena District, Southwest Ethiopia

Land use and cover changes alter the functions and structures of ecosystem, resulting in variations in Ecosystem Service Values (ESVs). Thus, we examined the impacts of land use/land cover (LULC) changes on ESVs from 1992 to 2052 using geospatial technologies. The Landsat images were classified using the supervised maximum likelihood classification technique, and future changes in LULC were predicted using the CA-Markov model. Ecosystem Service Values coefficients were adopted from empirical studies and ESVs changes were evaluated based on the benefit transfer method using LULC data for the study periods, with their corresponding modified ESVs coefficients. The results revealed that, the proportions of grassland, forestland and shrubland declined by 58.5 %, 48.15 % and 33.48 %, respectively, from 1992 to 2022. Simultaneously, the highest rate of expansions of waterbodies (34 times), farmland and settlement threefold as well as bareland (60.2 %) from 1992 to 2022 was noticed. As a result, decreasing trends were experienced in the total ESVs of the district from US$33.6 million in 1992 to US$27.79 million in 2022, and are anticipated to further decline to US$25.94 million in 2052. The ESVs of forestland, shrubland and grassland shrank from 53.1 %, 40 % and 2.78 % in 1992 to 33.28 %, 33.16 % and 1.4 % in 2022 these changes are anticipated to continue for the next three decades, except trend for the increase in grassland ecosystem service value. Therefore, the government should redesign effective land management strategies to alleviate the negative consequences of LULC changes, facilitate payment for ecosystem services, and design ecotourism to boost the income of residents for major land use management-based production systems to increase the ESV in the district.

Human activities affecting lesser flamingo (Phoeniconaias minor) habitat in Momella lakes, Tanzania

ABSTRACT Land use/land cover (LULC) change, caused by human activities, can strongly affect wildlife species and their habitats. Yet, human impacts onto lakes and associated indicator bird species such as the lesser flamingos (Phoeniconaias minor) have rarely been investigated. We used remote sensing images from 1989 to 2019, with additional interviews and focus group discussions to investigate how LULC change and agricultural activities adjacent to the Momella lakes, Tanzania, have affected the flamingo habitat. Agricultural areas increased strongly over time, and most respondents earned their living through crop farming. The use of synthetic fertilizers, and limited knowledge about fertilizer effects on environmental health, were evident. We highlight that LULC data combined with socio-economic assessments is essential to understanding the dynamics and impacts of human activities on wildlife. We recommend training for sustainable farming practices around areas of high conservation values and integrating land use changes and socio-economic dynamics into conservation efforts.

Investigating the interactions between spatiotemporal land use/land cover dynamics and private land ownership

Increasing population caused by migration from rural to urban leads the cities, whose ownership is primarily public, to spread over forest areas and agricultural lands in their periphery. Today, simulation models created by geospatial data mining (GDM) techniques are frequently used to model the effects of uncontrolled urban sprawl on land use/land cover (LULC) changes. However, most of these models have yet to consider the impact of ownership diversities. The study’s main purpose is to investigate the interactions and interdependencies between Spatiotemporal LULC changes and private land ownership and monitor the precipitous urban sprawl through simulation models created by GDM techniques. In this context, the Sancaktepe district of Istanbul, where the population growth is rapid and the LULC change is very excessive, was chosen as the study area. CORINE LULC data for 1990, 2000, and 2018 were used in the study. Two hybrid prediction models were produced for 2040 by integrating multi-layer perception (MLP) and support vector machine (SVM) algorithms with Markov Chains (MC) method. A base model was prepared with the intersection of these two to take protection precautions in the short and medium term. According to the results, it was determined by MLP that private land ownership significantly affects LULC. In addition, the pressure of urbanization on forests and agricultural lands, most of which are publicly owned, increases, and urban sprawl causes informal settlement.

Can Landuse Landcover changes influence the success of India's national clean air plans ?

India implemented a range of multifarious strategies to address the issue of substandard air quality. One such flagship scheme of government of India is National Clean Air Programme (NCAP), which recommends sector specific reduction in emissions and increase in forest cover etc. To reduce particulate matter concentrations by 40% in 2026 compared to 2019. The present study aims to gauge the impact of Land Use Land Cover (LULC) changes alone on success of NCAP, using weather research forecasting model with chemistry (WRF-Chem) and integrated geographical information system and remote sensing software Terrset. The findings elucidate that, by the year 2026, the Ventilation Coefficient (VC) in India's eastern, central, northern, and north-eastern regions is anticipated to register a decline ranging from 18% to 50% compared to the baseline year of 2019. Conversely, an increase of 17% is expected in the southern region. The alterations in Fallow Land, Barren and sparsely vegetated land, Urban and Built-up Land, and Tundra, contribute to these shifts, displaying varying percentage changes across distinct zones. Simulations indicate that these LULC changes are impeding the planned reduction in PM2.5 levels. Projections suggest an increase in PM2.5 levels as high as 13% in the eastern, central, northern, and north-eastern regions, accompanied by a decrease of 33% in the Southern zone of the country. Significantly, non-attainment cities in Himachal Pradesh and Maharashtra are expected to witness a substantial rise in PM2.5-induced premature mortality, with Pune city projected to experience over 24,525 additional premature deaths by 2026. A comparable examination conducted for the year 2022, utilizing actual LULC data, suggests that if the NCAP fails to effectively implement LULC changes, it may reduce this anticipated trade-off. Addressing this concern, the study employed WRF-Chem to simulate 60 combinations, proposing LULC enhancements conducive to improving VC. The results underscore the critical importance of preserving at least 36% of the LULC category of mixed forest land, encompassing plantations, orchards, and areas under shifting agriculture. Additionally, a reduction in barren land and fallow land emerges as pivotal for enhancing the ventilation coefficient. The study accentuates the necessity of refraining from further expansion in densely populated areas to counter these anticipated VC trends. This study provides valuable insights, highlighting the need to prioritize LULC management to effectively combat the alarming air pollution.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlıdere, and Elmalı), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

Hydrogeological Implications of Slope and Land Use/Land Cover Distribution in Part of Southern, Nigeria

The study focuses on the hydrogeological implications of slope and Land Use/Land Cover (LULC) distributions in the Aniocha and Oshimili regions of Nigeria. Understanding the spatial variations in these parameters is critical for effective groundwater management and sustainable land use planning in these areas. The aim is to assess the spatial distribution of slopes and LULC types and their impact on groundwater recharge and surface runoff in the study area. The study employs spatial analysis techniques to categorize slope data into five classes and LULC data into seven types for the years 2017 and 2023. The analysis covers Aniocha North, Aniocha South, Ika North, Ika South, Oshimili North, and Oshimili South. The slope analysis reveals that the largest area (1022.73 km²) falls within the 0-1.52° slope category, indicating predominantly flat terrain with high infiltration potential. The LULC analysis shows that in 2017, trees covered the most extensive area (2143.21 km²), which decreased to 1573.76 km² by 2023, indicating significant deforestation. Built areas expanded from 281.76 km² to 391.99 km², reflecting increased urbanization. Flat terrains (0-3.51°) are conducive to groundwater recharge due to their high infiltration rates, whereas steeper slopes (3.51-26.00°) exhibit higher runoff potential, impacting groundwater recharge negatively. The reduction in tree cover and expansion of built areas have significant hydrogeological implications. Deforestation reduces groundwater recharge potential, while urbanization increases surface runoff, potentially leading to flooding and reduced groundwater levels. The study highlights the need for integrated land and water management strategies to balance development with environmental sustainability. Protecting and managing flat areas for groundwater recharge, implementing soil conservation in steeper regions, and promoting sustainable agricultural practices are essential. Urban planning should incorporate green infrastructure to mitigate the impacts of increased impervious surfaces. This study provides a comprehensive analysis of slope and LULC changes over time and their hydrogeological implications, offering valuable insights for sustainable land and water resource management in the Aniocha and Oshimili regions.

A comprehensive systematic review: Impact of Land Use/ Land Cover (LULC) on Land Surface Temperatures (LST) and outdoor thermal comfort

Globally, urbanization-driven changes in Land Use and Land Cover (LULC) jeopardize Land Surface Temperatures (LSTs) and the outdoor thermal comfort of urban residents. While urban heat stress's impact on outdoor thermal comfort garners ample scholarly attention, research on the parallel influence of LULC remains limited. This paper comprehensively reviews prevalent tools for collecting LULC data, criteria for evaluating LST and outdoor thermal comfort, correlation measures and mitigation strategies. Focused on peer-reviewed publications from the last 25 years, our data is drawn from Scopus, Web of Science, Google Scholar and Science Direct.Our findings reveal Landsat and geographic information systems (GIS) as predominant datasets and processing platforms for LULC and LST information delineation. Common outdoor thermal comfort indices include the Universal Thermal Climate Index (UTCI), Physiologically Equivalent Temperature (PET) and Surface Urban Heat Island Intensity (SUHII). Validation employs Normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and Pearson's correlation. Geographically, Asia and Africa feature prominently in studies, with Europe comparatively underrepresented. We also highlight key LULC mitigation strategies, including augmenting vegetation in moderate climates, effective albedo management in warmer climates, and optimizing urban layouts for enhanced outdoor thermal comfort.

Modeling the effects of land use/land cover changes on river runoff using SWAT models: A case study of the Danjiang River source area, China

Land use/land cover (LULC) is a crucial factor that directly influences the hydrology and water resources of a watershed. In order to assess the impacts of LULC changes on river runoff in the Danjiang River source area, we analyzed the characteristics of LULC data for three time periods (2000, 2010, and 2020). The LULC changes during these periods were quantified, and three Soil and Water Assessment Tool (SWAT) models were established and combined with eight LULC scenarios to quantitatively analyze the effects of LULC changes on river runoff. The results revealed a decrease in the cropland area and an increase in the forest, grassland, and urban land areas from 2000 to 2020. Grassland, forest, and cropland collectively accounted for over 94% of the total area, and conversions among these land types were frequent. The SWAT models constructed based on the LULC data demonstrated good calibration and validation results. Based on the LULC data in three periods, the area of each LULC type changed slightly, so the simulation results were not significantly different. In the subsequent LULC scenarios, we found that the expansion of cropland, grassland, and urban areas was associated with increased river runoff, while an increase in forest area led to a decrease in river runoff. Among the various LULC types, urban land exerted the greatest influence on changes in river runoff. This study establishes three SWAT models and combines multiple LULC scenarios, which is novel and innovative. It can provide scientific basis for the rational allocation of water resources and the optimization of LULC structure in the Danjiang River source area.

Which land cover product provides the most accurate land use land cover map of the Yellow River Basin?

Precise land use land cover (LULC) data are essential for understanding the landscape structure and spatial pattern of land use/cover in the Yellow River Basin (YRB) to regulate scientific and rational territorial spatial planning and support sustainable development. However, differences in the multiple sets of LULC products in portraying the land composition of the YRB limit our understanding of the land cover composition in this region. To address this issue, this study chose five sets of open and high spatiotemporal LULC data in 2020, namely, CLCD, LSV10, ESRI10, CLC_FCS30, and Globeland30, to evaluate the accuracy and consistency of classification in the YRB. Our results show that: (1) The LULC composition of the YRB in 2020 was mapped consistently by the five datasets. Grasslands, croplands, and woodlands constitute the major LULC types, accounting for 96% of the total area of the study area. (2) The correlation coefficients of the LULC types of any two of the five datasets ranged from 0.926 to 0.998, showing high land compositional consistency. However, among the five datasets, there were considerable differences in the areas of a single LULC type. (3) The classification consistencies of croplands, woodlands, grasslands, and water bodies were higher than 60% in any two datasets. The spatial consistencies of grasslands, croplands, and woodlands were higher than those of other LULC types. An area with better consistency can reach more than 50% of the average area of the corresponding land types, but grasslands were mixed with other LULC types in ESRI10 and GLC_FCS30. (4) According to the accuracy assessments, LSV10 data have the highest overall classification accuracy, 79.32%, and the classification accuracy of major land types is also higher than 70%; GLC_FCS30 data have the lowest overall accuracy, 70.14%. Based on these results, LSV10 can more accurately demonstrate LULC than the other four datasets. This study can be used as a reference for selecting land cover data, and it also highlights that the necessary assessments of consistency and accuracy are essential when conducting land use/cover change studies in a specific region.

Land Use/Land Cover Changes in Baicheng District, China during the Period 1954–2020 and Their Driving Forces

Temporal and spatial variations in land use/land cover (LULC) and their driving factors are direct reflections of regional natural and anthropogenic impacts. To explore the pathways for green upgrading development in ecologically fragile areas, this study focused on Baicheng, located in the northern agropastoral transition zone, China. Based on the topographic map of 1954 and Landsat remote sensing images taken from 1976 to 2020, the spatial distribution of LULC data for the study area in 1954, 1976, 1988, 2000, 2010, and 2020 was obtained. The temporal and spatial characteristics of LULC changes and their driving factors under the combined influence of human activities and climate were analyzed using dynamic degree, flowchart, spatial analysis, and principal component analysis. The results indicate that (1) the dominant LULC type in Baicheng is cropland. By 2020, dry land accounted for over 41% of the total area, while the area of saline–alkaline land increased the most, and grassland decreased most drastically. (2) The dynamic degree of different LULC types ranked from highest to lowest as follows: paddy field > unused land and other types > woodland > saline–alkaline land. (3) LULC Changes in Baicheng were mainly influenced by human activities and economic development, especially regional gross domestic product and the sown area of crops. These research findings can provide a scientific basis for formulating sustainable development and protection strategies to ensure regional green upgrading development.

DETERMINATION OF LANDSCAPE TYPOLOGY WITH REMOTE SENSING TECHNIQUES AND CORRELATION WITH DIFFERENT BIOCLIMATIC CONDITIONS: THE CASE OF BUYUK MENDERES BASIN

Abstract. Due to the multidisciplinary nature of landscape research, there are different systems and methods for landscape identification and classification. The methodology of the study is based on landscape characterization, which is based on the pre-selection of geo-ecological and land-use related features of the landscape. For this purpose, landscape character types were identified using climate classes, land use/land cover (LULC), soil and morphological structure. In obtaining the LULC data, which is one of the major inputs of the study, 36-band images were created covering four seasons by producing NDVI (Normalized Difference Vegetation Index); LAI (Leaf Area Index); MSAVI2 (Modified Soil-Adjusted Vegetation Index); fCOVER (fraction of Cover); indices, which are important in determining LULC, together with the basebands in the remotely sensed Sentinel 2 satellite data. LAI is used to determine the amount of leaves per unit area; NDVI is used to determine the green areas in the area; fCOVER is used to determine the ratio of the study area to the area covered by vegetation, MSAVI2 is used to determine the amount of vegetation cover in the study area by using the soil reflectance correction factor (L). The phenological structure of the species was statistically determined with the indices derived from satellite images, and the bioclimatic condition and landscape character type were associated with the species.

Dynamics of land use land cover change in an East African inland valley wetland

AbstractThe dynamics of inland valley wetlands landuse landcover (LULC) changes are of ecological and economical significance hence necessitating up‐to‐date datasets of resource status. Nalwekomba inland valley wetland is highly agriculturally utilised yet ecologically important wetland threatened with conversion. Historical LULC data sets as bench marks justifying wetland conservation are lacking. Therefore, the spatial–temporal LULC changes in Nalwekomba inland valley wetland were analysed through a combination of remote sensing and GIS, using satellite images of 1990, 2000, 2010 and 2020 as reference years. Results revealed variable dominance of grassland and tree cover for the wetland and its catchment over the years; 32.7% and 35.3% and 30.9% and 29.8%, respectively, for 1990 and 41.4% and 30.4% and 26.3% and 29.1% for 2020. The 30‐year analysis revealed increasing area‐shifts for cropland and built‐up/settlement of 265.7% and 76.2%, respectively, reducing cover for shrubland by 78.31% in the wetland. Similar trend emerged for its catchment. Highest net cover gains and losses identified between 1990 and 2000 were for cropland and tree cover respectively. The observed trend points to the need for landuse planning and landcover management to enhance ecosystems' resilience to support sustainable livelihoods dependent on this ecosystem.

A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China

Reliable hydrological data ensure the precision of the urban waterlogging simulation. To reduce the simulation error caused by insufficient basic data, a multi-strategy method (MHFE) for extracting hydrological features is proposed, which includes land use/land cover (LULC) extraction (LE) and digital elevation model (DEM) reconstruction (DR). First, the high-resolution remote image, satellite DEM, precipitation, flood points and depth, and planned LULC were collected. Second, the buildings, roads, and other areas of the satellite image were segmented using the U-Net model, and the LULC data with drainage features were extracted by combining the segmentation result with the planned LULC and drainage data. Then, the terrain features of the road were enhanced to construct high-precision DEM based on the fusion of multi-source data, such as elevation points, LULC, and satellite DEM. Finally, the waterlogging model was implemented under different return periods of rainfalls and typhoon rainfall to obtain the waterlogging distribution and water depth. The simulation results were compared with historical waterlogging event data and water depth observations. The results indicated that the proposed method significantly improved the accuracy of the simulation. In terms of identifying the waterlogging points, the average F1 score increased by 0.36, 0.20, and 0.07 compared to the raw model and the single LE and DR methods, respectively. In terms of water depth simulation, the average Nash-Sutcliffe efficiency (NSE) was increased from −0.24 to 0.86, with DR and LE contributing to 79.1 % and 20.9 %, respectively. The principal contribution and novelty of this paper is to explore the generic method that enhance the hydrological data, and the findings of this study improved the performance of urban waterlogging simulation.

Predicting water quality using partial least squares regression of land use and morphology (Danjiangkou Reservoir, China)

According to previous hydrology studies, both the land-use type and the spatial pattern of landscapes affect the quality of water in river basins. However, demonstrating and quantifying the effects of landscape patterns on water quality remain challenging. The present study was conducted in the water source protection zone of Danjiangkou in Hubei Province, China. Based on locations of water quality monitoring stations in the area of the reservoir and the hydrological analysis module in the ArcGIS software, the study area was divided into 10 sub-basins. The “source-sink” landscape pattern obtained from land use/land cover (LULC) analysis of the sub-basins was then used as the medium. Binary data characterized as foreground and background required for morphological spatial pattern analysis (MSPA) corresponded to source and sink landscape types. Partial least squares regression (PLSR) analysis of water quality and the MSPA data were integrated to comprehensively evaluate the impacts of land-use types and landscape pattern changes on water quality in the basin. The results show that land use, landform and spatial landscape pattern have a important impact on the water quality of the watershed. When using the PLSR model based on the “source-sink” morphological spatial pattern to predict water quality, compared to the model analysis solely based on LULC, the MSPA based PLSR improves the principal component grouping, x-variable interpretation rate, y-variable interpretation rate (R2), model prediction rate (Q2), and model prediction accuracy. This study shows that that landscapes is demonstrated to affect water quality and that the use of multiple methods - PLSR, LULC and MSPA data - is a useful complement to hydrological literature. PLSR modeling combined with LULC analysis and MSPA data can accurately predict the water quality of the basin. The use of this improved model for water quality prediction is the focus of this study.