Land Use Cover Research Articles

A Comparative Study of Land Use Land Cover (LULC) Change Detection: A Case Study of Tehsil Babuzai, Swat (Pakistan)

Tehsil Babuzai, District Swat, Khyber Pakhtunkhwa, Pakistan, has experienced a fast population increase, substantially influencing land use and land cover (LULC), raising ecological and environmental issues. This study examines changes in LULC between 2001 and 2020 using US Geological Survey (USGS) satellite images. The paper analyses six primary land cover classes: farmland, built-up area, woodland, water bodies, rangeland, and barren Land using maximum likelihood supervised classification. The findings show a significant rise in rangeland (from 49.1% to 53.7%), built-up areas (from 8.5% to 13.7%), and barren Land (from 5.1% to 11.1%). On the other hand, there is a noticeable decline in the number of water bodies (from 2.1% to 1.6%), agriculture (from 21.2% to 12.2%), and forest cover (from 13.7% to 7.5%). These alterations underscore the detrimental consequences of urban growth on the area's natural resources, demanding prompt and deliberate planning to protect the ecosystem and guarantee sustainable development. This research is crucial for understanding land use changes' dynamics and impacts, providing valuable insights for policymakers and urban planners. It highlights the need for sustainable land management practices to mitigate adverse environmental effects and promote balanced regional development.

Land use-land cover dynamics in Boyo wetland using remote sensing and GIS for the period 1992–2022

ABSTRACT Land use changes are one of the main factors contributing to the degradation of wetland ecosystems. The objective of this study was to describe land use and land cover (LULC) change in the Boyo Wetland from 1992–2022. We employed remote sensing and GIS to obtain data for this study. As the main data source, Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) images were used. We downloaded Landsat images from the USGS website that were cloud-free. A supervised maximum likelihood classifier algorithm was used for the classification process. Five LULCs are identified, which include farmland, built-up areas, bare land, grazing land, and wetland. For the duration of the research period, the rate of increase in the percent of farmland, built-up, and barren land was observed to be 18.24%, 9.48%, and 4.52%, respectively, while a decreasing pattern was observed in grazing land and wetland by − 19.44% and − 12.79%, respectively. The expansion of agriculture and built-up areas owing to population growth are the primary factors influencing change in the study area. Therefore, a rapid protective measure should be taken to rehabilitate the study wetland.

Associations of neighbourhood attributes with depression in mid-age and older adults: the moderating role of traffic-related air pollution and neighbourhood socioeconomic status

ABSTRACT We aimed to examine whether features of the natural, built and socioeconomic neighbourhood environments were related to depressive symptoms in Australian mid-age and older adults; and whether traffic-related air pollution (TRAP) (NO2) and area-level SES moderated the associations. A total of 4141 of participants (M age = 61 years) were included in this cross-sectional study. Neighbourhood environmental characteristics included percentages of parkland, blue space and commercial land use, population density (persons/hectare), street intersection density (intersections/km2) within 1 km residential buffers. Annual mean concentrations of nitrogen dioxide (NO2) were included. In total-effect models, greater street intersection connectivity was associated with fewer depressive symptoms. Population density was positively associated with depressive symptoms in direct-effect models. Residents from disadvantaged areas with low levels of NO2 and with greater commercial land use coverage reported fewer depressive symptoms. In contrast, residents were more likely to report depressive symptoms with greater coverage of commercial land use in high SES neighbourhoods with low levels of NO2. Street connectivity and population density play a role in the maintenance of mental health in older adults. NO2 and area-level SES are moderators of commercial land use-depression associations, and this knowledge can better inform planning of commercial destinations and facilities in urban environments.

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

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

Mapping of Groundwater Recharge Zones in Hard Rock Aquifer through Analytic Hierarchy Process in Geospatial Platform

Extensive use of groundwater is a result of the growing population; in relation to this, studies have focused on groundwater conservation measures. This study identified groundwater artificial recharge zones (GWARZs) in the upper Manimuktha sub-basin through the application of remote sensing and GIS. A spatial analysis using the analytical hierarchical process (AHP) and weighted overlay analysis (WOA) was employed by integrating several spatial thematic layers such as geology, geomorphology, aquifer thickness, lineament density (LD), drainage density (DD), soil, slope, rainfall, and land use/land cover (LULC) in order to classify the GWARZs. The geomorphology along with lithology, higher aquifer thickness, low lineament densities, higher drainage densities, and gentle slope regions, were identified as suitable areas for artificial recharge zones. The study area was divided up into five classifications based on the integration analysis: excellent (41.1 km2), good (150.6 km2), moderate (123.9 km2), bad (125.5 km2), and very poor (57.7 km2). Excellent and good GWARZs were identified in the eastern and central regions of the study area. The final outcomes of this research were evaluated with seasonal electrical conductivity (EC) variations. The majority of samples with minor seasonal EC variations were observed in the excellent and good GWARZ categories. The results showed that the spatial analysis tool is useful for GWARZ delineation and sustainably managing groundwater resources.

A landscape index for indicating the spatio-temporal dynamics of carbon storage in an opencast coal mine

Coal is China’s main resource, with open-pit mining accounting for a significant portion of global production. However, this activity, including mining and ecological restoration, can have a definite impact on ecosystem carbon storage and its distribution; its associated factors are also unclear. In this paper, we quantify the carbon storage changes in Haerwusu coal mine, a typical large-scale coal mine in China, based on land use/land cover (LULC) characteristics, and analyze the impact factors of carbon density from 2007 to 2022 by integrating the InVEST model with the landscape ecological function contribution ratio and multiple regression model. The results are as follows. (1) Carbon storage decreased from 159.95 × 104 to 147.51 × 104 from 2007 to 2017 and then increased to 151.91 × 104 to 2022. (2) The degree of coordination between carbon storage forest and grassland area landscape pattern coupling ranged from 0.887 to 0.867 from 2007 to 2022, with the lowest point at 0.720 in 2012. (3) Carbon storage was significantly related to vegetation indices, temperature, and elevation, and these factors can explain 37.5% of the carbon storage spatial variability; stepwise regression analysis showed that the integration of landscape patterns, such as Shannon’s diversity index (SHEI) and the aggregation index (AI), could improve the explanation by 1.4%. (4) Based on the analysis of the landscape ecological function contribution ratio, the carbon storage-sensitive areas can be classified into three levels: extremely sensitive areas ranging 0 to 4 km from the mine, sensitive areas ranging 4 to 8 km, and insensitive areas ranging beyond 8 km. This study proposes a strategy for analyzing changes of carbon storage in coal mines, highlights the important role of landscape patterns in influencing carbon storage, and provides a reliable reference support for the ecological management of coal mines.

Integrated Supervised Classification of LULC in Identifying Musang King Durian Illegal Farming Location

Musang King Durian (MKD) is in high demand due to its unique taste and aroma; hence, some opportunistic farmers grow it on a large scale. The issue arises when some MKD is planted on encroached land in remote places, making it impossible for local authorities to locate them. This study proposes to examine the changes in land use land cover (LULC) within the Benom Permanent Forest Reserved area that were reported to have land invasion activities caused by illegal MKD plantations between 2019 and 2022 using Sentinel imagery. The objectives were to investigate the location of illicit MKD farming at Mount Benom based on the interpretation of LULC changes and Normalized Different Vegetation Index (NDVI) analysis. ArcGIS and eCognition Developer software were used for data processing and analysis to identify water bodies, bare land, green areas, forests, and potential illegal MKD plantings (PIDP) areas. Between the years 2019 and 2022, it was found that there has been a significant rise in water bodies (36%), green areas (34%), and forests (19%). However, the potential illegal MKD plantation area fell by 59%, while bare land declined by 35%. These downsizing changes might occur due to illegal MKD destroyed operations executed in 2019 by the forestry department in Raub, Pahang. Keywords: illegal farming, image classification, land use land cover, supervised classification

Geospatial forest fire risk assessment and zoning by integrating MaxEnt in Gorkha District, Nepal

Forest fires are an imminent danger to natural forest ecosystems, and carrying out zoning studies and forest fire risk assessments are of great practical significance in steering fire prevention, minimizing fire incidents, and limiting the environmental consequences of fire. Using the Gorkha district of Nepal as a case study, this study used remotely sensed high-temperature fire data as the forest fire sample. Nine parameters related to topography, climatic conditions, vegetation, and human intervention were used as environmental variables affecting fire occurrence. Next, a MaxEnt forest fire risk assessment model was generated with GIS and R, which analysed the contribution, significance, and responses of environmental variables to the forest fire in Gorkha District. The findings demonstrate that (1) following a test of sample locations for forest fires, the MaxEnt model has excellent relevance and practicality when applied to fire risk assessment; (2) Out of 2747 fire points in the forest, only 110 Spatio-temporally independent fire points were used for the model building having high and normal confidence level. Regarding Area Under Curve (AUC) values, the training data yielded results of 0.875, while the test data produced acceptable results of 0.861 with a standard deviation of 0.0322; (3) the importance of climatic and Land Use Land Cover (LULC) variables to forest fire are 56.2 % and 32.9 %, respectively, and their contribution to forest fire are 32 % and 47.6 %, respectively. (4) There are numerous and intricate ways that environmental factors influence forest fires. The forest fire response curves to the nine chosen environmental variables are complex and nonlinear rather than linear; Maximum temperature of the warmest month (bio_5), Isothermality (bio_3), Precipitation of Driest Quarter (bio_17) and mean Diurnal Range (bio_2) bear a nonlinear positive link with the possibility of forest fires. In contrast, elevation, slope, temperature seasonality (bio_4), distance from the settlement, and LULC have a favorable stimulating response to the possibility of forest fires within an appropriate interval. (5) In Gorkha, there are geographical differences in the risk of forest fires. Only 12.83 % of the whole area is made up of areas at significantly high risk or above, compared to 87.17 % for high-risk and below.

Assessment of groundwater potential zones using GIS-based multicriteria decision analysis: a case study on Enemor and Ener Woreda, Central Ethiopia Region

ABSTRACT Groundwater is one of the most important contributors to fresh water for humankind in the world. The knowledge of groundwater distribution can play a significant role in the planning and implementation of groundwater management strategies. The major aim of this study was to assess the groundwater potential zone using geographic information system (GIS)-based multicriteria decision analysis in the Enemor and Ener Woreda, Ethiopia. The factors considered in this study are rainfall, geology, slope, drainage density, land use/land cover (LULC), lineament density, and soil type. As a result of the analytic hierarchy process (AHP), it was determined that rainfall and geology are the most influencing factors based on their scoring higher weights. The groundwater potential zone of the study area was categorized into five classes, namely, very low, low, moderate, high, and very high. On analysis, most portions of the study area are found to be under the moderate situation, which covers around 540.23 km2 (65.6%) of the study area, whereas very low, low, high, and very high potentiality classes cover 0.002, 10.99, 21.93, and 0.55% of the area, respectively. Approximately 92% of the observed boreholes agreed with the developed map. Finally, the output of this study is important to regional as well as local policymakers for the exploitation of groundwater or management purposes.

Relationship between Landscape Pattern and Human Disturbance in Serbia from 2000 to 2018

This study intends to verify how the alteration of the landscape configuration, represented by different metrics of configuration and diversity, is related to the intensity of human disturbance. The objectives of the study are: (1) to quantify the change in land use/land cover (LULC) patterns and the degree of human disturbance in Serbia between 2000 and 2018, and (2) to study the relationship between LULC configuration and the impact resulting from human disturbance under different levels of intensity, to understand how changing trends in landscape pattern can serve as indicators to estimate landscape changes resulting from human actions. The Hemeroby Index (HI) was calculated to quantify the impacts on ecosystems resulting from disturbance caused by human actions. Based on the analysis of the variation in the value corresponding to the HI for the period between 2000 and 2018, the level of naturalness increased by only 5% of the territory of Serbia, with this change being verified mainly in SE Serbia. The landscape pattern was quantified using a set of LULC metrics. We used the Spearman method to identify the existing statistical correlations between the geometric parameters of the landscape and the HIs values. At the landscape level, the Mean Shape Index, Edge Density, Mean Patch Fractal Dimension, and Shannon Diversity Index show a strong negative correlation with HI. This correlation suggests that landscapes with greater structural complexity are good indicators of low levels of hemeroby. At the class level, Edge Density and Mean Patch Size correlate significantly with the HI for artificial surfaces, agricultural areas, forests, and semi-natural areas.

Assessing the Impacts of Landuse-Landcover (LULC) Dynamics on Groundwater Depletion in Kabul, Afghanistan’s Capital (2000–2022): A Geospatial Technology-Driven Investigation

Land use/land cover (LULC) changes significantly impact spatiotemporal groundwater levels, posing a challenge for sustainable water resource management. This study investigates the long-term (2000–2022) influence of LULC dynamics, particularly urbanization, on groundwater depletion in Kabul, Afghanistan, using geospatial techniques. A time series of Landsat imagery (Landsat 5, 7 ETM+, and 8 OLI/TIRS) was employed to generate LULC maps for five key years (2000, 2005, 2010, 2015, and 2022) using a supervised classification algorithm based on Support Vector Machines (SVMs). Our analysis revealed a significant expansion of urban areas (70%) across Kabul City between 2000 and 2022, particularly concentrated in Districts 5, 6, 7, 11, 12, 13, 15, 17, and 22. Urbanization likely contributes to groundwater depletion through increased population growth, reduced infiltration of precipitation, and potential overexploitation of groundwater resources. The CA-Markov model further predicts continued expansion in built-up areas over the next two decades (2030s and 2040s), potentially leading to water scarcity, land subsidence, and environmental degradation in Kabul City. The periodic assessment of urbanization dynamics and prediction of future trends are considered the novelty of this study. The accuracy of the generated LULC maps was assessed for each year (2000, 2005, 2010, 2015, and 2022), achieving overall accuracy values of 95%, 93.8%, 85%, 95.6%, and 93%, respectively. These findings provide a valuable foundation for the development of sustainable management strategies for Kabul’s surface water and groundwater resources, while also guiding future research efforts.

Restore polder and aquaculture enclosure to the lake: Balancing environmental protection and economic growth for sustainable development

The restoration of lakes and their buffer zones is crucial for understanding the intricate interplay between human activities and natural ecosystems resulting from the implementation of environmental policies. In this study, we investigated the ecological restoration of shallow lakes and buffer zones in the Yangtze-Huaihe River Basin, specifically focusing on the removal of polder and aquaculture enclosure areas within the lakes. By examining data from eight shallow lakes and their corresponding buffer zones, encompassing lake morphology, water quality parameters, and land use/land cover (LULC) data spanning from 2008 to 2022, which shed light on the complex relationships involved. During the process of restoring polder and aquaculture enclosure areas, we observed a general decrease in the extent of polders and aquaculture enclosures within the lakes. Notably, the removal of aquaculture enclosures had a more pronounced effect (reduction rate of 83.37 %) compared to the withdrawal of polders (reduction rate of 48.76 %). Linear regression analysis revealed a significant decrease in the concentrations of seven water quality parameters, including COD, CODMn, TN, TP, NH3-N, Chl-a, and F, while pH and DO factors exhibit a distinct increasing trend. The results of redundancy analysis and Pearson correlation analysis demonstrated significant correlations between the area of polders and aquaculture enclosures and the changes in lake water quality. Encouragingly, the withdrawal of polders and the removal of aquaculture enclosures had a positive impact on the lake water quality improvement. In contrast, the LULC in the buffer zones of the lakes experienced a gradual decline owing to land degradation, resulting in a reduction in ecosystem service value (ESV). These results offer valuable support for policymakers in their endeavors to restore lake water quality, mitigate the degradation of buffer zones land, and promote the sustainable development of land and water resources.

Land Use-Land Cover Changes and Associated Drivers: A Case of Salooni Block of Chamba

Land Use-Land Cover (LULC) changes are the anthropogenic alteration of the natural landscape. The LULC changes have been generated by a variety of factors, including natural, demographic, and economic ones, which put pressure on the land and had a variety of environmental effects. The transformation in Landscape is affecting the livelihoods of people in numerous ways. Therefore, it is crucial to understand the patterns in land use in order to determine how land should be developed, managed, and planned in order to understand the way it will be utilised in the future. In the present study, Salooni Block of Chamba district, Himachal Pradesh was subjected to a first-ever attempt at land-use change detection for the years 2002 and 2022. Satellite imageries of Landsat-7 Enhanced Thematic Mapper (ETM+) Landsat-8 Operational Land Imager (OLI) were used for landuse maps of 2002 and 2022 respectively. The classification was completed using a supervised classification technique and the USGS Land use Classification Scheme at Level I classes, including Agricultural Land, Barren Land, Built-up, Forest, Pasture Land, Snow Cover and Waterbody. Furthermore, fifty people aged 50 and above were interviewed to understand the local factors responsible for LU/LC change. The result indicates a rise in the forest cover, which now accounts for 48.2% of the total area. Since 2002, there has been a 2.3-fold rise in built-up land, which is a glaring indication of the pressure on land. The total area of agricultural and pasture land is decreasing, while the percentage of barren land has increased by 2%. To preserve the sustainability and food security of the ecologically vulnerable environment, stringent land use planning needs to be enforced in the mountainous region. The results of the LU/LC dynamics discussed above can be used to establish policies and strategies for restoring and enhancing the sustainability of the environment as well as the carrying capacity of the land.

Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa

Capturing the phenological responses of crops in diverse, small-scale agricultural systems is one of the most notable remote sensing research gaps in Eastern Africa. Because of the heterogeneous and complex agricultural environments, remote sensing methodologies successfully implemented elsewhere yield poor results in Eastern Africa. The reliance on local rainfall and dry spells can lead to alterations in planting patterns, impacting growing season timing and yield, underscoring the importance of high temporal resolution for capturing crop phenology. We generated a high-resolution land use/land cover (LULC) classification map of 10 m using Sentinel-1 and Sentinel-2 imagery together with a large set of reference data acquired in situ in 2022. The LULC classification yielded an overall accuracy of 87.9% and Cohen's kappa value of 0.83. Based on the LULC map, we investigated the phenological metrics of the most important crops in the Kamuli district of Eastern Uganda by combining a multi-sensor harmonization approach, thresholding method, and cloud computing. Previously unavailable Landsat 9 band adjustment coefficients were derived. By harmonizing Landsat (LS) 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 and 9 Operational Land Imager (OLI), with Sentinel-2 Multispectral Instrument (MSI), the phenological patterns during the March to May growing season were captured using a dense temporal Normalized Difference Vegetation Index (NDVI) time series. The seasonal onset for most crop types was in the end of March to middle of April while the end of the growing season occurred from the end of June to middle of July. Investigation of the 13 crop types using a high-resolution LULC map and dense, harmonized NDVI time series signifies the first field-level phenological metrics estimation of smallholder farming systems in Eastern Africa. Future research should focus on expanding the methodology in space and time to provide insights into past and current growing season development.

Selected Driver Variables for the Simulation of Land-Use and Land-Cover Change for the Republic of Djibouti: A Study from Semi-Arid Region

Abstract. This study aims to integrate driver variables with a land use change model (LCM) to explore their impact on the natural environment within the context of land-use changes in the Republic of Djibouti, considering possible Business-as-usual scenarios. Secondary data from 1990 and 2012 on land use land cover (LULC) were analyzed, with a 2022 map generated by adopting the same method of secondary data used (random forest classification in Google Earth Engine (GEE)) for validation. Eight key driver variables were utilized to model plausible future land cover (2035) for Djibouti. Statistical outputs and change maps from the LCM were compared to gauge historical change estimates and simulated scenarios. Analysis from 1990 to 2022 highlights significant land use and cover changes spurred by urbanization, environmental factors, and economic development. Barren land and bushland dominated, while built-up areas and water bodies expanded notably. Urbanization, agriculture, and climate change contributed to vegetation degradation, with declines in mangroves and increases in built-up areas. Water bodies also expanded during this period. Projections from the 2035 LULC map anticipate further urban expansion, underscoring the need for sustainable land management practices. In conclusion, comprehensive land-use planning, interdisciplinary approaches, and stakeholder engagement are deemed critical for addressing Djibouti's socio-economic and environmental challenges and steering towards a sustainable future. These simulated results offer valuable insights for regional governments to frame strategic policies and assess management actions for resource utilization amidst urbanization and population growth trends.

A Study on Urban Heat Island (UHI): Challenges and Opportunities for Mitigation

Urban Heat Island (UHI) is a significant issue in metropolitan regions in India, where cities are experiencing increasing temperatures ranging from 1 to 5 °C. Although UHI is a global phenomenon, it particularly impacts major cities in India, and there is a need for more research in this field. This review compares the latest global developments in UHI research with the current research in India, highlighting areas where India may lag. The review notes that there is a lack of research on UHI in numerical modeling in India, which is a limitation. Because of this, finding a solution to UHI and putting it into law requires more focus and effort. Urgent measures are required to lessen the effects of UHI in urban areas of India. Parameter outputs such as Land Surface Temperatures (LST), Normalized Difference Vegetation Index (NDVI), Land Use/Land Cover (LULC), and Normalized Difference Built-up Index (NDBI) were also compared with the results of prior research and modules in the review. Using correlations from three prior studies, the analysis demonstrated that the temperature deviation approximation was 0.157 °C better than last year's manuals. Consequently, additional research is needed to address urban heat islands (UHI) in Indian cities, focusing on numerical modeling. Prompt legislative and policy responses are also necessary. The review emphasizes the importance of ongoing research into UHI in India and measures to mitigate its effects.

Analysis of landscape pattern and ecological risk change characteristics in Bosten Lake basin based on optimal scale

Amidst swift socioeconomic progress, urbanization is intensifying, necessitating robust methodologies for environmental quality monitoring. Landscape Ecological Risk (LER) has emerged as a pivotal tool in this context. Extensive research has focused on the land utilization rates within watersheds, scrutinizing them through the lens of land use/land cover (LULC) dynamics. This study delves into the LULC attributes of the Bosten Lake Basin (BLB) across three temporal snapshots: 2000, 2010, and 2020. Grasslands and bare ground made up the vast majority area of the BLB. Selected twelve landscape pattern indices, analyzed their response curves with respect to grain size, determined the optimal grain size to be 150 m, and analysed its optimum amplitude of 10 km × 10 km on the basis of the semi-variance function, thus determined the optimal scale for analyzing the landscape pattern. Exploring scale effect facilitates a nuanced examination of the landscape patterns and land use efficacy within BLB. From 2000 to 2020, the landscape pattern showed less fragmented, less aggregated, and more spatially heterogeneous. The landscape ecological risk index model has been constructed for the investigation of the LER change characteristics of the BLB. Lowest-risk zones and lower-risk zones of LER predominate in BLB. In 2000, 2010 and 2020 the combined lower and lowest risk zones represent 62.02 %, 59.37 % and 58.44 % of the catchment area respectively. Finally, analyzed the spatial autocorrelation variation characteristics of LER. The global Moran' I index of BLB for the three periods from 2000 to 2020 exceeded 0.7. This study establishes theoretical and practical foundation for future multi-scale landscape ecological risk studies in eco-fragile regions.

Sentinel 1a-2a Incorporating an Object-Based Image Analysis Method for Flood Mapping and Extent Assessment

Abstract. This study presents flood extent extraction and mapping from Sentinel images. Here we suggest an algorithm for extracting flooded areas from object-based image analysis (OBIA) using Sentinel-1A and Sentinel-2A images to map and assess the flood extent from the beginning to one week after the event. This study used multi-scale parameters in OBIA for image segmentation. First, we identified the flooded regions by applying our proposed algorithm on the Sentinel-1A. Then, to evaluate the effects of the flood on each land-use/land cover (LULC) class, Sentinel-2A images is classified using the OBIA after the event. Besides, we also used the threshold method to compare the proposed algorithm applying OBIA to determine the efficiency in computing parameters for change detection and flood extent mapping. The findings revealed the best performance for the segmentation process with an Object Fitness Index (OFI) is 0.92 when the scale parameter of 60 is applied. The results also show that 2099.4 km2 of the study area is flooded at the beginning of the flood. Furthermore, we found that the most flooded LULC classes are agricultural land and orchards with 695.28km2 (32.4%) and 708.63 km2 (33.7%), respectively. In comparison, about 33.9% of the remaining flooded area has occurred in other classes (i.e., fish farm, built-up, bare land and water bodies). The resulting object of each scale parameter was evaluated by Object Pureness Index (OPI), Object Matching Index (OMI), and OFI. Finally, our Overall Accuracy (OA) method incorporated field data using the Global Positioning System (GPS) shows 93%, 90%, and 89% for LULC, flood map (i.e., using our proposed algorithm), and threshold method, respectively.

Evaluation of urban flood susceptibility through integrated Bivariate statistics and Geospatial technology.

Flood disasters are frequent natural disasters that occur annually during the monsoon season and significantly impact urban areas. This area is characterized by impermeable concrete surfaces, which increase runoff and are particularly susceptible to flooding. Therefore, this study aims to adopt Bi-variate statistical methods such as frequency ratio (FR) and weight of evidence (WOE) to map flood susceptibility in an urbanized watershed. The study area encompasses an urbanized watershed surrounding the Chennai Metropolitan area in southern India. The essential parameters considered for flood susceptibility zonation include geomorphology, soil, land use/land cover (LU/LC), rainfall, drainage, slope, aspect, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The flood susceptibility map was derived using 70% of randomly selected flood areas from the flood inventory database, and the other 30% was used for validation using the area under curve (AUC) method. The AUC method produced a frequency ratio of 0.806 and a weight of evidence value of 0.865 contributing to the zonation of the three classes. The study further investigates the impact of urbanization on flood susceptibility and is further classified into high, moderate, and low flood risk zones. With the abrupt change in climatic scenarios, there is an increase in the risk of flash floods. The results of this study can be used by policymakers and planners in developing a preparedness system to mitigate economic, human, and property losses due to floods in any urbanized watershed.

Effect of intra-year Landsat scene availability in land cover land use classification in the conterminous United States using deep neural networks

The Landsat archive having consistent revisit times, near global extent and extensive multi-decadal temporal coverage offers a unique opportunity for land cover land use product generation. Along with this vast volume of freely available data, new classification methods based on deep learning have improved modeling capabilities. This manuscript investigates the effect of intra-annual Landsat scene availability in the accuracy of land cover land use classification in the conterminous United States. More specifically, we seek to quantify the effect of: i) increased monthly scene availability, and ii) specific months that may result in higher classification accuracy across different classes. Identifying specific months with comparable classification accuracy to the entire time series could offer significant computational gains for large-scale mapping. Our experiment incorporated deep learning classifiers and a wide range of reference data across the continental United States. Results were contrasted between five large U.S. climatic regions to further differentiate this intra-annual effect. Our findings indicate that the total number of months can have a highly variable effect in the classification accuracy ranging from minor (a few percentage points in terms of class F1 accuracy) to extremely beneficial (approaching 50% F1 improvement moving from four to twelve month observations). The benefit of increased month observations varied among climatic regions and classes: when all climate regions were combined, the grass/shrub and cultivated classes improved their F1 accuracy up to 30%, while the water class saw the least improvement of about 5%, partially due to its limited room for improvement. The effect of specific month combinations was also examined, where the total number of months was kept constant and the included months varied. The difference between the best month combination and the median combination value was estimated to be as high as about 30% for the four monthly observations scenario and the grass/shrub class. Further validation of the month selection importance comes from an example implementation scenario where F1 improvements can be as high as 10%. Our work demonstrated that month selection may offer such benefits that in some classes and climatic regions this time selection optimization is an inevitable choice due to large accuracy improvements. Also, the potential data reduction with targeted month selection would be particularly appealing to large-scale classification tasks. Due to the large extent of the climatic regions further studies are needed to quantify a more localized effect along with explanation of potential drivers.