Land Use Cover Classification Research Articles

Impact of land use/land cover changes on groundwater resources in Al Ain region of the United Arab Emirates using remote sensing and GIS techniques

Urbanisation causes land degradation problems, including an increased pressure on natural resources and management of water resources. This study aims to investigate the impact of the spatio-temporal dynamics of land use land cover (LULC) changes on groundwater table in the region of Al Ain, United Arab Emirates (UAE), from 2006 to 2016. The Landsat images, Landsat ETM for 2006 and Landsat 8 for 2016, were acquired from the earth explorer site. A semi-supervised hybrid classification method was used for image classification and post-classification techniques for LULC change detection. The study area was categorised into six major LULC classes. These are agriculture/farms/oasis, gardens/playgrounds, urban areas, sandy areas, lake and mixed urban/sandy areas. Accuracy assessment of LULC were evaluated using confusion matrix and ground truthing. The obtained land use and land cover maps were also correlated with spatial groundwater table maps prepared with groundwater data. It was found that agriculture/farms/oasis and urban areas expanded from 42,560 ha to 45,950 ha (7.38%) and from 8150 ha to 9105 ha (10.49%) from 2006 to 2016, respectively. The corresponding water demand was increased by 9.56% and 22.22%, respectively. Natural sandy area was found to decrease by 8.10%. As groundwater is major source of water for agriculture in this region, the spatial maps also revealead average declining rate of groundwater depth 40.44% with expansion of urban and agricultural areas over the last 10 years. The outcomes of the study would help concerning authorities for a sustainable management of its land and groundwater resources.

Predicting the impacts of land use/land cover change on land surface temperature using remote sensing approach in Al Kut, Iraq

Changes in land use/land cover (LULC) classes significantly impact land surface temperature (LST). This study predicted LULC change and their impacts on seasonal (summer and winter) LST variations in Al Kut, Iraq. Landsat TM/OLI images for the years 2000, 2010, and 2020 were used to estimate LULC and LST's past status using remote sensing (RS) techniques. Based on the past characteristics of the images future LULC and seasonal LST were predicted for year 2030. Support vector machine (SVM) algorithm was used to classify the LULC classes. Artificial neural network (ANN) algorithm was used to predict the future LST considering the LULC indexes as influential variables. Results suggest a significant increase in urban area by +8.74% and reduction in green cover by −25.87% from 2000 to 2020. Increment in maximum LST took place for both summer and winter season by 3.79 °C and 3.16 °C in last two decades. Maximum LST was recorded in urban area (48 °C) and water bodies (35 °C) exhibit minimum LST. The correlation study demonstrates a strong positive relation of LST with Normalized Difference Built-up Index (NDBI) and negative relation with Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). The prediction result also demonstrates increase in urban area (+17.02%) and loss of vegetation cover (−15.57%) for 2030. The maximum LST will likely to increase by 1.62 °C and 2.68 °C for summer and winter seasons in predicted year 2030. This study will provide effective guidelines for urban planners of Kut city for ensuring planned and sustainable urban development in future.

Geospatial analysis of wetlands based on land use/land cover dynamics using remote sensing and GIS in Sindh, Pakistan.

In this study, the Land Use/Land Cover (LULC) change has been observed in wetlands comprises of Manchar Lake, Keenjhar Lake, and Chotiari Reservoir in Pakistan over the last four decades from 1972 to 2020. Each wetland has been categorized into four LULC classes; water, natural vegetation, agriculture land, and dry land. Multitemporal Landsat satellite data including; Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images were used for LULC changes evaluation. The Supervised Maximum-likelihood classifier method is used to acquire satellite imagery for detecting the LULC changes during the whole study period. Soil adjusted vegetation index technique (SAVI) was also used to reduce the effects of soil brightness values for estimating the actual vegetation cover of each study site. Results have shown the significant impact of human activities on freshwater resources by changing the natural ecosystem of wetlands. Change detection analysis showed that the impacts on the land cover affect the landscape of the study area by about 40% from 1972 to 2020. The vegetation cover of Manchar Lake and Keenjhar Lake has been decreased by 6,337.17 and 558.18 ha, respectively. SAVI analysis showed that soil profile is continuously degrading which vigorously affects vegetation cover within the study area. The overall classification accuracy and Kappa statistics showed an accuracy of >90% for all LULC mapping studies. This work demonstrates the LULC changes as a critical monitoring basis for ongoing analyses of changes in land management to enable decision-makers to establish strategies for effectively using land resources.

Modeling and Prediction of Land Use Land Cover Change Dynamics Based on Land Change Modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia

Change of land use land cover (LULC) has been known globally as an essential driver of environmental change. Assessment of LULC change is the most precise method to comprehend the past land use, types of changes to be estimated, the forces and developments behind the changes. The aim of the study was to assess the temporal and spatial LULC dynamics of the past and to predict the future using Landsat images and LCM (Land Change Modeler) by considering the drivers of LULC dynamics. The research was conducted in Nashe watershed (Ethiopia) which is the main tributary of the Upper Blue Nile basin. The total watershed area is 94,578 ha. The Landsat imagery from 2019, 2005, and 1990 was used for evaluating and predicting the spatiotemporal distributions of LULC changes. The future LULC image prediction has been generated depending on the historical trends of LULC changes for the years 2035 and 2050. LCM integrated in TerrSet Geospatial Monitoring and Modeling System assimilated with MLP and CA-Markov chain have been used for monitoring, assessment of change, and future projections. Markov chain was used to generate transition probability matrices between LULC classes and cellular automata were used to predict the LULC map. Validation of the predicted LULC map of 2019 was conducted successfully with the actual LULC map. The validation accuracy was determined using the Kappa statistics and agreement/disagreement marks. The results of the historical LULC depicted that forest land, grass land, and range land are the most affected types of land use. The agricultural land in 1990 was 41,587.21 ha which increased to 57,868.95 ha in 2019 with an average growth rate of 39.15%. The forest land, range land, and grass land declined annually with rates of 48.38%, 19.58%, and 26.23%, respectively. The predicted LULC map shows that the forest cover will further degrade from 16.94% in 2019 to 8.07% in 2050, while agricultural land would be expanded to 69,021.20 ha and 69,264.44 ha in 2035 and 2050 from 57,868.95 ha in 2019. The findings of this investigation indicate an expected rapid change in LULC for the coming years. Converting the forest area, range land, and grass land into other land uses, especially to agricultural land, is the main LULC change in the future. Measures should be implemented to achieve rational use of agricultural land and the forest conversion needs to be well managed.

Evaluation of the status of land use/land cover change using remote sensing and GIS in Jewha Watershed, Northeastern Ethiopia

Evaluation of land use/land cover (LULC) status of watersheds is vital to environmental management. This study was carried out in Jewha watershed, which is found in the upper Awash River basin of central Ethiopia. The total catchment area is 502 km2. All climatic zones of Ethiopia, including lowland arid (‘Kola’), midland semi-arid (‘Woinadega’), humid highland (Dega) and afro alpine (‘Wurch’) can be found in the watershed. The study focused on LULC classification and change detection using GIS and remote sensing techniques by analyzing satellite images. The data preprocessing and post-process was done using multi-temporal spectral satellite data. The images were used to evaluate the temporal trends of the LULC class by considering the years 1984, 1995, 2005 and 2015. Accuracy assessment and change detection of the classification were undertaken by accounting these four years images. The land use types in the study area were categorized into six classes: natural forest, plantation forest, cultivated land, shrub land, grass land and bare land. The result shows the cover classes which has high environmental role such as forest and shrub has decreased dramatically through time with cultivated land increasing during the same period in the watershed. The forest cover in 1984 was about 6.5% of the total catchment area, and it had decreased to 4.2% in 2015. In contrast, cultivated land increased from 38.7% in 1984 to 51% in 2015. Shrub land decreased from 28 to 18% in the same period. Bare land increased due to high gully formation in the catchment. In 1984, it was 1.8% which turned to 0.6% in 1995 then increased in 2015 to 2.7%. Plantation forest was not detected in 1984. In 1995, it covers 1.5% which turned to be the same in 2015. The study clearly demonstrated that there are significant changes of land use and land cover in the catchment. The findings will allow making informed decision which will allow better land use management and environmental conservation interventions.

Effects of Land Use-Land Cover Thematic Resolution on Environmental Evaluations

Land use/land cover (LULC) maps are a key input in environmental evaluations for the sustainable planning and management of socio-ecological systems. While the impact of map spatial resolution on environmental assessments has been evaluated by several studies, the effect of thematic resolution (the level of detail of LU/LC typologies) is discordant and still poorly investigated. In this paper, four scenarios of thematic resolutions, corresponding to the four levels of the CORINE classification scheme, have been compared in a real case study of landscape connectivity assessment, a major aspect for the biodiversity conservation and ecosystem service provision. The PANDORA model has been employed to investigate the effects of LULC thematic resolution on Bio-Energy Landscape Connectivity (BELC) at the scale of the whole system, landscape units, and single land cover patches, also in terms of ecosystem services. The results show different types of impacts on landscape connectivity due to the changed spatial pattern of the LULC classes across the four thematic resolution scenarios. Moreover, the main priority areas for conservation objectives and future sustainable urban expansion have been identified. Finally, several indications are given for supporting practitioners and researchers faced with thematic resolution issues in environmental assessment and land use planning.

Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands

Information on the spatial extent of potential impact areas near disposal sites is vital to the development of a sustainable natural resource management policy. Eight Canadian landfills of various sizes and shapes in different climatic conditions are studied to quantify the spatial extent of their bio-thermal zone. Land surface temperature (LST) and normalized difference vegetation index (NDVI) are examined with respect to different Land Use Land Cover (LULC) classes. Within 1500 m of the sites, LST ranged from 18.3 °C to 29.5 °C and 21.3 °C–29.7 °C for forest land and agricultural land, respectively. Linear regression shows a decreasing LST trend in forest land for five out of seven landfills. A similar trend, however, is not observed for agricultural land. Both the magnitude and the variability of LST are higher in agricultural land. The size of the bio-thermal zone is sensitive to the respective LULC class. The approximate bio-thermal zones for forest class and agricultural classes are about 170 ± 90 m and 180 ± 90 m from the landfill perimeter, respectively. For the forest class, NDVI was negatively correlated with LST at six out of seven Canadian landfills, and stronger relationships are observed in the agricultural class. NDVI data has a considerably larger spread and is less consistent than LST. LST data appears more appropriate for identifying landfill bio-thermal zones. A subtle difference in LST is observed among six LULC classes, averaging from 23.9 °C to 27.4 °C. Geometric shape makes no observable difference in LST in this study; however, larger landfill footprint appears to have higher LST.

Delineating fragmented grassland patches in the tropical region using multi-seasonal synthetic aperture radar (SAR) and optical satellite images

ABSTRACT Globally, grasslands are declining and are in highly degraded conditions. In South Asia, grasslands are neglected and treated as wastelands. They remain unprotected, highly fragmented, and poorly understood, which has led to a loss of unique biodiversity and livelihoods. Mapping grasslands accurately is a challenge and current maps based on optical remote sensing often over- or underestimate grasslands in South Asia due to the prevalent complex landscape matrix, small patch sizes, and obscuring monsoonal clouds. Synthetic Aperture Radar (SAR), fused with moderate spatial resolution optical data, has been used to delineate grasslands, but the high-resolution, freely available European Space Agency (ESA)’s Sentinel-1 (SAR) and Sentinel-2 (Optical) provide an opportunity to map small and fragmented patches. Further, high-resolution imageries require high computing power, which is often limited to stand-alone machines. Here, we demonstrate that using cloud computing and optimal use of multi-seasonal imagery, one can obtain a highly accurate land-cover land use (LCLU) classification for a complex habitat matrix. We performed LCLU classification using the Support Vector Machine (SVM) algorithm of Sentinel-1, Sentinel-2, and Sentinel-1 and Sentinel-2 (Combined) in Google Earth Engine (GEE). GEE is a freely accessible cloud computing platform. We compared the accuracy of grassland classes between a) Sentinel-1 seasonal (pre-, during, and post-monsoon), b) Sentinel-2 post-monsoon, and c) combined Sentinel-1 and Sentinel-2. We tested this method at two sites in a highly fragmented habitat matrix in arid and semi-arid areas of Western and Southern India. The classification results have shown that the overall accuracy for the combined image classification was higher than only Sentinel-2 or Sentinel-1 alone for both sites. Grassland class accuracy was also consistent with combined image classification across the sites. Our results identified newer grassland areas that coarse land use management maps used by the government did not. Since the computation was conducted in GEE, a regular laptop was sufficient for the user and processing was completed rapidly. We, therefore, suggest that this approach of using cloud computing and optimal use of resource-hungry (computation and storage) high-resolution Sentinel-1 and Sentinel-2 data can be used to identify major LCLU classes and patchy grasslands in the arid and semi-arid regions of India and has the potential to map at a continent level.

Urban tree analysis using unmanned aerial vehicle (uav) images and object-based classification (case study: university of indonesia campus)

This paper aims to analyze the urban trees located in University of Indonesia campus using UAV image and Object-based Image Analysis (OBIA). Herein, DJI Phantom 4 Pro was flown at 90 meter height to take image above the study area with spatial resolution of 2.4 cm/pixel. The image from UAV then processed using Agisoft Photoscan and underwent geometric correction. The image containing red, green and blue (RGB) bands then segmented with multi-resolution algorithm. Four Vegetation Indices (VIs) namely Normalized Green-red Difference Index (NGRDI), Visible Atmospherically Resistant Index (VARI), Visible-band Difference Vegetation Index (VDVI) and Red-Green Ratio Index (RGRI) were used to develop rule sets for land use land cover (LULC) classification. Vegetation class was separated from LULC image to be further analysed with ArcGIS using information from ground truth observation. Final product is urban tree map containing tree names and LULC classes.

Using Satellite Image Fusion to Evaluate the Impact of Land Use Changes on Ecosystem Services and Their Economic Values

Accelerated land use change is a current challenge for environmental management worldwide. Given the urgent need to incorporate economic and ecological goals in landscape planning, cost-effective conservation strategies are required. In this study, we validated the benefit of fusing imagery from multiple sensors to assess the impact of landscape changes on ecosystem services (ES) and their economic values in the Long County, Shaanxi Province, China. We applied several landscape metrics to assess the local spatial configuration over 15 years (2004–2019) from fused imageries. Using Landsat-7 Enhanced Thematic Mapper Plus (ETM+), Landsat-8 Operational Land Imager (OLI) and Indian Remote Sensing Satellite System Linear Imaging Self Scanning Sensor 3 (IRS LISS 3) imageries fused for 2004, 2009, 2014 and 2019, we reclassified land use/land cover (LULC) changes, through the rotation forest (RF) machine-learning algorithm. We proposed an equivalent monetary metric for estimating the ES values, which also could be used in the whole China. Results showed that agriculture farmland and unused land decreased their spatial distribution over time, with an observed increase on woodland, grassland, water bodies and built-up area. Our findings suggested that the patterns of landscape uniformity and connectivity improved, while the distribution of landscape types stabilized, while the landscape diversity had a slight improvement. The overall ES values increased (4.34%) under a benefit transfer approach, mainly concerning woodland and grassland. A sensitivity analysis showed the selected economic value (EV) was relevant and suitable for the study area associated with our ES for LULC changes. We suggested that changes in landscape patterns affected the ESV trends, while the increases on some LULC classes slightly improved the landscape diversity. Using an interdisciplinary approach, we recommend that local authorities and environmental practitioners should balance the economic benefits and ecological gains in different landscapes to achieve a sustainable development from local to regional scales.

Mapping changes in artisanal and small-scale mining (ASM) landscape using machine and deep learning algorithms. - a proxy evaluation of the 2017 ban on ASM in Ghana

Artisanal and Small-Scale Mining (ASM) landscapes form integral part of the Land use land cover (LULC) in the developing worlds. However, the spatial, spectral, and temporal footprints of ASM present some challenges for using most of the freely available optical satellite sensors for change analysis. The challenge is even profound in tropical West African countries like Ghana where there is prolonged cloud cover. Whiles very few studies have used Sentinel-2 data to map change analysis in ASM landscape, none examined the contribution of individual S2 bands to the ASM classifications. Also, despite the capabilities of Machine Learning (ML) and Deep Learning (DL) models for LULC classifications, few studies have compared the performances of different classifiers in mapping ASM landscape. This study utilized Sentinel-2 data, four ML and DL models (Artificial Neural Network –ANN, Random Forest – RF, Support Vector Machines –SVM, a pixel-based Convolutional Neural Network-CNN) and image segmentation to examine the performance of S2 bands and ML and DL algorithms for change analysis in ASM landscape, with the Birim Basin in Ghana as a study area. The result of the change analysis was used to assess changes in LULC during the recent ban on the expansion of ASM in the country. It was found out that ANN is a better classifier of ASM achieving the highest overall accuracy (OA) of 99.80% on the segmented Sentinel-2 bands. The study also found out that the Band 5 Vegetation Red Edge (VRE) 1 contributed most to classifying ASM, with the segmented VRE 1 being superlative over the other predictors. In terms of expansion, ASM increased by 59.17 km2 within the period of the study (January 2017 to December 2018), suggesting that ASM still took place under the watch of the ban. The classification results showed that most of the peripheral of forest and farmland have been converted to ASM with little disturbance within the interior of the forest reserves. The study revealed that, the ban was yielding very little or no results due to a number of policy deficiencies including low staff strength, lack of logistics and low remuneration. Enforcement of legal instruments against ASM and farming activities within the forest reserves, improvement in the monitoring systems and intensification of public education on the value of forest and the need to protect it are some of the major recommendations that could control encroachment on the forest reserves.

Assessing the risk of land use change in the centre of the Ba river basin, Vietnam

Land use/land cover (LULC) changes may lead to vulnerable land resource and imbalanced ecosystem. This paper aimed to identify the change of LULC in the center of the Ba river basin. As well, the risk of land use change (LUC) was assessed based on remote sensing data and Analytic Hierarchy Process to determine the areas where LULC can modify with varying degrees. The LULC and Normalized Difference Vegetation Index (NDVI) data processed from Landsat images on Google Earth Engine platform were overlayed to create a zoning map of LUC risk levels in dry season of 2010 and 2020. Over 10 years, the largest increased area was bare land with a growth of 4.98%; whereas the most reduced area was shrub with a decrease of 4.21%. Forest area accounted for the highest proportion of the whole area. LULC classes and NDVI values were assigned different risk weightages to produce the risk distribution map of LUC levels and susceptible areas. The results revealed the most risk zones (High and Very high) constituted more than 50% and stayed steadily from 2010 to 2020, in which shrub areas were partially transformed to bare land or agricultural land due to economic development.

GEOMORPHOLOGY AND LAND USE/LAND COVER CHANGE DETECTION FOR EASTERN PART OF DESERT BACK OF MENOUFIA GOVERNORATE

The current investigation aims to identifying the geomorphological characteristics of the area east of desert part of Menoufia governorate west of Rashid Nile branch. Change detection of land use/land cover (LULC) classes between the years 2001 and 2020 are also carried out. The integration of Remote Sensing (RS) and Geographic Information System (GIS) techniques were used to perform this work. The studied area is located between latitudes 30° 5' to 30° 32' N and longitudes 30° 30' to 31° 00' E and covers about 1354 km2 or 322474 feddans. The interpretation of satellite image of the study area indicated that, there are three main identified geomorphic units including seven subunits in this area. These units are 1) Depressions: cover an area of 271 km2 or 64530 feds (about 20.0% of the total studied area(, 2) Sand Sheets: have 1022 km2 or 243354 feds (75.3 %), including Low- (40.0 %), Moderate- (24.8 %) and High-Terraces (10.5 %), and 3) Old Deltaic Revere Terraces: have 143 km2 or 34005 feds (10.5 %), including Low- (2.4 %), Moderate- (1.4 %) and High-Terraces (0.9 %). Binary encoding method was carried out based on classifying the Satellite images using decision tree classifier and statistical relative change detection to identify the relative change detection in (LULC) classes of agricultural land and barren area between 2001 and 2020 from the different Landsat images. The overall changes in LULC classes during this period indicated that, there is an increase in the cultivated area with 12.6% from the total studied area (170.2 km2 or 40535.6 fed) and shrunk in the baren land with 13.4% (181.2 km2 or 43155.4 fed).

Spatial and transient modelling of land use/land cover (LULC) dynamics in a Sahelian landscape under semi-arid climate in northern Burkina Faso

Sahelian landscapes have gone through rapid changes over the past decades under high pressure from an ever-growing population, climate hazards and land degradation. Understanding drivers of change and forecasting LULC dynamics in time and space are critical for sustainable land management. This research aimed at analyzing LULC patterns and forecasting future changes in the landscape at the watershed level in semi-arid climate in northern Burkina Faso. Three LULC categories (natural vegetation, degradation soils, and cultivated areas) were considered. Seasonal vegetation dynamics were assessed through the monitoring of radiometric indices over the years 2015, 2016 and 2017. The post-harvest period (October-November) was found to be optimal for single-date LULC mapping. Furthermore, LULC maps for 1986, 1999, 2009 and 2017 were produced through a supervised classification of Landsat satellite images. For land change modelling and prediction, a Multilayer Perceptron (MLP) neural network was calibrated between 1999 and 2009 and validated against 2017 reference map, then used to simulate prediction maps for 2030 and 2050, in conjunction with population growth forecasts. Analysis of LULC changes revealed degradation soils were the major contributor to cultivated areas expansion between 1986 and 2017. Land use maps predicted for 2030 and 2050 indicated an expected increase in cultivated areas of 10.4 % and 22.7 % respectively. These results highlight the strength of the human-environment relationship in the watershed and call for careful planning and conservation actions for a sustainable balance between sustainable agricultural production and natural resources conservation.

Mapping Land use/land cover using a combination of Radar Sentinel-1A and Sentinel-2A optical images

The current study used a combination of Sentinel-1A (SE-1A) radar and Sentinel-2A (SE-2A) optical images in mapping land use/land cover (LULC) in Dak Nong province in 2018. The Random Forest (RF) algorithm was adopted to digitally categorize Landsat images into LULC maps according to ten different LULC classes included: evergreen forest, semi-evergreen forest, deciduous forest, plantation, rubber, industrial plants, crop land, residential area, water surface and others. The results indicated an overall accuracy (OA) and kappa coefficient of 81.40%, Kappa = 0.79, respectively. Based on the results of classified image, a 2018 LULC map of the study area was simulated. Accordingly, the natural forests account for 34.27% of the total area of the province, distributed scattered in districts. In which, the evergreen forest occupies the highest area with more than 166.600 ha, equivalent to 74.54% of the total natural forest area, and concentrated in the high mountain areas. Non-forest covers occupy more than 63% area of Dak Nong province. The industrial and agricultural cropland indicated a high area in the study area with a rate of 34.82% and 11.38%, respectively. This shows a strong development in the scale of industrial and agricultural crops in the study area. The objective information on current land use/land cover in this study can serve as the basis for policymakers to orient the local forest resource sustainability strategies. Besides, the study also shows that the use of a combination of Sentinel-1A radar and Sentinel-2A optical image to classify and construct the LU/LC map is a high-efficiency research direction.

Potential Dam Breach Analysis and Flood Wave Risk Assessment Using HEC-RAS and Remote Sensing Data: A Multicriteria Approach

Dam breach has disastrous consequences for the economy and human lives. Floods are one of the most damaging natural phenomena, and some of the most catastrophic flash floods are related to dam collapses. The goal of the present study is to analyse the impact of a possible failure–collapse on a potentially affected area downstream of the existing Bramianos dam on southern Crete Island. HEC-RAS hydraulic analysis software was used to study the dam breach, the flood wave propagation, and estimate the extent of floods. The analysis was performed using two different relief datasets of the same area: a digital elevation model (DEM) taken from very high-resolution orthophoto images (OPH) of the National Cadastre and Mapping Agency SA and a detailed digital surface model (DSM) extracted from aerial images taken by an unmanned aerial vehicle (UAV). Remote sensing data of the Sentinel-2 satellite and OPH were utilised to create the geographic information system (GIS) layers of a thorough land use/cover classification (LULC) for the potentially flooded area, which was used to assess the impact of the flood wave. Different dam breach and flood scenarios, where the water flows over man-made structures, settlements, and olive tree cultivations, were also examined. The study area is dominated mainly by three geological formations with different hydrogeological characteristics that dictated the positioning and structure of the dam and determine the processes that shape the geomorphology and surface roughness of the floodplain, affecting flow conditions. The results show that the impact of a potential dam break at Bramianos dam is serious, and appropriate management measures should be taken to reduce the risk. The water flow downstream of the collapsed dam depends on the water volume stored in the reservoir. Moreover, the comparison of DSM and DEM cases shows that the detailed DSM may indicate more accurately the surface relief and existing natural obstacles such as vegetation, buildings, and greenhouses, enabling more realistic hydraulic simulation results. Dam breach flood simulations and innovative remote sensing data can provide valuable outcomes for engineers and stakeholders for decision-making and planning in order to confront the consequences of similar incidents worldwide.

Accuracy Improvements to Pixel-Based and Object-Based LULC Classification with Auxiliary Datasets from Google Earth Engine

The monitoring and assessment of land use/land cover (LULC) change over large areas are significantly important in numerous research areas, such as natural resource protection, sustainable development, and climate change. However, accurately extracting LULC only using the spectral features of satellite images is difficult owing to landscape heterogeneities over large areas. To improve the accuracy of LULC classification, numerous studies have introduced other auxiliary features to the classification model. The Google Earth Engine (GEE) not only provides powerful computing capabilities, but also provides a large amount of remote sensing data and various auxiliary datasets. However, the different effects of various auxiliary datasets in the GEE on the improvement of the LULC classification accuracy need to be elucidated along with methods that can optimize combinations of auxiliary datasets for pixel- and object-based classification. Herein, we comprehensively analyze the performance of different auxiliary features in improving the accuracy of pixel- and object-based LULC classification models with medium resolution. We select the Yangtze River Delta in China as the study area and Landsat-8 OLI data as the main dataset. Six types of features, including spectral features, remote sensing multi-indices, topographic features, soil features, distance to the water source, and phenological features, are derived from auxiliary open-source datasets in GEE. We then examine the effect of auxiliary datasets on the improvement of the accuracy of seven pixels-based and seven object-based random forest classification models. The results show that regardless of the types of auxiliary features, the overall accuracy of the classification can be improved. The results further show that the object-based classification achieves higher overall accuracy compared to that obtained by the pixel-based classification. The best overall accuracy from the pixel-based (object-based) classification model is 94.20% (96.01%). The topographic features play the most important role in improving the overall accuracy of classification in the pixel- and object-based models comprising all features. Although a higher accuracy is achieved when the object-based method is used with only spectral data, small objects on the ground cannot be monitored. However, combined with many types of auxiliary features, the object-based method can identify small objects while also achieving greater accuracy. Thus, when applying object-based classification models to mid-resolution remote sensing images, different types of auxiliary features are required. Our research results improve the accuracy of LULC classification in the Yangtze River Delta and further provide a benchmark for other regions with large landscape heterogeneity.

Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning.

Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.

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

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

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.