Land Use And Land Cover Change Detection Research Articles

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

Eucalyptus tree expansion and land use and land cover dynamics in Ethiopia- empirical evidence from Gurage Zone, Ethiopia

Gurage Zone is one of the areas of Ethiopia experiencing rapid land use and land cover (LULC) changes over the past few decades. This study assessed the contribution of eucalyptus expansion to LULC changes based on a detailed household level survey, key informant interviews, focus group discussions and a set of Landsat imageries. Both descriptive statistics and econometric model were employed for the analysis of drivers of eucalyptus plantation. LULC change detection technique was used to examine the changes in LULC over time. This study achieved a mean classification accuracy of 89.17% and 0.83 overall Kappa statistics for the tree LULC maps. The result of descriptive analysis revealed that more than 42% of the sampled households have planted eucalyptus trees on crop land. The study indicated that eucalyptus plantation which was fourth in LULC ranking in 2000 has increased by 68.3% in 2021 to become the second largest land cover next to crop land. On the other hand, from 2010 to 2021, the area covered by crops showed a decline. This indicates eucalyptus tree to be the major cause of LULC change in the study area. The econometric estimation identified the micro-level drivers such as land size and access to market to be important factors influencing eucalyptus plantation. This study suggests that the rapid expansion of eucalyptus plantation on fertile soil requires an immediate policy response in order to balance food crop production and eucalyptus plantation in Gurage Zone.

Novel approach for the LULC change detection using GIS & Google Earth Engine through spatiotemporal analysis to evaluate the urbanization growth of Ahmedabad city

Changes in Land Use and Land Cover (LULC) have a significant impact on both urban and environmental planning, especially in the context of rapidly urbanising areas. The largest city in the state of Gujarat, Ahmedabad, has experienced substantial growth. Utilising the powerful tools of Geographic Information System (GIS) and Google Earth Engine (GEE), this study uses a thorough methodology to track and examine changes in LULC over the last ten years. Modifications in LULC have a significant impact on both urban and environmental planning, especially in light of the fast urbanisation cycle. This study uses a comprehensive approach to monitor and analyze Landsat imagery changes in LULC over the past ten years, leveraging the powerful capabilities of Google Earth Engine and GIS. Important insights into LULC patterns are revealed by analysing Landsat imagery from Ahmedabad from 2013 to 2023. Pre-processing with GIS and GEE, accuracy evaluation, and classification of satellite images are all included in the methodology. With a notable 0.85 Kappa and 89 % accuracy in LULC classification, the resulting LULC classifications cover settlements, vegetation, water bodies, arid land, and other relevant features. Results show that over the previous ten years, Ahmedabad has seen a noteworthy decrease in agricultural lands of 13.74 % and a reduction in barren areas of 4.78 %. Meanwhile, the total LULC patterns have shown a significant expansion of 23.56 %. This study is significant because it highlights how urbanisation reduces vegetation cover. The results will helpful for urban planning strategies that take changing environmental dynamics into account.

Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling

This research uses a Classification and Regression Tree (CART) model with Google Earth Engine (GEE) to assess the winter season’s land cover and change detection mapping impact on the evapotranspiration (crop water requirement) parameters. Winter seasons, crucial for agricultural planning, and irrigation water requirement challenges in accurately mapping land cover and detecting changes due to the dynamic nature of farming practices during this period. In this study, Landsat-8 OLI images have been combined to map Land use and Land cover (LULC) and other change detection mapping in Akola Block, Maharashtra, India, during the 2018–2022 winter season. As an discoverer researcher that found detailed information of LULC classes during last 2018 to 2022 winter seasons, the use of the CART model in combination with a cloud-computing GEE demonstrates to be a practical approach for accurate land cover classification and change detection maps to create a pixel-based winter seasons information of study area. The novelty of this study lies in its innovative use of GEE, a powerful platform for remote sensing and geospatial analysis, to create LULC maps with remarkable accuracy. Achieving a 100% training accuracy across the four years under consideration is an exceptional feat, highlighting the reliability and stability of the methodology. Furthermore, the validation accuracy values, ranging from 89 to 94% for the winter seasons of 2018 to 2022, underscore the robustness of this approach. Such consistently high accuracy in mapping LULC over time is a groundbreaking achievement and offers a new dimension to the field of hydrology. For the hydrological community, the implications of this study are profound. Accurate LULC mapping and change detection provide critical data for modeling and analyzing the effects of land use changes on water resources, watershed management, and water quality. The User, Kappa, and Producer accuracy metrics used in this research highlight the model’s performance and its suitability for hydrological applications. These accurate LULC maps can aid in the development of hydrological models, forecasting, and decision-making processes, ultimately contributing to more effective water resource management and environmental conservation. In summary, this study’s innovative use of GEE, its remarkable accuracy in LULC mapping, and its relevance to the hydrological community demonstrate the potential for advanced remote sensing and geospatial tools to significantly improve our understanding of land use changes and their implications for water resources and environmental management.

Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies.

Understanding the linkages between spatio-temporal urban land system changes and land surface temperature in Srinagar City, India, using image archives from Google Earth Engine.

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city.

Effect of Urbanization on Land Surface Temperature: A Case Study of Kathmandu Valley


 
 
 Growth in the population over time is bound to increase the expansion of densely populated areas which will change the dynamics of land use and land cover (LULC) directly or indirectly. The objective of this study is to analyze the effect of urban growth on the land surface temperature (LST) in Kathmandu. We used the maximum likelihood classification method for LULC classification and change detection of pre-monsoon satellite data for the years 2000, 2005, 2010, 2015 and 2020. LST was then calculated using the thermal infrared band from the satellite imagery. Finally, the relationship between urban growth and LST was assessed by correlation and regression analysis. The result shows an increase in urban areas by 13.98% of the total area between 2000 and 2020. The analysis of thermal patterns shows that there is a gradual increase in temperature in the urban area. With an overall accuracy of 89.96% and a Kappa coefficient of 0.86, the study shows high agreement between classified data from images. This study shows that urbanization has a positive impact on surface temperature, yet the relationship is not significant. Sustainable city planning and strict adherence to the Land Use Act and regulations will assist to make the urbanization process more sustainable in the future.
 
 

Utilizing multi-temporal thermal data to assess environmental land degradation impacts: example from Suez Canal region, Egypt

Land surface temperature (LST) analysis of satellite data is critical for studying the environmental land degradation impacts. However, challenges arise to correlate the LST and field data due to the constant development in land use and land cover (LULC). This study aims to monitor, analyze, assess, and map the environmental land degradation impacts utilizing image processing and GIS tools of satellite data and fieldwork. Two thermal and optical sets of Landsat TM + 5 and TIRS + 8 data dated 1984 and 2018 were used to map the thermal and LULC changes in the Suez Canal region (SCR). The LULC classification was categorized into water bodies, urban areas, vegetation, baren areas, wetland, clay, and salt. LULC and LST change detection results revealed that vegetation and urban areas increased in their areas in 34 years. Moreover, 97% of the SCR witnessed LST rise during this period with an average rise rate of 0.352 °C per year. The most effective LULC class changes on LST were the conversions from or to baren areas, where baren areas were converted to 630.5 km2 vegetation and 104 km2 urban areas rising the LST to 43.57 °C and 45 °C, respectively. The spectral reflectance (LSR), LST profiles, and statistical analyses examined the association between LST and LULC deriving factors. In combination with field observations, five hotspots were chosen to detect and monitor natural and human land degradation impacts on LST of the SCR environment. Land degradations detected include water pollution, groundwater rising, salinity increase, sand dune migration, and seismic activity.

Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020

Land surface of the Earth has been changing as a result of human induced activities and natural processes. Accurate representation of landscape characteristics and precise determination of spatio-temporal changes provide valuable inputs for environmental models, landscape and urban planning, and historical land cover change analysis. This study aims to determine historical land use and land cover (LULC) changes using multi-modal geospatial data, which are the cadastral maps produced in 1858, monochrome aerial photographs obtained in 1955, and multi-spectral WorldView-3 satellite images of 2020. We investigated two pilot regions, Aksu and Kestel towns in Bursa/Turkey, to analyze the long-term LULC changes quantitatively and to understand the driving forces that caused the changes. We propose methods to facilitate the preparation of historical datasets for the LULC change detection and present an object-oriented joint classification scheme for multi-source datasets to accurately map the spatio-temporal changes. Our approach minimized the amount of manual digitizing required for the boundary delineation of LULC classes from historical geospatial data. Also, our quantitative analysis of LULC maps indicates diverging developments for the selected locations in the long period of 162 years. We observed rural depopulation and gradual afforestation in Aksu; whereas, agricultural land abandonment and deforestation in Kestel.

Land use and land cover change analysis using satellite images in Gua Musang, Kelantan

Abstract The use of multisensor remote sensing to characterise forest structure has significant promise for mapping and understanding forest biological processes, making remote sensing an effective technology in detecting deforestation for forest management and monitoring. The aim of this study is to detect land use and land cover (LULC) changes due to deforestation at Gua Musang, Kelantan using satellite images. The Landsat 8 OLI satellite images were obtained from United State Geological Survey (USGS) for year 2017 to 2019. The supervised classification with maximum likelihood algorithm was applied for image classification to generate land use land cover categories. Then, LULC change detection between 2017, 2018 and 2019 were conducted to analyse the change and pattern. Based on the results, it shows the percentage of each land use changes within three (3) years from 2017 to 2019 for Landsat 8 OLI. Percentage of deforestation due to logging areas for year 2017 to 2019 is 5.95%. Percentage of forest area is 5.57%. For water bodies, the percentages just only cover 0.36% in 3 years. The highest land use change is no changes which is 88.12% happened towards the land from 2017 and 2019. This shows within three years there is no great changes at the area. The overall accuracy for each map is more than 80% which are acceptable. From the analysis, there were no significant changes of LULC during this period due to the increased of timber extraction, agricultural land expansion, urban growth, and poor governance structures.

Application of multi layer perceptron neural network Markov Chain model for LULC change detection in the Sub-Himalayan North Bengal

Land use and land cover (LULC) changes may occur due to natural and anthropogenic activities. The present study area is situated in the Himalayan foothill region of north-eastern India and covers around 1507 km2. This area comprises Siliguri Municipal Corporation (SMC) and the surrounding five community development blocks. Geopolitical location of the study area makes it significant as it is exposed to the negative impacts of human and commercial activities across the region. The present study aims to identify the LULC classes in the study area from 1991 to 2021 with their change dynamics and predict the LULC changes in 2050. Apart from these, a perception study is also carried out to identify the major drivers of LULC changes in the study area. After delineating the study area, supervised classification of Landsat satellite images was done applying the maximum likelihood classification (MLC) tool for the year 1991, 2001, 2011 and 2021. Each image was classified into six LULC classes i.e., vegetation, plantation, agricultural land, built up, water bodies and fallow land and the overall accuracy (average) of the classified maps is 88.83%. The result shows that vegetation, agricultural land and fallow land are decreased from 23% to 20%, 35%–28% and 23%–19% respectively and plantation and built up areas are significantly increased from 14% to 24% and 2%–8% respectively. Then the land change modeler (LCM) under multi layer perceptron neural network Markov Chain (MLPNN-MC) model was applied to predict the spatio-temporal LULC changes from 2021 to 2050. The result reveals that in 2050 built up areas will be increased significantly and are expected to cover almost 12% of the total study area and only 24% of agricultural land will be left. The model was validated by Pearson's chi-squared test and found no significant distinguishes between simulated and actual classified maps. For identifying the main drivers of the LULC changes in the study area people's perceptions were taken from 25 focus group discussions (FGD) randomly over the area. The result reveals that physical and economic location, immigration, development of transport network, increasing multi-functional land use, expansion of small trade and enterprises, and cutting of trees for fuel wood and construction purposes are the key drivers of LULC change for this particular region. The change in the land surface in this Sub-Himalayan region is threatened by unplanned urbanization and unorganized land use practices so, crucial measures are needed indeed.

Land use and land cover change and sustainability assessment of Vijayawada city by RS&GIS

Land utilization is a key factor in the allocation and function of ecosystems. The limited available land is insufficient to satisfy the growing demand. Sustainable future depends on Human decisions on utilization of land. The landuse analysis between two time periods and their impact especially on sustainable development may leads to proper planning at different territorial levels. The present study examines spatiotemporal trends in modification of LULC (land use and land cover) changes in the Vijayawada area. Landsat 5 and 8 satellite imagery was used to produce change deduction maps over a sixteen year of time period (2005 and 2020). For the year 2005, 30 meter resolution Landsat 5 satellite image are used and for the year 2020 15 meter resolution Landsat 8 images are used to identify the changes in spatiotemporal trends. Maximum lilelihood supervised classification techniques are used to estimate land use and land cover change. The dynamics of LULC modification for 16 year time period and its impact were discussed as key findings in this paper. This study reports that, by using geo-spatial techniques could reduce the main causes of LULC change detection that threaten ecosystems and biodiversity through timely and adequate monitoring.

Socio-ecological change in the Ruvu Estuary in Tanzania, inferred from land-use and land-cover (LULC) analysis and estuarine fisheries

Ecosystem goods and services derived from estuaries have sustained coastal livelihoods in the Western Indian Ocean (WIO) region throughout recorded history. Estuaries provide fertile and seasonally irrigated space for planting crops, mangrove products for construction and fuel, and fish as a protein source. Human population growth and an escalating demand for natural resources threaten estuarine critical habitats and their functioning, exacerbated by the effects of climate change. Decadal and seasonal land-use and land-cover (LULC) changes in the Ruvu Estuary in Tanzania were investigated through analysis of Landsat 5/8 and Sentinel-2 satellite images. The estuary is river-dominated and truncated near the coast during high river flow, with tidal influence extending approximately 12 km upstream during low river flow. LULC change detection targeting nine classes (water, developed, barren, forest, grasslands, cultivated, mangroves, wetlands and mudflats) showed that estuary-associated wetlands and mangroves had declined significantly over the past two decades (1995-2016) making way for developed land (growth of Bagamoyo Town), cultivated land (agricultural expansion with increasing population) and grasslands (coastal habitat changes). Seasonal LULC changes were conversion of wetlands to cultivated land after the wet season, and transformation of fallow wetlands to grasslands. The estuarine fishery relied on a small number of mainly freshwater and marine migrant species, compared to a highly diverse mix of mainly marine species in the nearby coastal fishery. The sparsity of quantitative fisheries data, spectral confusion when modelling land-cover change, and absence of household survey data to assess livelihood activities remain major information gaps. Generalized recommendations for improving socio-ecological change studies in WIO estuarine systems are provided.

Land Use and Land Cover Change Assessment in the Context of Flood Hazard in Lagos State, Nigeria

Incessant flooding is a major hazard in Lagos State, Nigeria, occurring concurrently with increased urbanization and urban expansion rate. Consequently, there is a need for an assessment of Land Use and Land Cover (LULC) changes over time in the context of flood hazard mapping to evaluate the possible causes of flood increment in the State. Four major land cover types (water, wetland, vegetation, and developed) were mapped and analyzed over 35 years in the study area. We introduced a map-matrix-based, post-classification LULC change detection method to estimate multi-year land cover changes between 1986 and 2000, 2000 and 2016, 2016 and 2020, and 1986 and 2020. Seven criteria were identified as potential causative factors responsible for the increasing flood hazards in the study area. Their weights were estimated using a combined (hybrid) Analytical Hierarchy Process (AHP) and Shannon Entropy weighting method. The resulting flood hazard categories were very high, high, moderate, low, and very low hazard levels. Analysis of the LULC change in the context of flood hazard suggests that most changes in LULC result in the conversion of wetland areas into developed areas and unplanned development in very high to moderate flood hazard zones. There was a 69% decrease in wetland and 94% increase in the developed area during the 35 years. While wetland was a primary land cover type in 1986, it became the least land cover type in 2020. These LULC changes could be responsible for the rise in flooding in the State.

Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India

Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the lives and economy of the communities residing in that mountainous topography. This study aims to investigate whether the landscape change has any impact on landslide occurrences in the Kalsi-Chakrata road corridor by detailed investigation through correlation of the landslide susceptibility zones and the landscape change, and finally to demarcate the hotspot villages where influence of landscape on landslide occurrence may be more in future. The rational of this work is to delineate the areas with higher landslide susceptibility using the ensemble model of GIS-based multi-criteria decision making through fuzzy landslide numerical risk factor model along the Kalsi-Chakrata road corridor of Uttarakhand where no previous detailed investigation was carried out applying any contemporary statistical techniques. The approach includes the correlation of the landslide conditioning factors in the study area with the changes in land use and land cover (LULC) over the past decade to understand whether frequent landslides have any link with the physical and hydro-meteorological or, infrastructure, and socioeconomic activities. It was performed through LULC change detection and landslide susceptibility mapping (LSM), and spatial overlay analysis to establish statistical correlation between the said parameters. The LULC change detection was performed using the object-oriented classification of satellite images acquired in 2010 and 2019. The inventory of the past landslides was formed by visual interpretation of high-resolution satellite images supported by an intensive field survey of each landslide area. To assess the landslide susceptibility zones for 2010 and 2019 scenarios, the geo-environmental or conditioning factors such as slope, rainfall, lithology, normalized differential vegetation index (NDVI), proximity to road and land use and land cover (LULC) were considered, and the fuzzy LNRF technique was applied. The results indicated that the LULC in the study area was primarily transformed from forest cover and sparse vegetation to open areas and arable land, which is increased by 6.7% in a decade. The increase in built-up areas and agricultural land by 2.3% indicates increasing human interference that is continuously transforming the natural landscape. The landslide susceptibility map of 2019 shows that about 25% of the total area falls under high and very high susceptibility classes. The result shows that 80% of the high landslide susceptible class is contained by LULC classes of open areas, scrubland, and sparse vegetation, which point out the profound impact of landscape change that aggravate landslide occurrence in that area. The result acclaims that specific LULC classes, such as open areas, barren-rocky lands, are more prone to landslides in this Lesser Himalayan road corridor, and the LULC-LSM correlation can be instrumental for landslide probability assessment concerning the changing landscape. The fuzzy LNRF model applied has 89.6% prediction accuracy at 95% confidence level which is highly satisfactory. The present study of the connection of LULC change with the landslide probability and identification of the most fragile landscape at the village level has been instrumental in delineation of landslide susceptible areas, and such studies may help the decision-makers adopt appropriate mitigation measures in those villages where the landscape changes have mainly resulted in increased landslide occurrences and formulate strategic plans to promote ecologically sustainable development of the mountainous communities in India's Lesser Himalayas.

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

Land–Use and Land-Cover Change Detection Using Dynamic Time Warping–Based Time Series Clustering Method

Accurate and timely monitoring of urban land-use and land-cover (LULC) change is useful for understanding the various impacts of human activity on the urban environment. In order to demonstrate the advantage of time series imaging for urban LULC change detection, we selected time series Landsat images over a two-year period to detect inter-annual changes. A time series trajectory for each pixel was developed by the biophysical composition index (BCI) and normalized vegetation index (NDVI) values extracted from Landsat images. Considering that temporal length of trajectories in different years might be different, the dynamic time warping (DTW) measure was selected as the LULC change magnitude indicator. After DTW-based change/unchanged detection, the DTW-based clustering method was used in LULC change type extraction. Finally, the overall accuracy of change/unchanged detection result was 92.3%, and the overall accuracy of all change types was 71%. Some change types that are difficult to extract by bi-temporal images were detected, such as inter-class changes between farmland and forest, and intra-class change of farmland, indicating the advantage of time series information in LULC change detection.

Spatiotemporal variability in spate irrigation systems in Khirthar National Range, Sindh, Pakistan (case study)

Satellite remote sensing and geographical information system (GIS) have been used successfully to monitor and assess the land use and land cover (LULC) dynamics and their impacts on people and the environment. LULC change detection is essential for studying spatiotemporal conditions and for proposing better future planning and development options. The current research analyzes the detection of spatiotemporal variability of spate irrigation systems using remote sensing and GIS in the Khirthar National Range, Sindh Province of Pakistan. We use Landsat images to study the dynamics of LULC using ArcGIS software and categorize five major LULC types. We obtain secondary data related to precipitation and crop yield from the provincial department of revenue. The maximum likelihood supervised classification (MLSC) procedure, augmented with secondary data, reveals a significant increase of 86.25% in settlements, 83.85% in spate irrigation systems, and 65% in vegetation, and a substantial negative trend of 39.50% in water bodies and 20% in barren land during the period from 2013 to 2018. Our study highlights an increase in settlements due to the inflow of local population for better means of living and an increase in spate irrigation systems, which indicates the water conservation practices for land cultivation and human purpose lead to the shrinkage of water bodies. The confusion matrix using Google Earth data to rectify modeled (classified) data, which showed an overall accuracy of 82.8%–92%, and the Kappa coefficient estimated at 0.80–0.90 shows the satisfactory results of the LULC classification. The study suggests the need to increase water storage potential with the appropriate water conservation techniques to enhance the spate irrigation system in the hilly tracts for sustainable developments, which mitigates drought impact and reduces migration rate by providing more opportunities through agricultural activities in the study area.

Population growth, land use and land cover transformations, and water quality nexus in the Upper Ganga River basin

Abstract. The Upper Ganga River basin is socioeconomically the most important river basin in India and is highly stressed in terms of water resources due to uncontrolled land use and land cover (LULC) activities. This study presents a comprehensive set of analyses to evaluate the population growth, LULC transformations, and water quality nexus for sustainable development in this river basin. The study was conducted at two spatial scales: basin scale and district scale. First, population data were analyzed statistically to study demographic changes, followed by LULC change detection over the period of February–March 2001 to 2012 (Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data) using remote sensing and geographical information system (GIS) techniques. Trends and spatiotemporal variations in monthly water quality parameters, viz. biological oxygen demand (BOD), dissolved oxygen (DO, measured in percentage), fluoride (F), hardness (CaCO3), pH, total coliform bacteria and turbidity, were studied using the Mann–Kendall rank test and an overall index of pollution (OIP) developed specifically for this region, respectively. A relationship was deciphered between LULC classes and OIP using multivariate techniques, viz. Pearson's correlation and multiple linear regression. From the results, it was observed that population has increased in the river basin. Therefore, significant and characteristic LULC changes were observed. The river became polluted in both rural and urban areas. In rural areas, pollution is due to agricultural practices, mainly fertilizers, whereas in urban areas it is mainly contributed from domestic and industrial wastes. Water quality degradation has occurred in the river basin, and consequently the health status of the river has also changed from acceptable to slightly polluted in urban areas. Multiple linear regression models developed for the Upper Ganga River basin could successfully predict status of the water quality, i.e., OIP, using LULC classes.

An inverse method for watershed change detection using hybrid conceptual and artificial intelligence approaches

Land Use and Land Cover (LULC) changes intrinsically lead to various hydrological impacts especially on the outflow rate of the watersheds. This research investigated LULC changes and its effect on outlet runoff by detecting LULC changes location and severity via an inverse method for the Little River Watershed, USA. For this purpose, the Clark’s conceptual Rainfall-Runoff model was applied to the delineated sub-watersheds of the basin to generate different outflows by altering the Storage Coefficient (SC) of the model for each sub-watershed as the representative of LULC. Then, the relation between SC values and outflow time series was simulated by Artificial Intelligence (AI) based models of artificial neural network and least square support vector machine. In this way, in order to ignore redundant information and reduce the dimension of input vector, Wavelet-Entropy (WE) values of the outflow sub-series were computed and used as the inputs of the AI model to compute SCs of the sub-watersheds as the outputs. The trained multi-output AI model as an inverse method could be then used to predict the SC values (as representative of LULC) of the sub-watersheds using the observed time series of runoff at the outlet. The obtained results showed that the proposed inverse method could reliably detect not only the location but also the severity of LULC changes by prediction of SC values in the coming future years. For validation of the method, a comparison was also performed between the obtained results and recorded changes via normalized difference vegetation index (NDVI) and land use classification, extracted from Landsat images. The comparison approved the ability of the proposed method for LULC change detection in a way that deforestation and cropland increasing of the sub-watersheds from 1990 to 2013 were aligned with the SC reduction e.g., 26% decrease of SC for downstream sub-watershed versus 53% decrease and 21% increase of forest and crop lands, respectively.