Monitoring land use changes associated with urbanization: An object based image analysis approach

It is critically important to quantify the impact of land use land cover (LULC) changes on hydrology, and to understand the mechanism by which LULC changes affect the hydrological process in a river basin. To accurately simulate the hydrological process for a watershed like the Wei River Basin, where the surface characteristics are highly modified by human activities, we present an alternative approach of time-varying parameters in a hydrological model to reflect the changes in underlying land surfaces. The spatiotemporal impacts of LULC changes on watershed streamflow are quantified, and the mechanism that connects the changes in runoff generation and streamflow with LULC is explored. Results indicate the following: (1) time-varying parameters’ calibration is effective to ensure model validity when dealing with significant changes in underlying land surfaces; (2) LULC changes have significant impacts on the watershed streamflow, especially on the streamflow during the dry season; (3) the expansion of cropland is the major contributor to the reduction of surface water, causing decline in annual and dry seasonal streamflow. However, the shrinkage of woodland is the main driving force that decreases the soil water, thus contributing to a small increase in streamflow during the dry season.

The escalating demand for anthropic needs and urban development has highlighted the importance of monitoring and change detection of land use land cover (LULC). With an increase in agricultural expansion and infrastructural development, every land surface on earth calls for a long-term investigation of land modification patterns and their underlying contributory factors. The present investigation monitors the LULC changes and assesses the process controls in Kohima and Dimapur districts of Nagaland, India. Currently, these two districts encompassing the main urban cities of the hilly state are experiencing rapid urbanization and unplanned developmental activities. Alike any other LULC changes observed in unplanned and developing cities, these districts are likely to face environmental degradation, and particularly, the occurrence of frequent landslides and flash floods. The study has three objectives-(i) LULC mapping of Kohima and Dimapur districts for three periods (1998, 2008, and 2018), (ii) comparative analysis of LULC change patterns in the two districts during the three epochs (1998-2008, 2008-2018, and 1998-2018), and (iii) assessment of the contributory factors. For the study, remotely sensed LANDSAT images (TM and OLI) in Geographical Information System (GIS) platform were utilized along with field surveys. Supervised classification technique was employed and four major LULC classes were identified using Landsat level-1 classification system. The overall accuracy of the classification varied between 91 and 98%. Results showed that Built Up areas have increased significantly in both the districts at the rate of 322.6ha/year in Kohima and 301.9ha/year in Dimapur during 1998-2018. On the other hand, Agricultural Land and Forest Land declined in both districts. Changes in LULC were mainly due to marginalization of shifting cultivation, deforestation, infrastructural development, urban migration, and flourishing of aquaculture farming. This study furnishes baseline information on LULC in the data-scarce region of Northeast India and is an insinuation to the policy-makers to ensure sustainable land use planning in the face of rapid urbanization.

Climate change is a global phenomenon which is mainly caused due to anthropogenic activities. Land use-land cover (LULC) changes occur in river basins due to human interventions and change in climate. The change in LULC causes change in evapotranspiration and contributes to further climatic change in the area. Hydrologic extremes occur in a river basin due to the combined effect of climatic extremes and LULC changes. Both climate and LULC change scenarios are usually used for estimating the variation in the hydrological response of a watershed. Hence LULC change detection and estimation is very essential for any watershed prior to the estimation of hydrologic response variation from it. The influence of LULC change on the rainfall variability of a region was studied taking the Bharathapuzha river basin which is spread over Tamil Nadu and Kerala. Bharathapuzha river is the second longest river in Kerala. Drastic changes have taken place in the river basin which caused the degradation of the river. Hence the study of the effect of LULC change in the study region on the rainfall trend is of great significance. The LULC change in the area was estimated both spatially and temporally using remote sensing-based techniques. The LANDSAT1 and LANDSAT8 satellite images for the years 1973 and 2018 were used for determining the land cover change in the study region. Necessary corrections were applied on the mosaicked images and these satellite images were classified using supervised classification. The land use change analysis was done on the classified images and the land use classes used were waterbodies, mixed vegetation, agricultural field, cultivable land, dense trees, barren land/rocky outcrop, built-up area and sandy area. It is seen that there has been severe reduction in dense trees and large increase in barren land and built-up areas over time. The rainfall trend in the study region was estimated using the data obtained from India Meteorological Department. The study indicates that LULC change is associated to the decrease in the rainfall over the area. It denotes the necessity of controlling the LULC changes in the basin for preventing further deterioration of the basin. The rainfall trend analysis also helps to devise proper management plans for the basin.KeywordsClimate changeLand use-land cover changeRainfall trendMann–kendall testSen’s slope estimator

Remote sensing-based approaches to lithological mapping are traditionally pixel-oriented, with classification performed on either a per-pixel or sub-pixel basis with complete disregard for contextual information about neighbouring pixels. However, intra-class variability due to heterogeneous surface cover (i.e., vegetation and soil) or regional variations in mineralogy and chemical composition can result in the generation of unrealistic, generalised lithological maps that exhibit the “salt-and-pepper” artefact of spurious pixel classifications, as well as poorly defined contacts. In this study, an object-based image analysis (OBIA) approach to lithological mapping is evaluated with respect to its ability to overcome these issues by instead classifying groups of contiguous pixels (i.e., objects). Due to significant vegetation cover in the study area, the OBIA approach incorporates airborne multispectral and LiDAR data to indirectly map lithologies by exploiting associations with both topography and vegetation type. The resulting lithological maps were assessed both in terms of their thematic accuracy and ability to accurately delineate lithological contacts. The OBIA approach is found to be capable of generating maps with an overall accuracy of 73.5% through integrating spectral and topographic input variables. When compared to equivalent per-pixel classifications, the OBIA approach achieved thematic accuracy increases of up to 13.1%, whilst also reducing the “salt-and-pepper” artefact to produce more realistic maps. Furthermore, the OBIA approach was also generally capable of mapping lithological contacts more accurately. The importance of optimising the segmentation stage of the OBIA approach is also highlighted. Overall, this study clearly demonstrates the potential of OBIA for lithological mapping applications, particularly in significantly vegetated and heterogeneous terrain.

Support vector machine-based spatiotemporal land use land cover change analysis in a complex urban and rural landscape of Akaki river catchment, a Suburb of Addis Ababa, Ethiopia

This study aims to monitor the changes in land use land cover (LULC) in El-Fayoum governorate over time (past, present, and future) to provide current information for stakeholders involved in land use planning. The study utilized Landsat satellite images and applied the Support Vector Machine algorithm using ArcGIS Pro 2.8.3 to classify the images into four major LULC classes: water, desert, built-up, and agricultural. To evaluate the accuracy of the LULC maps, the study used kappa statistical parameters, which ranged from 0.91 to 0.94, indicating acceptable results for further analysis. To predict spatio-temporal LULC changes, the study considered biophysical and socioeconomic factors such as distance to canals, distance to roads, distance to urban areas, a digital elevation model, and slope. A combination of Multi-Criteria Evaluation, a Fuzzy Membership Function, and the Analytic Hierarchy Process were employed to develop a land cover suitability map. The Hybrid CA-Markov model of the IDRISI-TerrSet software was used to simulate LULC changes, and the accuracy of the simulation was validated using 2020 imagery data. The values gained from the kappa indices for agreement (standard) = 0.9006, kappa for lack of information (no) = 0.916, and kappa for location at grid cell level (location) = 0.9572 demonstrate that the results of the simulation of the LULC changes were deemed satisfactory. The future scenarios modeled in LULC indicate a significant change in the LULC classes over time, specifically for 2030. The change rates of agriculture, desert, built-up, and water areas in El-Fayoum in 2030 compared to 2020 are estimated to be 9.68%, − 17.58%, 133.62%, and 6.06%, respectively. These findings establish both past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

Water plays a vital role in sustaining the natural functioning of the entire ecosystem that supports life on Earth. It plays key roles in the well‐being of society in numerous ways. However, climate variability and land use land cover (LULC) change have caused spatiotemporal water supply variation. Disentangling the effects of climate variability from LULC change on water supply is crucial for sustainable water resource management. The main purpose of this study is, therefore, to disentangle the relative contribution of LULC change and climate variability to the overall average annual water supply variation. Residual trends analysis combined with Integrated Valuation of Environmental Services and Tradeoffs (InVEST) annual water yield model was adopted to perform simulations and disentangle the relative impacts of climate variability and LULC change. Ground and satellite data were used in this study. The study area has experienced a significant increasing wetness trend and significant LULC dynamics between 2003 and 2017. As a result, an increasing water supply was observed due to the joint effects of climate variability and LULC change in the watershed (203 mm). The contribution of climate variability was 94%, whereas LULC contributes only 6% from 2003 to 2017. Climate variability negatively led to water supply variation while LULC change contributed positively from 2010 to 2017. Although the ongoing soil and water conservation (SWC) practices improved vegetation cover and water retention of the watershed, climate variability is the main driver of water supply variation. Therefore, SWC practices should incorporate ecosystem‐based climate change adaptation strategies and scale up to community‐based integrated watershed management to sustain water supply.

Evaluating the joint effects of climate and land use change on runoff and pollutant loading in a rapidly developing watershed

Assessment of future prediction of urban growth and climate change in district Multan, Pakistan using CA-Markov method

Land use land cover (LULC) change is the crucial driving force that affects the hydrological processes of a watershed. The changes of LULC have an important influence and are the main factor for monitoring the water balances. The assessment of LULC change is indispensable for sustainable development of land and water resources. Understanding the watershed responses to environmental changes and impacts of LULC classes on hydrological components is vigorous for planning water resources, land resource utilization, and hydrological balance sustaining. In this study, LULC effects on hydrological parameters of the Nashe watershed, Blue Nile River Basin are investigated. For this, historical and future LULC change scenarios in the Nashe watershed are implemented into a calibrated Soil and Water Assessment Tool (SWAT) model. Five LULC scenarios have been developed that represent baseline, current, and future periods corresponding to the map of 1990, 2005, 2019, 2035, and 2050. The predicted increase of agricultural and urban land by decreasing mainly forest land will lead till 2035 to an increase of 2.33% in surface runoff and a decline in ground water flow, lateral flow, and evapotranspiration. Between 2035 and 2050, a gradual increase of grass land and range land could mitigate the undesired tendency. The applied combination of LULC prognosis with process-based hydrologic modeling provide valuable data about the current and future understanding of variation in hydrological parameters and assist concerned bodies to improve land and water management in formulating approaches to minimize the conceivable increment of surface runoff.

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.

LULC changes present the importance of watershed development program in agricultural land at watershed scale through interventions of various water harvesting structures especially in arid and semi-arid tropics. Bundelkhand region is a semi-arid tropic and hub of drought in Central India due to erratic rainfall, undulated topography and lack of water conservation practices. The study of last 20 years (2000-2019) revealed that 10 are severe drought years, 5 normal and 5 excess rainfall years. Under such circumstances, various water conservation practices were adopted and analysing their impacts through land use land cover map. Analysing landuse land cover using RS and GIS is a vital to investigate land use patterns and helping forecast for future sustainable land management and therefore, Garhkundar-Dabar(GKD) watershed located inTikamgrah district of Madhya Pradesh,Bundelkhand region of Central India was selected for the study.During the study, watershed is divided into two groups (first treated watershed and second control watershed) for hydrological study but in this study treated watershed is preferred based on objective.All the cloud free Landsat 7 and 8 imagery data pertaining to year 1999 to 2019 were downloaded from USGS and digitized in GIS environment in Arc GIS 10.3. In this study, LULC was prepared by using ERDAS Imagine 9.2 from 1999 to 2019 under supervised classification. The overall accuracy was found 86.7% for 1999, 88.3% for 2007 and 89.7% for 2019. The analysis revealed a huge transformation of fallow land (90 ha) into cultivated land (265 ha) and agroforestry based crop cultivation willingnessenhanced. Thus water conservation practices should be adopted and replicated at watershed scale for sustainable land management in drought prone areas.

As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities and continuous climate change. However, extensive cloud cover limits the ability of optical remote sensing satellites to monitor accurately LULC changes in this area. To overcome this problem in LULC mapping in the Ganan Prefecture, 2000–2018, we used the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform. The dynamic trends of LULC changes were analyzed, and geographical detectors quantitatively evaluated the key driving factors of these changes. The results showed that (1) the overall classification accuracy varied between 89.14% and 91.41%, and the kappa values were greater than 86.55%, indicating that the classification results were reliably accurate. (2) The major LULC types in the study area were grassland and forest, and their area accounted for 50% and 25%, respectively. During the study period, the grassland area decreased, while the area of forest land and construction land increased to varying degrees. The land-use intensity presents multi-level intensity, and it was higher in the northeast than that in the southwest. (3) Elevation and population density were the major driving factors of LULC changes, and economic development has also significantly affected LULC. These findings revealed the main factors driving LULC changes in Gannan Prefecture and provided a reference for assisting in the development of sustainable land management and ecological protection policy decisions.

This paper concerns the study of the seafloor through digital seabed elevation models, using object based image analysis methods. The goal of this research was the automated extraction of geomorphological features from the seabed in regions presenting intense volcanic activity. The study area is located around the submarine volcano of the Kolοumbo (in the submarine area northeast of the Santorini island, Greece). For this purpose, a Digital Elevation Model (DEM) of the seabed with a spatial resolution of 50m was used. Derivatives of the DEM, such us Slope, Topographic Position Index (TPI) and Terrain Ruggedness Index (TRI) were created in the open source software "QGIS 2.4". The implementation of the object based image analysis approach was performed in eCognition 8.7 software. Nine segmentation and classification levels were created in order to produce the final level segmentation "level 5", where the final geomorphological features were classified. The results of the method were evaluated using classification stability measures and qualitative and quantitative comparison of the results with existing map.

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China