Land Cover Change Models Research Articles

Predictive modeling of land cover changes in round-1 smart cities of India using cellular automata and GIS

The present study dealt with the Land Use Land Cover (LULC) and built-up dynamics for four round-1 smart cities within a period of 20 years (2001–2021) using satellite-based remote sensing and Geographic Information System (GIS) techniques. The study also presented predictive aspects of built-up dynamics by the year 2031. Various layers contribute to urban growth viz. slope, population density, distance to Central Business District (CBD), major roads, and drainage were analyzed and prepared. The Cellular Automata (CA) model was applied using the previous years’ layers and the thematic ones to predict the spatial extent of the year 2031. Thresholds for all thematic layers based on spatial proximity were selected for various classes of these factors. Built-up change rate for all the four round-1 smart cities viz. Ahmedabad (the year 2001 ~ 183.79 km2, the year 2021 ~ 252.3 km2), Chennai (the year 2001 ~ 102.45 km2, the year 2021 ~ 137.07 km2), Jaipur (the year 2001 ~ 132.73 km2, the year 2021 ~ 221.71 km2) and Surat (the year 2001 ~ 97.73 km2, the year 2021 ~ 158.79 km2) has shown a dynamic growth from 2001–2021. The study also revealed transition states of land cover classes between 2021 and 2031. The comparison between actual and simulated changes for the year 2021 showed the model’s accuracy assessment. The estimates also revealed a continuous increment in the impervious state of the urban surface due to urban expansion. Thus, satellite image-based LULC classification, built-up mapping, and model-based prediction could be utilized for planning infrastructure development.

Prediction of Land Cover Changes in Evaluation of Regional Spatial Planning in the Keduang Sub-Watershed, Wonogiri Regency

River watersheds have an important function for water absorption for human life. The Pemudang sub-watershed is a national priority because its condition is increasingly critical due to land degradation. This research aims to carry out spatial modeling of land cover changes in 2031 in the Uangng Sub-watershed which will be compared with the 2011-2031 Regional Spatial Plan scenario. The method used uses the Cellular Automata-Markov Chain method. Based on predictions of land cover in 2031, agricultural areas dominate in the Uangng Sub-watershed covering an area of 14,783.4 ha (37.5%). Agricultural area according to the model in 2031 is estimated to increase by 97.5 ha. The difference between the 2031 model area and the region spatial plan is 5,168.9 ha, which means that the region spatial plan settlements are not expected to be able to accommodate future settlements. The forest area in the 2031 model is 12,351.6 ha, while in the region spatial plan it is 12,300.3 ha with an area difference of 51.2 ha. This shows that controlling the use of space in forests is not in accordance with the policies in the region spatial plan.

Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management

A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.

Broadening the horizon in land use change modelling: Normative scenarios for nature positive futures in Switzerland

Within scenario-based research of social-ecological systems, there has been a growing recognition of the importance of normative scenarios that define positive outcomes for both nature and society. While several frameworks exist to guide the co-creation of normative scenario narratives, examples of operationalizing these narratives in quantitative simulation modelling are still limited. To address this gap, this paper presents an example of how aspects of normative scenarios can be realized within a spatial model of land use and land cover change. This is achieved through a combination of data-driven approaches to encapsulate scenario-specific differences in local and global scale phenomena, as well as iterative expert elicitation to quantify descriptive trends from narratives. This approach is demonstrated with a case study simulating five scenarios of landscape change (three normative and two exploratory) in Switzerland between 2020 and 2060. The resulting maps of future land use and land cover exhibited distinct variations between the scenarios, notably with regard to the prevalence of areas of heterogeneous semi-natural land, such as alpine pastures and grassland, often considered culturally emblematic of Switzerland. While the simulation results were generally consistent with the outcomes expressed in the scenario narratives, following a process of expert feedback, we reflect that there are clear challenges in leveraging such results to elicit further discussions as to the desirability and plausibility of future scenarios. Specifically, the need to summarize spatial simulations in a manner that is easily interpretable and encourages consideration of the broader patterns of change rather than focusing on fine-scale details.

Modeling of Land Cover Changes in Banjarbaru City South Kalimantan Province

Urban areas often experience land cover changes. Banjarbaru is one of several cities in Indonesia that has experienced land changes. The relocation of the administrative center of Banjarmasin City to Banjarbaru City led to the development of settlements. One spatial analysis carried out to examine the phenomenon of land change is remote sensing techniques. The method that can be used is the Land Change Modeler from MOLUSCE in QGIS. This model uses the CAM (Cellular Automata Markov) method to identify land cover change and predict land cover distribution. CAM can understand and predict land change patterns by considering land use, vegetation, and cell spatial interactions. This modeling is based on land cover data for 2015 and 2020 and several supporting parameters such as DEM data and distance to roads. Based on the modeling results from 2015 and 2020, Banjarbaru City experienced a change in built-up land, with most of it occurring in the center of Banjarbaru City. Based on the Markov Chain method by looking at land changes in the previous year, the development of built-up land increased by about 8% of the Banjarbaru City area of 32917.41 hectares. Based on the prediction results, the development of built-up land is centered in the middle of Banjarbaru City, such as North and South Banjarbaru Districts, due to the development of residential development.Keywords: Land Cover, Land Change Modeller, Cellular Automata, Markov Chain

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

A management unit-based land cover change analysis was examined in Kahayan Tengah Forest Management Unit (FMU) to understand past, present, and future land cover to assist forest management planning in Kahayan Tengah FMU. This study aims to model land cover change in 2011 and 2016, predict 2021, and simulate land cover in 2026 in Kahayan Tengah FMU. Modeling land cover prediction and simulation using MOLUSCE from the QGIS plugin. The results revealed that agricultural land experienced significant increase in total area during 2011–2016. Modeling potential land cover transitions in 2011 and 2016 with the Artificial Neural Network method showed a Kappa coefficient of 0.701 in the good category, and simulation of land cover in 2021 with the Cellular Automata method showed a Kappa coefficient of 0.672 in the good category. By 2026, the agricultural land will continue to increase while forest land tends to remain stable in its total area. This study managed to predict land cover in 2021 and simulated 2026 with good accuracy. Thus, this data and information can support forest management planning in Kahayan Tengah FMU. Keywords: forest management unit, Kahayan Tengah, land cover change, land cover prediction, MOLUSCE

Land use and land cover change simulation enhanced by asynchronous communicating cellular automata

Land use and land cover change (LUCC) modeling is crucial to urban planning and policy making. A well-used and effective paradigm for LUCC is the ANN-CA model which employs an artificial neural network (ANN) to calculate a transition probability over each land cell from various driving factors, and then uses a cellular automaton (CA) to evolve all cells consecutively to update their land usage and coverage according to the estimated probability distribution. This paper focuses on the effect of delays or perturbations possibly taking place between land cells on the LUCC modeling. To this end, a new ANN-CA model is proposed which adopts an asynchronous communicating cellular automaton (ACCA), rather than the conventional synchronous CA. Especially, the ACCA allows every cell to communicate with its neighbors independently at random times via a specific protocol, which offers a natural way to include stochastic delays in exchanging the current land usages between cells. As a result, every change of a cell's land use in the ACCA may not affect its neighbors immediately, but is subject to delays that might play an important role in modeling the practical LUCC. Numerical analysis of the new model are carried out over three regions of Chongqing city, China with different scales: Yongchuan District, Sanjiao Town, and Huangguashan Village, and experimental results demonstrate that the proposed ANN-ACCA model can achieve a higher accuracy for LUCC simulation as compared to conventional ANN-CA models.

Calibration interval scenario approach in spatial modeling of land cover change in East Kalimantan from 2016 to 2036

Spatial modeling can be used to predict future land cover changes based on past and present conditions. However, it is not yet known to what extent this model can be used to predict the future with reliable accuracy. Therefore, by using multi-temporal land cover data, this study aims to build an optimal model based on the calibration interval scenario. The optimal model is then used to predict and analyze changes in land cover in East Kalimantan in 2016–2036. 11 classified multi-temporal land cover maps from the Landsat Time Series using Random Forest in Google Earth Engine are used to model 14 calibration interval scenarios. A land Change Modeler is used to model and predict land cover change with 14 driving variables. The results of the classification of multi-temporal land cover maps show a good level of accuracy, with an Overall Accuracy value of 71.43–85.14% and a Kappa value of 0.667–0.827. Then 2016–2021 is one of the best scenarios with 5-year intervals where the accuracy of future predictions can still be relied upon for up to three prediction iterations. The calibration interval scenario approach in spatial modeling in East Kalimantan can be relied upon to show a decrease in forest cover from 2016 to 2021, with a deforestation rate of 651 km2/year. The prediction of land cover in 2036 estimates that the remaining forest cover area in East Kalimantan is 69.203 km2. It is believed that topography is the most influential variable driving land cover change in East Kalimantan.

Modeling of land use and cover changes (LUCC) in Deli Serdang Regency, North Sumatra Province

Land use/cover (LUC) is a substantial factor in land management and can influence policy in an area. LUC has the potential to change due to physical, economic, and social aspects. This study aims to analyze the spatialland use and cover changes (LUCC) in Deli Serdang Regency for the 2010 to 2020 period and predict LUC in 2030. The analysis was run by applying the Cellular Automata-Markov Chain method. The driving factors used in this modeling are the distance to the road, the distance to the river, population density, the distance to the district capital, and the distance to Medan city. The results showed that Kappa for image classification was 0.86. The dominant type of LUC in Deli Serdang Regency is a plantation, with a total area of more than 45%, followed by paddy fields, dryland agriculture, forests, and settlements/built-up areas. LUCC model validation obtained a kappa value of 0.89 (very good category) and can be applicated for predicting land use change models in 2030. By 2030, the settlements/built-up area and dryland agriculture will increase significantly, which 21,060 ha and 4,587 ha, respectively, while forests, plantations, and paddy fields will decrease significantly by around 9,266 ha, respectively, respectively 8,306 ha and 7,806 ha.

Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes

This study aimed to enhance land use and land cover (LULC) change models by addressing their main limitations, which include the lack of accountability and temporal stability of driving forces. Additionally, the study aimed to create area-based scenarios to forecast future LULCs, rather than solely relying on distribution-based scenarios. To accomplish this goal, the study developed a coupled System Dynamics (SD) and Cellular Automata (CA) modeling system to simulate possible LULC changes in the Gavkhooni Basin, central Iran. The study utilized LULC maps from Landsat images in 2001, 2011, and 2021 to analyze spatio-temporal land use changes in the region. Agricultural and residential transition suitability layers were produced using a spatial Multi-Criteria Evaluation procedure and applied to inform the CA model in the proper allocation of LULC changes. Three interconnected water supply, agricultural, and residential area projection subsystems were developed using system dynamics method to determine land requirements for LULC conversions from 2020 to 2041, taking into account factors such as water availability, land suitability, agricultural labor force, and economic development. Ten scenarios were developed based on changes in the key variables affecting the limiting factors, such as climatic conditions and water management policies, to project agricultural and residential areas in the future. The CA's spatial allocation informed by transition suitability layers was found to be satisfactory with a Kappa-location value of 0.85. The subsystems were competent in projecting water supply with Mean Absolute Error (MAE) values of 6.57% and the dynamics of agricultural and residential areas with MAE values of 2.94%, whereas those of the Markovian Chain model were found to be 23.02% and 7.5% for agricultural and residential areas, respectively. The study found that available agricultural areas varied significantly between 86.53 and 1480 sq.km under different climatic conditions, irrigation efficiency, and agricultural water assignment coefficients between 2024 and 2033. Residential area demand was found to be increasing with different rates under the scenarios between 47.40 and 73.01 sq.km. The SD-CA coupled framework presented in this research can be viewed as a decision support system to develop compensatory strategies for better management and planning of agricultural and residential lands.

A series of spatio-temporal analyses and predicting modeling of land use and land cover changes using an integrated Markov chain and cellular automata models.

For sustainable land cover planning, spatial land cover models are essential. Deforestation, loss of agriculture, and conversion of pasture land to urban and industrial uses are only some of the negative consequences of human kind's insatiable need for more land. Using remote sensing multi-temporal data, spatial criteria, and prediction models can effectively monitor these changes and plan for sustainable land use. This research aims to predict the land use and land cover (LULC) with cellular automata (CA) and Markov chain models. Landsat TM, ETM + , and OLI/TIRS data were used for mapping LULC distributions for the years 1990, 2006, and 2022. A CA-Markov chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2054. Analysis of urban sprawl was carried out by using the support vector machine (SVM). Through the CA-Markov chain analysis, we expect that built-up area will grow from 285.68 km2 (22.59%) to 383.54 km2 (30.34%) in 2022 and 2054, as inferred from the changes that occurred from 1990 to 2022. Therefore, substantial deforestation area reduction will result if existing tendencies in change continue despite sustainable development efforts. The findings of this research can inform land cover management strategies and assist local authorities in preparing for the present and the future. They can balance expanding the city and preserving its natural resources.

Genetic viability and habitat suitability of the critically endangered southern muriqui (Brachyteles arachnoides) in the Atlantic Forest's fragmented landscapes under land use and climate change scenarios

The joint effects of climate change and landscape fragmentation to the genetic viability of isolated populations has barely been addressed for the Atlantic Forest fauna. Therefore, this work explored the potential habitat suitability for the southern muriqui (Brachyteles arachnoides), by modeling climate change, landscape fragmentation, and genetic diversity loss of the species. Maxent was used to model its potential distribution in 2050, with two climate change scenarios. A land use and land cover change model was applied to describe current and future forest fragmentation patterns, and a Population and Habitat Viability Analysis (PHVA) was used to describe the retention of genetic diversity of the southern muriqui. Although PHVA modeling provided a low risk of extinction of the southern muriqui, climate change and fragmentation could result in the loss of >65% of the suitable forest patches, and reduce the habitat suitability to only 11% of the potential distribution area, which could lead to future genetic diversity loss and decreased capacity of self-sustained populations. In both climate change scenarios, the suitable areas for the southern muriqui in Paraná and Rio de Janeiro states will decrease more drastically. Areas where the primate occurs in the interior of São Paulo and Rio de Janeiro states will disappear or be climatically disconnected from the core potential habitat. Alike preventing further deforestation, Atlantic Forest restoration actions are needed to connect the viable populations for compensating the projected land use and climate change impacts to the long term persistence of the southern muriqui.

A Comparative Study of Various Land Use and Land Cover Change Models to Predict Ecosystem Service Value

Ecosystem services are closely related to human well-being and are vulnerable to high-intensity human land-use activities. Understanding the evolution of land use and land cover (LULC) changes and quantifying ecosystem service value (ESV) are significant for sustainable development. In this study, we used land use and land cover data and other data from 2000 to 2020 to analyze the evolution of land use and land cover and ESV in Tongliao, China. With the goal of exploring the characteristics of different cellular automata (CA)-based models, CA-Markov, Future Land Use Simulation (FLUS), and Patch-generating Land Use Simulation (PLUS) models were used to simulate future land use and land cover, and the results were verified and compared. Considering the impacts of policies for capital farmland (CF) and ecological protection red line (EPRL) in the context of territorial spatial planning, four scenarios (inertial development, S1; CF, S2; EPRL, S3; EPRL and CF, S4) were set. The results showed that from 2000 to 2020, farmland and built-up land increased the most (341.18 km2 and 220.56 km2), while grassland had the largest decrease (380.08 km2). The main mutual transitions were from grassland and farmland. The total ESV showed a decreasing trend (from 52,364.56 million yuan to 51,620.62 million yuan). The simulation results for 2035 under four scenarios were similar, where farmland would decrease the most (96.81 km2). The ESV in 2035 would decrease from 51,620.62 million yuan to 51,541.12 million. In addition, under scenarios for the impact of policy, the land showed a trend of scattered expansion. This study provides a scientific basis for making regional sustainable development policy decisions and implementing ecological environmental protection measures.

Re-considering the status quo: Improving calibration of land use change models through validation of transition potential predictions

The increasing complexity of the dynamics captured in Land Use and Land Cover (LULC) change modelling has made model behaviour less transparent and calibration more extensive. For cellular automata models in particular, this is compounded by the fact that validation is typically performed indirectly, using final simulated change maps; rather than directly considering the probabilistic predictions of transition potential. This study demonstrates that evaluating transition potential predictions provides detail into model behaviour and performance that cannot be obtained from simulated map comparison alone. This is illustrated by modelling LULC transitions in Switzerland using both Logistic Regression and Random Forests. The results emphasize the need for LULC modellers to explicitly consider the performance of individual transition models independently to ensure robust predictions. Additionally, this study highlights the potential for predictor variable selection as a means to improve transition model generalizability and parsimony, which is beneficial for simulating future LULC change.

Spatial dynamic model of land use land cover change on hydrological response characteristics in the Upper Ciliwung Sub-Watershed

Ciliwung Watershed is one whose state is critical. This condition causes the expansion of residential areas upstream of Ciliwung. Land use and land cover change affect a region’s hydrological characteristics. As a hydrological model, the SWAT (Soil and Water Assessment Tools) can predict the hydrological watershed characteristics affected by land use. This study aims to assess the land use change influence on hydrological characteristics and variations in inflow and outflow to develop the ideal recommendations for land use in the Ciliwung sub-watershed using system dynamics. The main river channel is constructed based on the watershed delineation process results, the watershed boundary consisting of 27 sub-watersheds. Due to average monthly discharge, the impact of land use change on hydrological characteristics in 2011, 2016, and 2020 were 65.01, 63.59, and 64.31 m3/second, respectively. The hydrological characteristics of all HRUs generated revealed that the Q max was 13,09 m3/s, and the Q min was 0.18 m3/s. In upstream Ciliwung, based on an inflows and outflows analysis, there were variations in 2011-2016 and 2016-2020. Based on the calibrated simulation results, the coefficient of determination (R2) was 0.8, and NSE was 0.78. The resulting R2 and NSE values indicate excellent model performance in the validation results. The ideal scenario is the intervention scenario on land use and population demography since it can reduce runoff. Runoff is the water that flows over the surface because the soil has reached its maximum infiltration capacity.

Assessing the Impact of Neighborhood Size on Temporal Convolutional Networks for Modeling Land Cover Change

Land cover change (LCC) studies are increasingly using deep learning (DL) modeling techniques. Past studies have leveraged temporal or spatiotemporal sequences of historical LC data to forecast changes with DL models. However, these studies do not adequately assess the association between neighborhood size and DL model capability to forecast LCCs, where neighborhood size refers to the spatial extent captured by each data sample. The objectives of this research study were to: (1) evaluate the effect of neighborhood size on the capacity of DL models to forecast LCCs, specifically Temporal Convolutional Networks (TCN) and Convolutional Neural Networks (CNN-TCN), and (2) assess the effect of auxiliary spatial variables on model capacity to forecast LCCs. First, each model type and neighborhood setting configuration was assessed using data derived from multitemporal MODIS LC for the Regional District of Bulkley-Nechako, Canada, comparing subareas exhibiting different amounts of LCCs with trends obtained for the full region. Next, outcomes were compared with three other study regions. The modeling results were evaluated with three-map comparison measures, where the real-world LC for the next timestep, the real-world LC for the previous timestep, and the forecasted LC for the next year were used to calculate correctly transitioned areas. Across all regions explored, it was observed that increasing neighborhood sizes improved the DL model’s capabilities to forecast short-term LCCs. CNN–TCN models forecasted the most correct LCCs for several regions while reducing error due to quantity when provided additional spatial variables. This study contributes to the systematic exploration of neighborhood sizes on selected spatiotemporal DL techniques for geographic applications.

Remote sensing and geographic information systems technics for spatial-based development planning and policy

Indonesia's land-use and land-cover change (LULCC) is a global concern. The relocation plan of the capital city of Indonesia to East Kalimantan will be becoming an environmental issue. Knowing the latest land cover change modeling and prediction research is essential for fundamental knowledge in spatial planning and policies for regional development. Five articles related to integrated technology of geographic information systems (GIS) and remote sensing for spatial modeling were reviewed and compared using nine variables: title, journal (ranks), keywords, objectives, data sources, variables, location, method, and main findings. The results show that the variables that significantly affect LULCC are height, slope, distance from the road, and distance from the built-up area. The artificial neural network-based cellular automata (ANN-CA) method could be the best approach to model the LULCC. Furthermore, by the current availability of global multi-temporal and multi-sensor remote sensing data, the LULCC modeling study can be limitless

Editorial for the Special Issue: “Integrated Applications of Geo-Information in Environmental Monitoring”

Geo-information technology has been playing an increasingly important role in environmental monitoring in recent decades. With the continuous improvement in the spatial resolution of remote sensing images, the diversification of sensors and the development of processing packages, applications of a variety of geo-information, in particular, multi-resolution remote sensing and geographical data, have become momentous in environmental research, including land cover change detection and modeling, land degradation assessment, geohazard mapping and disaster damage assessment, mining and restoration monitoring, etc. In addition, machine learning algorithms such as Random Forests (RF) and Convolutional Neural Networks (CNN) have improved and deepened the applications of geo-information technology in environmental monitoring and assessment. The purpose of this Special Issue is to provide a platform for communication of high-quality research in the world in the domain of comprehensive application of geo-information technology. It contains 10 high-level scientific papers on the following topics such as desertification monitoring, governance of mining areas, identification of marine dynamic targets, extraction of buildings, and so on.

Modeling of Cellular Automata Markov Chain for Predicting the Carrying Capacity of Ambon City

Ambon is a city with the highest economic and population growth in Maluku Province, which makes built-up land have high and rapid growth so that it can reduce the carrying capacity of the city's land. This can lead to an imbalance between population and available housing and may result in higher population pressure on available areas. Prediction of spatial modeling is needed as a preventive measure to prevent excessive land cover changes in the future. This study aims to analyze the carrying capacity of residential areas and spatial modeling of land cover changes in Ambon City in 2010, 2015, and 2020 using Cellular Automata Markov Chain (CAMC) and identify settlement patterns based on population density in 2031 in Ambon city. The results of the Cellular Automata Markov Chain (CAMC) analysis show that the residential area in 2031 has increased to 7,910.03 ha and the development of residential areas in 2031 in Ambon City is centered on Sirimau District.

Editorial on Special Issue “Geo-Information Technology and Its Applications”

Geo-information technology plays a critical role in urban planning and management, land resource quantification, natural disaster risk and damage assessment, smart city development, land cover change modeling and touristic flow management. In particular, the development of big data mining and machine learning techniques (including deep learning) in recent years has expanded the potential applications of geo-information technology and promoted innovation in approaches to mining in different fields. In this context, the International Conference on Geo-Information Technology and its Applications (ICGITA 2019) was held in Nanchang, Jiangxi, China, 11–13 October 2019, co-organized by the Key Laboratory of Digital Land and Resources, East China University of Technology, the Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS), which was renamed in 2017 the Aerospace Information Research Institute (AIR), CAS, and the Institute of Space and Earth Information Science of the Chinese University of Hong Kong. The outstanding papers presented at this event and some other original articles were collected and published in this Special Issue “Geo-Information Technology and Its Applications” in the International Journal of Geo-Information. This Special Issue consists of 14 high-quality and innovative articles that explore and discuss the typical applications of geo-information technology in the above-mentioned domains.