Predict Land Use Research Articles

A fractional land use change model for ecological applications

By mapping land use under projections of socio-economic change, ecological changes can be predicted to inform conservation decision-making. We present a land use model that enables the fine-scale mapping of land use change under future scenarios. Its predictions can be used as input to virtually all existing spatially-explicit ecological models. Our model maps the fractional cover of land use within each grid cell, providing higher information content than discrete classes at the same spatial resolution. The method accurately reproduced land use patterns observed in the Amazon, both in terms of the allocated fractional amounts and also the direction of predicted land use changes. A small case study showcases the application of our model to reproduce patterns of agricultural expansion and natural habitat declines. The model source code is provided as an open-source R package, making this new method available to bridge the gap between socio-economic, land use and biodiversity modelling.

Urban livability index assessment based on land-use changes in an Indian medium-sized city (Raiganj)

Improvement of a city’s liveability, especially in developing countries, is increasingly viewed as a way to enhance city dwellers’ quality of life. This study applied remote sensing and GIS-based techniques to assess urban livability conditions based on the World Health Organization guidelines. For this, 21 fine-grained spatial indicators were selected, and a geographically weighted principal component analysis was applied to evaluate the spatial pattern of urban livability in Raiganj. The results showed that the city’s central and eastern part had a livability index score over 0.75 (5.31 km2 area) appeared as the most livable. The southwestern part of the city was the least livable. Finally, the predicted Land Use Land Cover for 2025 in Raiganj based on the Multilayer Perceptron Neural Network-Markov Chain showed that the city (+19.50%) will expand mainly in the eastern direction at the expense of green and blue spaces.

Analyzing and Predicting Land Use and Land Cover Changes in New Jersey Using Multi-Layer Perceptron–Markov Chain Model

This study analyzed the changes of land use and land cover (LULC) in New Jersey in the United States from 2007 to 2012. The goal was to identify the driving factors of these changes and to project the five-year trend to 2100. LULC data was obtained from the New Jersey Department of Environmental Protection. The original 86 classes were reclassified to 11 classes. Data analysis and projection were performed using TerrSet 2020. Results from 2007 to 2012 showed that the rate of LULC changes was relatively small. Most changes happened to brush/grasslands, mixed forest lands, farmlands and urban/developed lands. Urban/developed lands and the mixed-forest cover gained while farmlands lost. Using a multi-layer perceptron–Markov chain (MLP–MC) model, we projected the 2015 LULC and validated by actual data to produce a 2100 LULC. Changes from 2012 to 2100 showed that urban/developed lands, as well as brush/grasslands, would continue to gain, while farmlands would lose, although the projected landscape texture would likely be identical to the 2012 landscape. Human and natural factors were discussed. It was concluded that the MLP–MC model could be a useful model to predict short-term LULC change. Unexpected factors are likely to interfere in a long-term projection.

A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output.

Spatial Pattern Simulation of Land Use Based on FLUS Model under Ecological Protection: A Case Study of Hengyang City

With rapid economic development in China, the excessive expansion of cities has led to the imbalance of land use structure, and then the ecological regulation function of the land ecosystem experiences problems, which has become an obstacle to sustainable development. Therefore, in order to protect the ecological environment, regulate urban development and pursue the maximization of ecological benefits, it is necessary to analyze, simulate and predict land use change. In this study, Hengyang City was taken as the study area, and based on the current land use data of Hengyang City in 2010, 2015, and 2018, the land use type transfer during 2010–2015 and 2015–2018 was analyzed. Then, starting from 2010, the FLUS model was used to simulate the spatial distribution of land use in 2015 and 2018, and then the spatial distribution of land use in Hengyang City in 2025 was predicted with the Markov prediction method under the premise of ecological protection priority. The results show that the change in ecological land in Hengyang City is mainly distributed in the surrounding and marginal areas, because the topography of Hengyang City is a basin. Changes in land type in Hengyang City in 2015 were subtle and difficult to observe. However, in 2018, the transformation of non-ecological land into ecological land was obvious, and the distribution area of ecological land expanded significantly. The Kappa index of the results simulated by the FLUS model based on neural network is above 0.72, and overall accuracy is above 0.9, which is highly consistent with the actual situation. It is reasonable and convincing to predict the spatial distribution of land use in the context of ecological protection. The predicted results can be useful for urban planning and land use distribution and provide a reference for relevant decision-makers.

What is the future for production-living-ecological spaces in the Greater Bay Area? A multi-scenario perspective based on DEE

Rapid industrialization and urbanization accelerate land use and land cover change, resulting in problems of stability and health with ecosystem services losses. It remains unclear and needs to be detailed discussed what spatial–temporal characteristics of the production-living-ecological spaces (PLES) would be in a rapidly developing bay area under a certain development goal. A novel future land use simulation model integrated with a dual environment evaluation system consisting of the evaluation of resources and environmental carrying capacity and territorial spatial development suitability was conducted to predict the PLES in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in a multi-scenario view; and then we figured out the spatial–temporal changes of the PLES in the GBA during 2015–2035. Results suggest: 1) Using the DEE to determine conversion probability estimation helps to simulate and predict land use changes with high accuracy. Under the CUA scenario, the PLES account for 21.76%, 14.60% and 63.64%, respectively. The production space in Shenzhen, Hong Kong and Dongguan is dispersed, while the living space in Dongguan is clustered. The patch boundary of ecological space in Zhaoqing, Huizhou and Jiangmen would become more complex. 2) The change of the PLES in the GBA is mainly manifested by the processes of production and ecological space transferring to living space even under CUA scenario, which mainly occurs on the edge of the existing living space, especially in Zhaoqing, Conghua District of Guangzhou, Longmen County and Boluo County of Huizhou. 3) With potential conflicts between production space and ecological space, more attention should be paid to reducing the occupation of basic farmland in ecological restoration in the GBA. The future PLES in the GBA would be characterized by the conflict between the living and non-living spaces and that between productive and ecological spaces. The predicted PLES under CUA scenario may provide a spatial reference for policy-making in the future territorial spatial development and construction of ecological security patterns in the GBA.

Analisis Perubahan Penggunaan Lahan Dan Prediksinya dengan Menggunakan Markov – Cellular Automata Di Wilayah Peri Urban Kota Malang

Peri Urban Area is a unique region with spatial dynamics that continues to experience changes that have an important role to play in the urban life in the future. There are 8 sub-districts in Malang Regency which are directly adjacent to Malang City and have a significant influence on the development of the city. Objectives of this research are to determine the dynamics of land use change from 2008 - 2018 and to predict land use in 2030. The method used is the analysis of land use changes from landsat TM 8 images in 2008 and 2013 to predict land use in 2018 which then tested the validity to get a level of accuracy. The results showed the development of built-up area has increased by 12% while agricultural land has experienced a declining trend. Significant changes occurred in Singosari, Pakisaji and Karangploso sub-district. Validation of land use between the predictions of 2018 and actual land use in 2018 showed that the value of kappa was quite high, at 87%. The trend of land use in peri-urban areas until the year 2030 is predicted to have built up area of 26,456 ha, which means an increase 17,686 ha (33.6%) from the existing year 2018. The potential incompatibility of the RTRW with the predicted land use in 2030 is 11,950 ha or 22.7%.

The Evaluation of Regional Spatial Plan for 2011-2031 Based on Land Use Changes Prediction Using Cellular Automata-Markov Model in Sleman

In the past years, the development of Sleman Regency has been considered rapid as evidenced by the emergence of built up areas including expansion of the university areas, shopping malls, and housing. Along with the increase in the total population, university students and workers from other regions coming to this regency, the land use in Sleman Regency has started to shift. Land use changes need to be controlled by predicting land use using the CA-Markov model. CA-Markov modeling has dynamic properties that integrate the dimensions of space and time, where the occurrence of events is determined by events that directly precede them and can be used to predict the next event. The accuracy of the CA-Markov concept can be determined by validation and expressed in the Kappa coefficient value (≥ 0.70). This CA-Markov concept has been developed since the 1940s in the field of computers by Von Neumann and Ulam. In this concept it is assumed that pixels are the beginning of the mathematical concept. When a pixel changes, its new status is only affected by its old status and the neighbor status. This research was conducted to predict the land use in 2031 using the Cellular Automata-Makov model, evaluate the use of land in 2031 in relation to RTRW or city plan, and create a scenario of the direction for land use control in 2031 for disaster-prone areas. Based on the prediction of land use in Sleman Regency in 2031, Kappa coefficient was obtained at 0.7399, implying that the suitability of spatial area and distribution reached 73.99% which is considered good. The results of the prediction also showed that in 2031, the land use would be dominated by building area which was predicted to reach 43.53% out of the total area. The evaluation of land use prediction in 2031 based on RTRW method showed that as large as 40.137,39 ha land would be used according to the RTRW, while 17.411,00 ha would not be used accordingly. The improper use of land might be due to the shift in the use of 4.659,18 ha of rice fields into buildings.

Rapid adaptive modelling for policy support towards achieving Sustainable Development Goals: Brexit and the livestock sector in Wales

Sustainable use of land is central to achieving the United Nations Sustainable Development Goals (SDG). However, implementation of policy targeting SDG delivery can be driven off track by external factors. There is a pressing need for rapid, adaptable modelling approaches to support policy development against a background of dynamic changes in environmental and political conditions. An example is the imminent departure of the UK from the EU (Brexit), which requires responsive inputs of robust evidence to inform decision making. We combine existing agri-economic modelling with well-tested environmental models, driven by farm survey and landscape data in an adaptive, participatory approach to assess potential agricultural sector responses to Brexit. We explore potential Brexit impacts on land use, environmental outcomes and agricultural jobs in Wales, UK, where agriculture is dominated by animal production on grassland systems. Three potential post-Brexit trade agreements scenarios are considered: EU Deal; No Deal; and Multilateral Free Trade Agreements. For each scenario potential changes in animal numbers in livestock systems in Wales are converted to predicted land use change at field and farm scale and combined with national data sources and models to explore consequences for the environment and jobs. Potential changes in grazing animal numbers range between − 30% (sheep for No Deal and MFTA) and + 53% (No Deal dairy), affecting 3% (EU Deal) to 17% (No Deal) of agricultural land and creating potential losses of 700 (EU Deal) to 7000 (MFTA) full time jobs. Environmental outcomes are mixed, with reductions in greenhouse gas emissions (− 1% EU deal to − 16% MFTA) and variable impacts on pollutant loads to water (N ranges + 3% for No Deal to − 14% for MFTA) across all scenarios. Air pollution is most scenario dependent (+ 11% No Deal to − 12% MFTA), and biodiversity showed a mixed response to each scenario. The findings of this study support Welsh government in developing programmes to manage the Brexit transition proactively, mitigating risks to the environment, rural communities and agricultural sector..

Prediksi Perubahan Penggunaan Lahan dan Pola Berdasarkan Citra Landsat Multi Waktu dengan Land Change Modeler (LCM)

Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry

Integration of Neural Network, Markov Chain and CA Markov Models to Simulate Land use Change Region of Behbahan County

Purpose- Important land use typical of human impact on the environment and in the past half century, land use changes has been the largest and most complete. the modeling of land cover changes provides valuable information for understanding changes process, determining effective factors and forecasting areas which are exposed to changes. Therefore, the present study aims at (i) using the Landsat imagery to prepare land use- cover (LULC) maps for 2000 and 2014; (ii) assessing Land use changes on the basis of land change modeler (LCM) for the period from 2000 and 2014, and (iii) predicting the plausible land cover pattern in the region of Behbahan using an algorithm based on ANN for 2028. Design/methodology/approach- Hybrid model consisting of neural network model, Markov chain (MC), and cellular automata (CA Markov) were designed to improve the performance of the standard network model. The modeling of transfer power is done by multilayer Perceptron of artificial neural network and six variables. The change allocated to each use and the forecasting is computed by Markov chain and CA Markov. Operation model calibration and verification of land use data at two points, was conducted in 2000 and 2014 Finding- Modeling results indicate that the model validation phase, has a good ability to predict land use change on the horizon is 14 years old (2028). The comparison between modeling map and map related to 2013 show that residential area and agricultural land continue to their own growth trend so that residential area will be increased from 3157 hectares in 2014 to 4180 hectares in 2028 and it has 2% growth that this level of growth has been 2% from 2000 to 2014. Results of this study can provide suitable perspective for planners to manage land use with regard to land use changes in past, present and future. They are also can be used for development assessment projects, the cumulative effects assessment, and the vulnerable and sensitive zone recognition

Major United States Land Use as Influenced by an Altering Climate: A Spatial Econometric Approach

Climate and socioeconomic and policy factors are found to stimulate land use changes along with changes in greenhouse gas emissions and adaption behaviors. Most of the studies investigating land use changes in the U.S. have not considered potential spatial effects explicitly. We used a two-step linearized multinomial logit to examine the impacts of various factors on conterminous U.S. land use changes including spatial lag coefficients. The estimation results show that the spatial dependences have existed for cropland, pastureland, and grasslands with a negative dependence on forests but weakened in most of the land uses except for croplands. Temperature and precipitation were found to have nonlinear impacts on the land use shares in the succeeding years by exerting opposite effects on crop versus pasture/grass shares. We also predicted land use changes under different climate change scenarios. The simulation results imply that the southern regions of the U.S. would lose cropland shares with further severity under the business-as-usual climate scenarios, while the land use shares for pasture/grass and forest would increase in those regions. As land use plays an important role in the climate system and vice versa, the results from this study may help policymakers tackle climate-driven land use changes and farmers adapt to climate change.

A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases

Land use as contained in geospatial databases constitutes an essential input for different applications such as urban management, regional planning and environmental monitoring. In this paper, a hierarchical deep learning framework is proposed to verify the land use information. For this purpose, a two-step strategy is applied. First, given high-resolution aerial images, the land cover information is determined. To achieve this, an encoder-decoder based convolutional neural network (CNN) is proposed. Second, the pixel-wise land cover information along with the aerial images serves as input for another CNN to classify land use. Because the object catalogue of geospatial databases is frequently constructed in a hierarchical manner, we propose a new CNN-based method aiming to predict land use in multiple levels hierarchically and simultaneously. A so called Joint Optimization (JO) is proposed where predictions are made by selecting the hierarchical tuple over all levels which has the maximum joint class scores, providing consistent results across the different levels.The conducted experiments show that the CNN relying on JO outperforms previous results, achieving an overall accuracy up to 92.5%. In addition to the individual experiments on two test sites, we investigate whether data showing different characteristics can improve the results of land cover and land use classification, when processed together. To do so, we combine the two datasets and undertake some additional experiments. The results show that adding more data helps both land cover and land use classification, especially the identification of underrepresented categories, despite their different characteristics.

Predicting Land Use Changes within the Tazekka Park and Its Borders via a Cellular Automata-Markov Modeling of Satellite Images

This article describes a new approach of land cover study to predicting and combatting deforestation based on satellite imagery as environmental statistics. Specifically, a stochastic mathematical cellular automata-Markov model was used to predict land-use changes in the Tazekka Park and its borders in TAZA province in Morocco. The model was used mainly to create thematic forecast maps. Through the proposed approach, we derived data and statistics covering the period 2000 to 2020 and then constructed a predictive map for the year 2040 using ArcGIS 10.4. The evaluation of our model’s effectiveness was confirmed by calculating the Markov transition matrix in the derivation of the final map. These results can improve the management of forest areas and serve as a reference in addressing the direct effects of forests on the environment.

Assessment of automated evapotranspiration estimates obtained using the GP-SEBAL algorithm for dry forest vegetation (Caatinga) and agricultural areas in the Brazilian semiarid region

Real evapotranspiration (ETr) plays a key role in water balance, especially for dry forest vegetation (Caatinga) and irrigated agricultural areas, as is the case in the Petrolina region located in the Brazilian semiarid region (BSAR). Population growth increases the need for food and the use of water resources, which are scarcer in semiarid regions. Thus, knowledge of the energy balance (EB) and radiative balance are indispensable for the management of water resources, especially in irrigated agricultural areas. Currently, one of the biggest challenges in the application of algorithms for estimating ETr based on satellite images is the calibration of the EB. The goal of this study was to evaluate the applicability of Geographic Resources Analysis Support System (GRASS) Python surface EB algorithm for land (GP-SEBAL) to calculate ETr for Caatinga vegetation and agricultural areas in the BSAR. GP-SEBAL was applied to two images from Landsat 8 obtained in 2013 and compared with manual SEBAL and observed data. In this study, kappa coefficient accuracy was used to compare the ground truth data and predicted land use and land cover. The performance of algorithms in determining the EB components, land-surface temperature (LST), net radiation (Rn), soil heat flux (G), latent heat flux (LE), sensible heat flux (H), and ETr were compared with statistical indices and uncertainty percentages (PUs) were calculated. In addition, a sensitivity analysis using a deviation of 30 W/m2 with an interval of 5 W/m2 was used to analyze the sensitivity of ETr. The results showed a strong correlation between GP-SEBAL and manual SEBAL for ETr, LST, Rn, LH, and H. The values suggest the regularity of estimating the EB in an automated manner. The results indicate a small PU difference in the estimated ETr values calculated using GP-SEBAL and SEBAL for two images of 10.87% and 20.11% for Caatinga vegetation and 15.01% and 35.16% for agriculture, respectively. We conclude that GP-SEBAL is an efficient automated algorithm for estimating ETr that uses a less complex process with a lower incidence of errors and considerably faster execution time than classical applications of SEBAL.

Land use change detection and prediction using Markov-CA and publishing on the web with platform map server, Case Study: Qom Metropolis, Iran

To achieve sustainable development, detailed planning, control and management of land cover changes that occur naturally or by human caused artificial factors, are essential. Urban managers and planners need a tool that represents them the information accurate, fast and in exact time. In this study, land use changes of 3 periods, 1994-2002, 2002-2009, 2009-2015 and predictions of 2009, 2015 and 2023 were assessed. In this paper, Maximum Likelihood method was used to classify the images, so that after evaluation of accuracy, amount of overall accuracy for images of 2013 was 85.55% and its Kappa coefficient was 80.03%. To predict land use changes, Markov-CA model was used after assessing the accuracy, and the amount of overall accuracy for 2009 was 82.57% and for 2015 was 93.865%. Then web GIS application was designed via map server application and evoked shape files through map file and open layers to browser environment and for design of appearance of website CSS, HTML and JavaScript languages were used. HTML is responsible for creating the foundation and overall structure of webpage but beautifying and layout design on CSS.

Current and Future Land Use Characters of a National Central City in Eco-Fragile Region—A Case Study in Xi’an City Based on FLUS Model

Land use change plays a key role in terrestrial systems and drives the process of ecological pattern change. It is important to investigate the process of land use change, predict land use patterns, and reveal the characteristics of land use dynamics. In this study, we adopted the Markov model and future land use (FLUS) model to predict the future land use conditions in Xi’an city. Furthermore, we investigated the characteristics of land use change from a novel perspective, i.e., via establishment of a complex network model. This model captured the characteristics of the land use system during different periods. The results indicated that urban expansion and cropland loss played an important role in land use pattern change. The future gravity center of urban development moved along the opposite direction to that from 2000 to 2015 in Xi’an city. Although the rate of urban expansion declined in the future, urban expansion remained the primary driver of land use change. The primary urban development directions were east-southeast (ENE), north-northeast (NNE) and west-southwest (WSW) from 1990 to 2000, 2000 to 2015, and 2015 to 2030, respectively. In fact, cropland played a vital role in land use dynamics regarding all land use types, and the stability of the land use system decreased in the future. Our study provides future land use patterns and a novel perspective to better understand land use change.

Predicting the surface urban heat island intensity of future urban green space development using a multi-scenario simulation

The distribution and morphology of urban green space (UGS) have a significant impact on its ability to mitigate surface urban heat island intensity (SUHII). In this study, the Future Land Use Simulation model was used to predict land use in Xuchang City by 2030 under three different scenarios. Landsat Operational Land Imager images from 2014 were used to calculate SUHII. And, the relationships and regression models between summer SUHII and land use data were analyzed and developed. The SUHII in 2030 based on simulated land use was predicted using regression models in 2014. Results showed that reductions in SUHII were greater under present land use scenario with a greater proportion of UGS. Furthermore, the degree of aggregation, average patch area, maximum patch shape index, and complexity of the green space also impacted the effectiveness of SUHII mitigation. In future land use scenarios with different constraints, the ecological service function guided scenario was the most effective at mitigating SUHII. These findings provide a reference for the reasonable allocation of urban land and the optimization of UGS in terms of SUHII reduction in small- and medium-sized cities where there is a need for expansion.

Land Use Change Detection and Prediction Using Markov-CA and Publishing on the Web with Platform Map Server, Case Study: Qom Metropolis, Iran

To achieve sustainable development, detailed planning, control and management of land cover changes that occurs naturally or by human caused artificial factors, is essential. Urban managers and planners need a tool that represents them the information accurate, fast and in exact time. In this study, land use changes of 3 periods 1994-2002, 2002-2009, 2009-2015 and predictions of 2009, 2015 and 2023 were assessed. In this paper, Maximum Likelihood method was used to classify the images, so that after evaluation of accuracy, amount of overall accuracy for images of 2013 was 85.55 % and its Kappa coefficient was 80.03%. To predict land use changes, Markov-CA model was used that after assessing the accuracy, the amount of overall accuracy for 2009 was 82.57% and for 2015 was 93.865%. ThenWebGis application was designed via map server application and evoked shape files through map file and open layers to browser environment and for design of appearance of website Css, HTML and JavaScript languages were used. HTML is responsible for creating the foundation and overall structure of webpage but beautify and layout design is on Css.

Predicted land use and land cover outlook for semi-arid Lokere and Lokok catchments in Karamoja region, Uganda

The semi-arid Lokere and Lokok catchments in northeastern Uganda are experiencing land use and land cover (LULC) change driven by policies and actions aimed at pastoralist sedentarisation. While these efforts present a trajectory of a landscape dominated by farming, livestock herding or grazing persists. The objective of this study was to project medium, and long-term LULC for Lokere and Lokok catchments in Karamoja, Uganda. We applied automatic multi-perceptron neural network, built on Markov chain modeling method, along with multi-criteria evaluation strategies; all embedded in the IDRISI Land Change Modeler (LCM) to project the catchments’ LULC to the year 2030 and 2050. The model was trained using 1994 and 2003 LULC, and validated with 2013 LULC. Results of three modelled policy scenarios; business as usual (BAU), pro-livestock and pro-farming; to the years 2030 and 2050 showed that small scale farming (SSF) would increase in all scenarios, even if policy shifts to promote livestock rearing. Pro-farming policies would, in both 2030 and 2050, result in reduction of grassland as SSF increases; doubling the 2003 land area by 2050. The results of this study facilitate assessment of potential impacts of the future LULC and policy evaluation in the catchments.