Scenario-based land cover change modeling and its implications for landscape pattern analysis in the Neka Watershed, Iran

Evaluation of mining landscape changes with development landscape metrics in the Ostrava-Karviná Mining District (Czech Republic)

The study presents an analysis of changes in the landscape of the Ostrava-Karviná Mining District (in the Czech Republic) covering the period of more than 170 years. In the area of interest affected by underground coal mining, both areas affected by changes and land cover preserving areas were identified in the study. A detailed assessment of the landscape changes was enabled by using landscape metrics and indices, namely the development index and total landscape change index. The underlying data were obtained from maps of stable cadastre (from the year 1836) and aerial images of the years 1947, 1971, and 2009. Visual photointerpretation of aerial images and interpretation of the maps of stable cadastre made it possible to create land cover maps according to CORINE Land Cover categories. Obtained information on the representation of individual land cover categories were used to identify and to analyze changes in the landscape affected by hard coal mining.

Prediction of the dynamics of land use land cover using a hybrid spatiotemporal model in Iran

Human activities are prone to be the main drivers of land use land cover (LULC) changes, which have cascading effects on the environment and ecosystem services. The main objective of this study is to assess the historical spatiotemporal distributions of LULC changes as well as estimated future scenarios for 2035 and 2045 by considering the explanatory variables of LULC changes in Zanjan province, Iran. The LULC time-series technique was applied using three Landsat images for the years 1987, 2002, and 2019. Multi-layer Perceptron Artificial Neural Network (MLP-ANN) is applied to model the relationships between LULC transitions and explanatory variables. Future land demand was calculated using a Markov chain matrix and multi-objective land optimization in a hybrid simulation model. Validation of the model's outcome was performed using the Figure of Merit index. The residential area in 1987 was 6406.02 ha which increased to 22,857.48 ha in 2019 with an average growth rate of 3.97%. Agriculture increased annually by 1.24% and expanded to 149% (890,433 ha) of the area occupied in 1987. Rangeland showed a decline concerning its area, with only about 77% (1,502,201 ha) of its area in 1987 (1,166,767 ha) remaining in 2019. Between 1987 and 2019, the significant net change was a conversion from rangeland to agricultural areas (298,511 ha). Water bodies were 8 ha in 1987, which increased to 1363 ha in 2019, with an annual growth rate of 15.9%. The projected LULC map shows the rangeland will further degrade from 52.43% in 2019 to 48.75% in 2045, while agricultural land and residential areas would be expanded to 940,754 ha and 34,727 ha in 2045 from 890,434 ha and 22,887 ha in 2019. The findings of this study provide useful information for the development of an effective plan for the study area.

Application of Integrated Watershed Management Measures to Minimize the Land Use Change Impacts

Non-point source pollution is a major factor in excessive nutrient pollution that can result in the eutrophication. Land use/land cover (LULC) change, as a result of urbanization and agricultural intensification (e.g., increase in the consumption of fertilizers), can intensify this pollution. An informed LULC planning needs to consider the negative impacts of such anthropogenic activities to minimize the impact on water resources. The objective of this study was to inform future land use planning by considering nutrient reduction goals. We modeled the LULC dynamics and determined the capacity for future agricultural development by considering its impacts on nitrate runoff at a watershed scale in the Tajan River Watershed in northeastern Iran. We used the Soil and Water Assessment Tool (SWAT) to simulate the in-stream nitrate concentration on a monthly timescale in this watershed. Historical LULCs (years 1984, 2001 and 2010) were derived via remote sensing and were applied within the Land Change Modeler to project future LULC in 2040 under a business-as-usual scenario. To reduce nitrate pollution in the watershed and ecological protection, a conservation scenario was developed using a multi-criteria evaluation method. The results indicated that the implementation of the conservation scenario can substantially reduce the nitrate runoff (up to 72%) compared to the business-as-usual scenario. These results can potentially inform regional policy makers in strategic LULC planning and minimizing the impact of nitrate pollution on watersheds. The proposed approach can be used in other watersheds for informed land use planning by considering nutrient reduction goals.

Responses of surface water quality to future land cover and climate changes in the Neka River basin, Northern Iran

The spatial and temporal dimensions of environmental impacts of climate and land cover changes are two significant factors altering hydrological processes. Studying the effects of these factors on water quality, provides important insight for water resource management and optimizing land planning given increasing water scarcity and water pollution. The impact of land cover and climate changes on surface water quality was assessed for the Neka River basin in Northern Iran. The widely used Soil and Water Assessment Tool (SWAT) was applied for pollutant modeling, and was calibrated using the Sequential Uncertainty Fitting (SUFI-2) algorithm. An ensemble of 17 CMIP5 climate models under two IPCC greenhouse gas emission scenarios were selected, and future land cover change (LCC) was modeled based on the evolution that occurred in the last decades. We simulated the impacts of climate change (CC) and LCC on sediment, nitrate, and phosphate for the 2035-2065 time slice. The annual loads of sediment, phosphate, and nitrate are projected to decrease under both CC scenarios based on the inter-model average, and generally follow a pattern similar to the change in river discharge. Nitrate concentrations show an increase across all seasons, while the sediment and phosphate concentrations increase in winter and autumn under CC conditions. Results indicate that pollutants are expected to increase under LCC alone, mainly due to the expansion of the cultivated areas. Overall, it seems CC has a greater impact than LCC on the variation of water quality variables in the Neka River basin. With a combined change in climate and land cover, the annual nitrate concentrations are expected to increase by + 19.7% and + 17.9%, under RCP 4.5 and RCP 8.5, respectively. The combined impacts of the CC and LCC caused a decline in the annual sediment and phosphate concentrations by -10.1% and -2.2% under RCP 4.5 and -9%, and -3.2% under RCP 8.5, respectively.

From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

It is essential to produce land cover maps and land use maps separately for different purposes. This study was conducted to generate such maps in Binh Duong province, Vietnam, using a novel combination of pixel-based and object-based classification techniques and geographic information system (GIS) analysis on multi-temporal Landsat images. Firstly, the connection between land cover and land use was identified; thereafter, the land cover map and land use function regions were extracted with a random forest classifier. Finally, a land use map was generated by combining the land cover map and the land use function regions in a set of decision rules. The results showed that land cover and land use were linked by spectral, spatial, and temporal characteristics, and this helped effectively convert the land cover map into a land use map. The final land cover map attained an overall accuracy (OA) = 93.86%, with producer’s accuracy (PA) and user’s accuracy (UA) of its classes ranging from 73.91% to 100%. Meanwhile, the final land use map achieved OA = 93.45%, and the UA and PA ranged from 84% to 100%. The study demonstrated that it is possible to create high-accuracy maps based entirely on free multi-temporal satellite imagery that promote the reproducibility and proactivity of the research as well as cost-efficiency and time savings.

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

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

Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)

Monitoring land use/land cover (LULC) change dynamics plays a crucial role in formulating strategies and policies for the effective planning and sustainable development of rapidly growing cities. Therefore, this study sought to integrate the cellular automata and Markov chain model using remotely sensed data and geographical information system (GIS) techniques to monitor, map, and detect the spatio-temporal LULC change in Zaria city, Nigeria. Multi-temporal satellite images of 1990, 2005, and 2020 were pre-processed, geo-referenced, and mapped using the supervised maximum likelihood classification to examine the city’s historical land cover (1990–2020). Subsequently, an integrated cellular automata (CA)–Markov model was utilized to model, validate, and simulate the future LULC scenario using the land change modeler (LCM) of IDRISI-TerrSet software. The change detection results revealed an expansion in built-up areas and vegetation of 65.88% and 28.95%, respectively, resulting in barren land losing 63.06% over the last three decades. The predicted LULC maps of 2035 and 2050 indicate that these patterns of barren land changing into built-up areas and vegetation will continue over the next 30 years due to urban growth, reforestation, and development of agricultural activities. These results establish past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: integration of remote sensing, CA-Markov, and landscape metrics

In the present paper, land use/land cover (LULC) change was predicted in the Greater Isfahan area (GIA), central Iran. The GIA has been growing rapidly in recent years, and attempts to simulate its spatial expansion would be essential to make appropriate decisions in LULC management plans and achieve sustainable development. Several modeling tools were employed to outline sustainable scenarios for future dynamics of LULCs in the region. Specifically, we explored past LULC changes in the study area from 1996 to 2018 and predicted its future changes for 2030 and 2050. For this purpose, we performed object-oriented and decision tree techniques on Landsat and Sentinel-2 satellite images. The CA-Markov hybrid model was utilized to analyze past trends and predict future LULC changes. LULC changes were quantitatively measured using landscape metrics. According to the results, the majority of changes were related to increasing residential areas and decreasing irrigated lands. The results indicated that residential lands would grow from 27,886.87 ha to 67,093.62 ha over1996-2050 while irrigated lands decrease from 99,799.4 ha to 50,082.16 ha during the same period of time. The confusion matrix of the 2018 LULC map was built using a total of 525 ground truth points and yielded a Kappa coefficient and overall accuracy of 78% and 82%, respectively. Moreover, the confusion matrix constructed base on the Sentinel-2 map, as a reference, to judge the predicted 2018 LULC map with a Kappa coefficient of 88%. The results of this study provide useful insights for sustainable land management. The results of this research also proved the promising capability of remote sensing algorithms, CA-Markov model and landscape metrics future LULC planning in the study area.

A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models

Due to the increase in future uncertainty caused by rapid environmental, societal, and technological change, exploring multiple scenarios has become increasingly important in urban planning. Land Change Modeling (LCM) enables planners to have the ability to mold uncertain future land changes into more determined conditions via scenarios. This paper reviews the literature on urban LCM and identifies driving factors, scenario themes/types, and topics. The results show that: (1) in total, 113 driving factors have been used in previous LCM studies including natural, built environment, and socio-economic factors, and this number ranges from three to twenty-one variables per model; (2) typical scenario themes include “environmental protection” and “compact development”; and (3) LCM topics are primarily growth prediction and prediction tools, and the rest are growth-related impact studies. The nature and number of driving factors vary across models and sites, and drivers are heavily determined by both urban context and theoretical framework.

Landscape Management through Change Processes Monitoring in Iran

The presented research investigated and predicted landscape change processes (LCPs) in the Talar watershed, northern Iran. The Land Change Modeler was used for change analysis, transition potential modeling, and prediction of land use/land cover (LULC) map. The evaluation of projected LULC map was performed by comparing the real and predicted LULC maps for the reference year, 2014. Landscape metrics and change processes were investigated for the period 1989–2014 and for exploring the situation in 2030. Results illustrated that the increase in agricultural land and residential areas took place at the expense of forest and rangeland. The distance from forests was the most sensitive parameter for modeling the transition potentials. The modelling of the LULC change projected the number of patches, the landscape shape index, interspersion and juxtaposition index, and edge density, Euclidean nearest-neighbor distance, and area-weighted shape index will amount to 65.3, 7.63, 20.1, 8.77, −1.35, and 0.61% as compared to 2014, respectively. Our findings indicated that the type of change processes that occurred was not entirely the same in 1989–2000 and 2000–2014. In addition, change processes in the creation of dry farming, orchard, and residential classes, attrition of forest and rangeland categories, and dissection in irrigated farming are projected. The dynamics of landscape metrics and change processes combined in one analytical framework can facilitate understanding and detection of the relationship between ecological processes and landscape pattern. The finding of current research will provide a roadmap for improved LULC management and planning in the Talar watershed, southern coast of the Caspian Sea.

Land Use Change Detection and Prediction in Upper Siem Reap River, Cambodia

Siem Reap River has played a crucial role in maintaining the Angkor temple complex and livelihood of the people in the basin since the 12th century. Land use in this watershed has changed considerably over the last few decades, which is thought to have had an influence on river. This study was carried out as part of assessing the land use and climate change on hydrology of the upper Siem Reap River. The objective was to reconstruct patterns of annual deforestation from 1988 to 2018 and to explore scenarios of land use 40 and 80 years into the future. A supervised maximum likelihood classification was applied to investigate forest cover change in the last three decades. Multi-layer perceptron neural network-Markov chain (MLPNN-MC) was used to forecast land use and land cover (LULC) change for the years 2058 and 2098. The results show that there has been a significantly decreasing trend in forest cover at the rate 1.22% over the last three decades, and there would be a continuous upward trend of deforestation and downward trend of forest cover in the future. This study emphasizes the impacts of land use change on water supply for the Angkor temple complex (World Heritage Site) and the surrounding population.