CA MARKOV MODELING OF LAND USE LAND COVER DYNAMICS AND SENSITIVITY ANALYSIS TO IDENTIFY SENSITIVE PARAMETER(S)

Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. Land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.

The study aimed to map and predict land use and land cover change dynamics in a human-wildlife mediated landscape of Save Valley Conservancy (SVC), south-eastern lowveld of Zimbabwe. In April 2018, remote sensing was used to quantify land use and land cover changes between 1990 and 2015 based on satellite images acquired from North Atlantic Space Agency (LANDSAT TM, path 170 raw 73). Household surveys were administered using a structured questionnaire to 100 communal settlers to collect biophysical/socioecological data on explanatory variables which included; density of human and livestock, fuelwood consumption and area under cultivation. The study used multi-criteria evaluation procedure based on dynamic adjustments of socio-ecological data to generate transitional probability maps of SVC. Thresholds of socio-ecological data were then computed in the Markov-cellular automata model through a multi-objective land allocation procedure and a cellular automata spatial filter in order to simulate future land use and land cover maps of 2020, 2030 and 2040 for SVC. If the Zimbabwe land reform and its agricultural resettlement program is not properly coordinated and planned in the study area, the study predicted a continuing downward trend in woodland cover category and a significant upward trend of land under agriculture. For the period 1990 to the 2040s, the woodland cover is likely to decrease by 46.7% changing into agricultural land use and/or bare land in SVC. Future land use and land cover simulations indicated that if the current land use and land cover trends continue unabated across the study area without a holistic sustainable agricultural land-wildlife management plan and community development measures, severe woodland degradation will occur. The study recommends ecological restoration and/or re-planning on agriculture-wildlife land use and zonation, delineating agriculture and human settlement in the southern part of the conservancy while the northern part of SVC is exclusively reserved and protected for wildlife management. Key words: Land use, land cover, human encroachment, protected area, savanna

An attempt has been made to explore and evaluate the Cellular Automata (CA) Markov modelling to monitor and predict the future land use and land cover (LULC) scenario in a part of Brahmaputra River basin using LULC maps derived from multi-temporal satellite images. CA Markov is a combined cellular automata/Markov chain/multi-criteria/multi-objective land allocation (MOLA) LULC prediction procedure that adds an element of spatial contiguity as well as knowledge base of the likely spatial distribution of transitions to Markov chain analysis. Evidence likelihood map was used for as knowledge base of the likely spatial procedure in CA Markov model. The predicting quantity and predicting location change have been analysed and statistically evaluated. The validation statistics indicated how well the comparison map agreed and disagreed with the reference map. Predicted results accuracy is slightly higher when compare to others studies of LULC change using CA Markov approaches.

Land use is a process that turns a piece of land’s natural ecosystem into an artificial one. The mix of plant and man-made covers on the Earth’s surface is known as land cover. Land use is the primary external force behind change in land cover, and land cover has an impact on how land use is carried out, resulting in a synergistic interaction between the two at the Earth’s surface. In China’s Shandong Peninsula city cluster, Dongying is a significant coastal port city. It serves as the administrative hub for the Yellow River Delta and is situated in Shandong Province, China’s northeast. The changes in its urban land use and land cover in the future are crucial to understanding. This research suggests a prediction approach that combines a patch-generation land use simulation (PLUS) model and long-term short-term memory (LSTM) deep learning algorithm to increase the accuracy of predictions of future land use and land cover. The effectiveness of the new method is demonstrated by the fact that the average inaccuracy of simulating any sort of land use in 2020 is around 5.34%. From 2020 to 2030, 361.41 km2 of construction land is converted to cropland, and 424.11 km2 of cropland is converted to water. The conversion areas between water and unused land and cropland are 211.47 km2 and 148.42 km2, respectively. The area of construction land and cropland will decrease by 8.38% and 3.64%, respectively, while the area of unused land, water, and grassland will increase by 5.53%, 2.44%, and 0.78%, respectively.

Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results

The Markov-cellular automata is suitable to study complex spatial-temporal geographic system, especially for regional land use, and it has been an important tool and research focus for regional land use change modeling. Previous researchers focused on a few kind of land use type at the regional scale and the data resolution was cursory because land use maps were usually derived from TM image. Few researchers involved precise scale of land use change within a region. To solve this problem, we took the data of land-use survey as a data source maps that include detailed multiple land use types. The case study area was Changping District, which is a rapidly growing area of Beijing. We select the land use map of 2001 and 2005 which include the multiple land use types as data source to simulate the land use of 2012. The results of simulation show that simulation accuracy of multiple land use types is better than them of cursory scale land use types, although it takes a substantial amount of time to run. The statistical result derived from Moran's I and fractal parameter indicates that simulation shows the high spatial stability. The simulation results showed that the number of cropland is keeping on decrease from 2005 to 2012 without the holistic sustainable development measures and severe land policy. This paper represents a good try to local land use change modeling as shown combined Markov chain analysis and cellular automata models. The simulated future land use changes have significant environmental and socioeconomic implications for sustainable region land detailed planning in the study area.

Increased anthropogenic activities in the Little Ruaha River Catchment have modulated the catchment condition, nevertheless, the future changes as a result of increased anthropogenic activities are unknown. Understanding the future changes is vitally important for the design of appropriate strategies towards sustainable management of the catchment resources. This study applied Remote Sensing and GIS techniques (Jensen & Lulla, 1987) to assess the historical long-term changes in land use and land cover using Landsat satellite images of 1990, 2005 and 2015, and modelled the future change in land use and land cover up to 2040 using the stochastic CA-Markov chain (Almeida et al., 2005). The historical land use and land cover change detection results indicate that between 1990 and 2005 the area under forest changed from 39,872 ha to 22,957 ha, woodland changed from 109,692 ha to 72,809 ha, wetland decreased from 19,157 ha to 11,785 ha, the cultivated land increased from 106,782 ha to 109,047 ha, likewise, the built-up area increased from 9408 ha to 11,674 ha. Results between 2005 and 2015 show the substantial changes where the forest decline from 22,957 ha to 15,950 ha, woodland decreased from 72,809 ha to 58,554 ha and the wetland changed from 11,785 ha to 5622 ha. Cultivated land and built up area increased from 109,047 ha and 11,674 ha to 143,468 ha and 13,765 ha respectively. Generally, the study has revealed the substantial decline in forest, woodland and wetland by 23,922 ha, 51,138 ha and 13,535 ha respectively, and an increase of cultivated land and built up area by 36,668 ha and 4357 ha respectively in 15 years, between 1990 and 2015. The predicted future land use and cover for the next 15 years (2040) showed an overall increase in cultivated land, built up area, grassland and bushland to 24.82%, 2.24%, 25.18% and 20.41% respectively, and a decrease in forest, woodland and wetland in the order of 1.87%, 7.87% and 0.03% respectively. The study concludes that, there have been significant changes in land use and cover in the catchment which likely to impend the sustainability of the catchment productivity, hence recommends the holistic system thinking and analysis approach in management and utilization of catchment resources.

<p>Large industrial pollution, agricultural runoff, and disposal of untreated sewage into the river have made Kanpur the most critical water pollution hotspot of Ganga River. This study assesses the risk of nutrient pollution and resulting eutrophication in this industrialized stretch passing through Kanpur for the mid-21st century under climate change and land use land cover projections. For this assessment, climate projections from an ensemble of 20 GCMs for the RCP 4.5 and RCP 8.5 scenarios, and future land use land cover (LULC) projections from a multi-layer perceptron neural network are used to drive a hydrological model HEC-HMS which is coupled to the water quality model QUAL2K. The nutrients assessed are ammonia, nitrate, total nitrogen, organic-, inorganic- and total phosphorous. An increase in nutrient pollution is simulated for future climate change due to a reduction in dilution volume caused by reduced low flows. An increase in nutrient pollution is also simulated for future land use land cover because of an increase in pollution from agricultural runoff. Both nitrogen and phosphorous components are highly sensitive to climate change, while only phosphorous components are highly susceptible to land use land cover. This is because, the major contribution of phosphorous pollution in this stretch is from agricultural runoff and only a negligible contribution is from point sources. The risk of nitrate pollution decreases and ammonia pollution increases with future climate change due to higher denitrification rate with warming, but the risk of total phosphorous pollution slightly decreases due to an overall reduction in phosphorous with warming following an overall increase in mean streamflow. A shift in the hotspot of eutrophication from Kanpur to Jajmau is also simulated due to limiting phosphorous eutrophication for future climate at Kanpur. The risk of eutrophication would increase with future climate change due to increased total nitrogen and total phosphorous with warming, and the risk is likely to become higher for the combined climate change and land use land cover projections. The results of this ongoing study will be presented in the meeting. Our study would be highly beneficial for the policymakers to save the Ganga River from further pollution in the future.</p>

Land use changes can majorly affect many parameters that are directly or indirectly interlinked to various human-environmental systems, including hydrological processes and flood risks. The knowledge of future land cover changes is crucial for better managing human-environmental interactions and addressing potential environmental challenges, such as floods. In this work, the impact of future land cover changes in flood inundation is assessed, using a case study in northeast Indiana, US. A Cellular Automata Markov (CAM) model is applied, combining Geographic Information Systems (GIS) and Python, to predict land changes and provide future land cover maps, along with statistical validation measures. The land use map outputs are then used in a HEC-RAS hydraulic model, to test the different flooding impacts under a design storm, using the rain-on-grid routine. The results indicate that even slightly more urbanized and deforested areas can increase the potential flood extent. Furthermore, the impacts of these forecasted land cover changes are quantified in monetary terms, based on a spatial Ecosystem Services Valuation (ESV) model. The findings indicate that as certain land uses (mainly wetlands, followed by forests) give their place to build-up areas, barren land, or even agricultural lands, the ‘lost’ value due can reach 1.5 million USD in 2051. The novelty of this study lies in int integrated character, combining for the first time to our knowledge land cover forecast with hydrologic-hydraulic modelling and spatial ESV, showing thus the future changes, risks, and potential economic losses, respectively. This application uses the minimum necessary input data to perform the analyses, and all data and codes are publicly available, contributing thus to the transferability and reproducibility of the approach.

Land use changes can majorly affect many parameters that are directly or indirectly interlinked with various human-environmental systems, including hydrological processes and flood risks. The knowledge of future land cover changes is crucial for better managing human-environmental interactions and addressing potential environmental challenges, such as floods. In this work, the impact of future land cover changes on flood inundation is assessed, using a case study in northeast Indiana, US. A Cellular Automata Markov (CAM) model is applied, combining Geographic Information Systems (GIS) and Python, to predict land changes and provide future land cover maps, along with statistical validation measures. The land use map outputs are then used in a HEC-RAS hydraulic model to test the different flooding impacts under a design storm, using the rain-on-grid routine. The results indicate that even slightly more urbanized and deforested areas can increase the potential flood extent. Furthermore, the impacts of these forecasted land cover changes are quantified in monetary terms, based on a spatial Ecosystem Services Valuation (ESV) model. The findings indicate that as certain land uses (mainly wetlands, followed by forests) give their place to build-up areas, barren land, or even agricultural lands, the ‘lost’ value can reach 1.5 million USD in 2051. The novelty of this study lies in its integrated character, combining for the first time to our knowledge land cover forecast with hydrologic-hydraulic modelling and spatial ESV, thus showing the future changes, risks, and potential economic losses, respectively. This application uses the minimum necessary input data to perform the analyses, and all data and codes are publicly available, contributing thus to the transferability and reproducibility of the approach.

Abstract. The effects of future land use and land cover change on the chemical composition of the atmosphere and air quality are largely unknown. To investigate the potential effects associated with future changes in vegetation driven by atmospheric CO2 concentrations, climate, and anthropogenic land use over the 21st century, we performed a series of model experiments combining a general circulation model with a dynamic global vegetation model and an atmospheric chemical-transport model. Our results indicate that climate- and CO2-induced changes in vegetation composition and density between 2100 and 2000 could lead to decreases in summer afternoon surface ozone of up to 10 ppb over large areas of the northern mid-latitudes. This is largely driven by the substantial increases in ozone dry deposition associated with increases in vegetation density in a warmer climate with higher atmospheric CO2 abundance. Climate-driven vegetation changes over the period 2000–2100 lead to general increases in isoprene emissions, globally by 15% in 2050 and 36% in 2100. These increases in isoprene emissions result in decreases in surface ozone concentrations where the NOx levels are low, such as in remote tropical rainforests. However, over polluted regions, such as the northeastern United States, ozone concentrations are calculated to increase with higher isoprene emissions in the future. Increases in biogenic emissions also lead to higher concentrations of secondary organic aerosols, which increase globally by 10% in 2050 and 20% in 2100. Summertime surface concentrations of secondary organic aerosols are calculated to increase by up to 1 μg m−3 and double for large areas in Eurasia over the period of 2000–2100. When we use a scenario of future anthropogenic land use change, we find less increase in global isoprene emissions due to replacement of higher-emitting forests by lower-emitting cropland. The global atmospheric burden of secondary organic aerosols changes little by 2100 when we account for future land use change, but both secondary organic aerosols and ozone show large regional changes at the surface.

To demonstrate potential future consequences of land cover and land use changes beyond those for physical climate and the carbon cycle, we present an analysis of large‐scale impacts of land cover and land use changes on atmospheric chemistry using the chemistry‐climate model EMAC (ECHAM5/MESSy Atmospheric Chemistry) constrained with present‐day and 2050 land cover, land use, and anthropogenic emissions scenarios. Future land use and land cover changes are expected to result in an increase in global annual soil NO emissions by ∼1.2 TgN yr−1 (9%), whereas isoprene emissions decrease by ∼50 TgC yr−1 (−12%). The analysis shows increases in simulated boundary layer ozone mixing ratios up to ∼9 ppbv and more than a doubling in hydroxyl radical concentrations over deforested areas in Africa. Small changes in global atmosphere‐biosphere fluxes of NOx and ozone point to compensating effects. Decreases in soil NO emissions in deforested regions are counteracted by a larger canopy release of NOx caused by reduced foliage uptake. Despite this decrease in foliage uptake, the ozone deposition flux does not decrease since surface layer mixing ratios increase because of a reduced oxidation of isoprene by ozone. Our study indicates that the simulated impact of land cover and land use changes on atmospheric chemistry depends on a consistent representation of emissions, deposition, and canopy interactions and their dependence on meteorological, hydrological, and biological drivers to account for these compensating effects. It results in negligible changes in the atmospheric oxidizing capacity and, consequently, in the lifetime of methane. Conversely, we expect a pronounced increase in oxidizing capacity as a consequence of anthropogenic emission increases.

<p>The Alps are experiencing large climatic and socio-economic changes. Climate change is leading to an above-average increase in temperatures and subsequent changes in the timing and duration of snow cover. In parallel, socio-economic changes are affecting land use in the Alpine region. Both, snow cover duration/timing and land use changes directly affect the surface albedo of this landscape and therefore the energy balance of this region. Globally, changes in surface albedo due to land use changes and changes in snow/ice cover affect surface albedo, and thus radiative forcing, in opposite directions.<br>In this study, we investigated the impact of four different future land use scenarios, 12 future snow cover scenarios on the surface albedo in the alpine region of South Tyrol (Italy) in the year 2100 compared to conditions in 2010. Both, the individual effects of changes in land use and future snow cover patterns were investigated, as well as the interactive effects of these two processes.<br>The hypothetical changes in albedo until 2100 associated with changes in land and/or snow cover were assessed by establishing a surface albedo model based on remotely sensed albedo (MODIS MCD43A1), snow cover data (MODIS MOD10A1), land cover data, as well as geographical information (ASTER ASTGTM).  Potential future land covers were developed on the basis of likely socio-economic pathways and their spatial distribution was mapped. Snow cover scenarios for 2100 are based on EURO CORDEX RCP 2.6 and 8.5 climate scenarios.<br>Snow cover was by far the most important predictor for albedo, followed by the occurrence of needle leaf forests using a regression tree algorithm. This algorithm exhibited excellent skill in modelling current albedo conditions based on the above-mentioned predictors.<br>Likely future snow cover conditions lead to a decrease in average albedo, the magnitude of which depended on the chosen RCP and combination of global/regional climate model. Likely future land cover scenarios caused changes in spatially averaged albedo of the study domain in the same order of magnitude like the RCP 2.6 snow cover scenarios. Simulations with factorial combinations of land cover and snow cover scenarios showed the compounding effect of these two processes. </p><p> </p><p> </p>

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.

The growing human population accelerates alterations in land use and land cover (LULC) over time, putting tremendous strain on natural resources. Rapid land use transformations, encompassing urbanization, intensive agriculture, and changes in natural landscapes, have a profound impact on water cycle. This necessitates the development and implementation of sustainable land management strategies to mitigate adverse effects on water resources. Anticipating future land use and cover (LU&LC) dynamics in the Munneru river basin is pivotal for modelling of hydrological processes. This study delves into the combined impact of Land Use and Land Cover Scenarios (LU&LC) which is based on Shared Socioeconomic Pathway (SSP2-45, SSP3-75 and SSP5-85) and climate change within the context of representative concentration pathway (RCP 4.5 & RCP 8.5) scenarios on water resources for Munneru river basin, India. Landsat data was employed for preparing LU&LC maps from the Google Earth Engine (GEE) using the random forest (RF) method for the period 2005-2020 with the accuracy of 91% and kappa coefficient of 0.89. The future scenarios of LU&LC’s were projected by integrating Global Change Assessment Model (GCAM) data and DynaCLUE model for 2030, 2050 and 2080. DynaCLUE model uses driving factors, Binary Logistic Regression analysis for past LU&LC maps for projecting future LU&LC maps. The SWAT model is calibrated and validated for the period 1983–2017 in SWAT-CUP using the SUFI2 algorithm for 2015 LU&LC map. The future projected LU&LC maps based on SSP’s are incorporated in SWAT model for future periods under both RCP 4.5 & 8.5 scenarios. The average monthly streamflow’s are simulated for the baseline period (1983–2005) and for three future periods, namely the near future (2021–2039), mid future (2040–2069) and far future (2070–2099) under both LU&LC and climate change scenarios. Results indicate that there is increase in surface runoff and water yield and decrease in evapotranspiration, groundwater and total aquifer for three SSP scenarios under both RCP’s. Assessing the impact on water balance components, provides the necessity for adaptive strategies in the face of shifting climate and land use dynamics.