Land Change Modeler Research Articles

Benefits of a multiple‐solution approach in land change models

Benefits of a multiple‐solution approach in land change models

Land change detection and effective factors on forest land use changes: application of land change modeler and multiple linear regression

Reducing forest covered areas and changing it to pasture, agricultural, urban and rural areas is performed every year and this causes great damages in natural resources in a wide range. In order to identify the effective factors on reducing the forest cover area, multiple regression was used from 1995 to 2015 in Mazandaran forests. A Multiple regressions can link the decline in forest cover (dependent variable) and its effective factors (independent variable) are well explained. In this study, Landsat TM data of 1995 and Landsat ETM+ data of 2015 were analyzed and classified in order to investigate the changes in the forest area. The images were classified in two classes of forest and non-forest areas and also forest map with spatial variables of physiography and human were analyzed by regression equation. Detection satellite images showed that during the studied period there was found a reduction of forest areas up to approximately 257331 ha. The results of regression analysis indicated that the linear combination of income per capita, rain and temperature with determined coefficient 0.4 as independent variables were capable of estimating the reduction of forest area. The results of this study can be used as an efficient tool to manage and improve forests regarding physiographical and human characteristics. Keywords: Land change Modeler, Multiple linear regression, remote sensing, Mazandaran forests

Development of land-use scenarios using vegetation inventories in Japan

Changes in land use and land cover (LULC) have major effects on biodiversity and ecosystem services. Land change models can simulate future trends of ecosystem services under different scenarios to inform the actions of decision makers towards building a more sustainable society. LULC data are essential inputs for predicting future land changes. It is now possible to derive high-resolution LULC maps from satellite data using remote sensing techniques. However, the classification of land categories in these maps is too limited to sufficiently assess biodiversity and ecosystem services. This study aims to develop land-use scenarios, using an appropriate LULC map, to enable assessment of biodiversity and ecosystem services at the national scale. First, we developed an LULC dataset using vegetation inventories based on field records of vegetation collected throughout the country in the periods 1978–1987, 1988–1998 and 1999–2014. The vegetation maps consist of over 905 vegetation categories, from which we aggregated the most prevalent categories into 9 LULC categories. Second, we created a business-as-usual scenario and plausible future scenarios on the land use change maps using the Land Change Model tool. In the process of developing the model, we considered key drivers including biophysical and socio-economic factors. The results showed some key land changes as consequences of intensive/extensive land-use interventions. These derived scenario maps can be used to assess the impacts of future land change on biodiversity and ecosystem services.

Integrating strategic land use planning in the construction of future land use scenarios and its performance: The Maipo River Basin, Chile

The integration of urban, rural, and environmental systems is fundamental for resource management. Strategic land use planning attempts to balance these systems to reach sustainability by incorporating future scenario analyses. However, long-term implications of these strategic land use plans are not easily understood, nor spatially visualized, due to their multidimensional effects. This paper illustrates the integration of political conditionings obtained from a strategic land use plan, into a land change model (LCM) to assess long-term policy impacts using landscape metrics. Guided by participatory instances with a group of public, private, and civil stakeholders called the Scenario Building Team (SBT), the Dyna-CLUE LCM was implemented. Two policy frameworks (a trending business-as-usual and a strategic land use plan) were assessed under high and low urban demands up to year 2050 for the Maipo River basin, where the capital of Chile is located. The participatory approach proved to be a useful tool for scenario development and assessment, as the SBT provided valuable information and result’s feasibility analysis. Under all scenarios, peri-urban municipalities would concentrate most urban growth, and those located in the North-West part of the capital would face the largest urban expansion. However, strategic planning implementation would restrict urban areas within the actual urban limits enhancing small town growth. Agricultural areas remain in the best places for production, but still are the greatest contributors to urban expansion. The strategic plan is less effective in diminishing flood risk zones or protecting ecosystems that support the SBT’s perception about the relevance of stronger environmental legislation in the region.

Recent land cover change after the Kyrill windstorm in the Šumava NP

Mountain forests in Central Europe are mostly disturbed by windstorms and subsequently by insect outbreaks. Disturbances play a key role in forest composition, structure, ecosystem functioning or species richness and consequently in general land cover changes. The Šumava National Park was affected by a windstorm known as Kyrill in January 2007. This event initiated dynamic period of large scale land cover changes from 2006 to 2012. The main goal of this study is to analyse and quantify the development of land cover changes caused by the disturbance with the use of detailed airborne data. We recorded a spatial-temporal classification of changes by so-called land cover flows, which detect changes among defined land cover types.Layers of land cover for each year were collected in GIS software (ArcGIS 10.3) and they were used as inputs for the Land Change Modeler for ArcGIS software. The results show relatively small direct effects of the Kyrill windstorm, windfalls subsequently (in the year 2007) increased their proportion by 4.73 km2, e.g. 0.69% of the Šumava National Park area in 2007. Area of coniferous forest decreased from 56.55% to 48.27% (between 2006 and 2012) of the National Park area and from 2008 this decrease was the dominant change. In neighbourhood of windfalls, new dead-standing forest in 2008 originated. Differences in management in particular zones of the national park resulted in an increase of clear-cuts and clear-cuts with dead wood in the intervention zone. Coniferous forest was widely changed into dead-standing forest in non-intervention areas. This pattern of change was the most intensive between 2008 and 2011. Changes were spatially clustered and dead-standing forest work as a main driver of subsequent dynamics. During the bark beetle infestation period (2008–2011), more than 80% of new dead-standing forest occur within 100 m distance from patches of dead-standing forest from previous year. During the last period (2011–2012) these processes slowed down and large-scale regeneration took place within the focus area.

Prediction of land use changes based on Land Change Modeler and attribution of changes in the water balance of Ganga basin to land use change using the SWAT model

Conflicts between increasing irrigated agricultural area, commercial crops, shifting cultivation and ever increasing domestic and industrial demand has already been a cause of tension in the society over water in the Ganga River Basin, India. For the development of sustainable water resource strategies, it is essential to establish interaction between landuse changes and local hydrology through proper assessment. Precisely, seeing how change in each LULC affects hydrologic regimes, or conversely evaluating which LULC shall be appropriate for the local hydrological regime can help decision makers to incorporate in the policy instruments. In this study, hydrologic regimes of the Ganga River basin have been assessed with landuse change. Catchment hydrologic responses were simulated using Soil and Water Assessment Tool (SWAT). Meteorological data from IMD of 0.25° × 0.25° spatial resolution were taken as the climate inputs. Simulated stream flow was compared at different gauge stations distributed across the Gang River and its tributaries. Urbanization has been the topmost contributor to the increase in surface runoff and water yield. While increased irrigation demands were the dominant contributor to the water consumption and also added to the increased evapotranspiration. This study can be important tool in quantifying the changes in hydrological components in response to changes made in landuse in especially basins undergoing rapid commercialization. This shall provide substantive information to the decision makers required to develop ameliorative strategies.

Future land cover change scenarios in South African grasslands – implications of altered biophysical drivers on land management

Future land cover changes may result in adjustments to biophysical drivers impacting on net ecosystem carbon exchange (NEE), catchment water use through evapotranspiration (ET), and the surface energy balance through a change in albedo. The Land Change Modeller (Idrisi Terrset 18.08) and land cover for 2000 and 2014 are used to create a future scenario of land cover for two catchment with different land management systems in the Eastern Cape Province for the year 2030. In the S50E catchment, a dualistic farming system, the trend shows that grasslands represented 57% of the total catchment area in 2014 decreasing to 52% by 2030 with losses likely to favour a gain in woody plants and cultivated land. In T35B, a commercial system, persistence of grasslands is modelled with approximately 80% coverage in both years, representing a more stable system. Finally, for S50E, NEE and ET will increase under this land cover change scenario leading to increased carbon sequestration but less water availability and corresponding surface temperature increases. This implies that rehabilitation and land management initiatives should be targeted in catchments under a dualistic farming system, rather than those which are predominantly commercial systems.

Performance comparison of land change modeling techniques for land use projection of arid watersheds

The change of land use/land cover has been known as an imperative force in environmental alteration, especially in arid and semi-arid areas. This research was mainly aimed to assess the validity of two major types of land change modeling techniques via a three dimensional approach in Birjand urban watershed located in an arid climatic region of Iran. Thus, a Markovian approach based on two suitability and transition potential mappers, i.e. fuzzy analytic hierarchy process and artificial neural network-multi layer perceptron was used to simulate land use map. Validation metrics, quantity disagreement, allocation disagreement and figure of merit in a three-dimensional space were used to perform model validation. Utilizing the fuzzy-analytic hierarchy processsimulation of total landscape in the target point 2015, quantity error, the figure of merit and allocation error were 2%, 18.5% and 8%, respectively. However, Artificial neural network-multi layer perceptron simulation led to a marginal improvement in figure of merit, i.e. 3.25%.

Predicting future land cover change and its impact on streamflow and sediment load in a trans-boundary river basin

Abstract. Sediment load can provide very important perspective on erosion of river basin. The changes of human-induced vegetation cover, such as deforestation or afforestation, affect sediment yield process of a catchment. We have already evaluated that climate change and land cover change changed the historical streamflow and sediment yield, and land cover change is the main factor in Red river basin. But future streamflow and sediment yield changes under potential future land cover change scenario still have not been evaluated. For this purpose, future scenario of land cover change is developed based on historical land cover changes and land change model (LCM). In addition, future leaf area index (LAI) is simulated by ecological model (Biome-BGC) based on future land cover scenario. Then future scenarios of land cover change and LAI are used to drive hydrological model and new sediment rating curve. The results of this research provide information that decision-makers need in order to promote water resources planning efforts. Besides that, this study also contributes a basic framework for assessing climate change impacts on streamflow and sediment yield that can be applied in the other basins around the world.

Simulating and monitoring future land-use trends using CA-Markov and LCM models

Simulating and monitoring future land-use trends is one of the major challenges for researchers, decision makers, and local authorities in terms of data, methods, and models that should be used to create a realistic and sustainable land-use planning process. This study aims to use spatio-temporal data and models to generate realistic simulation and assessment of future land-use trends in Seremban, Malaysia. For this purpose, four land-use maps of 1984, 1990, 2000, and 2010 were used. The CA-Markov model was used to simulate future land-use change of 2020 and 2030 in Seremban. After that, a Land Change Modeler (LCM) was used to assess and monitor future land-use patterns. The results confirm that the agriculture area will be affected by urban uses in the next decade in Seremban. This increase in urban uses is due to the impact of the steady increase in economic and demographic development. These results indicate the necessity to create new policies in the city to protect the sustainability of land uses in Seremban.

LOSSES ON THE ATLANTIC MATA VEGETATION INDUCED BY LAND USE CHANGES

ABSTRACT Land use changes are related to urbanization, economics and technology, influencing the management of natural resources and the ecosystem processes. The present study evaluated the behavior of land use changes in the period of 2007 and 2016, and identified the influence of these changes on the Atlantic Forest vegetation and the expected consequences for a future scenario in the short (2020) and medium term (2025). The study was developed in the Una river basin, located in southeastern Brazil. Land use mapping was performed using Landsat 5 and Sentinel-2A satellite imagery, with visual interpretation techniques. The assessment of land use changes and ecological losses was modeled in the Land Change Modeler and the gains and losses for each category and their future scenarios were identified. The calculations of landscape metrics were performed with the V-LATE extension for ArcGIS. The Anthropogenic Exposure Indicator was applied to evaluate the exposure of fragments. The comparative between land uses showed an increase of 484.33 ha of anthropogenic areas and a decrease of 484.14 ha of vegetation. The greater conversion of forests to other uses was related to agriculture. The fragments are under intense edge effect and 13.70% are under high anthropogenic exposure. By 2016, a loss of 484 ha of Atlantic Forest was identified, being expected a loss of 694.11 ha by 2020 and of 934.10 ha by 2025.

Future land use management effects on ecosystem services under different scenarios in the Wabe River catchment of Gurage Mountain chain landscape, Ethiopia

In this study, we have modeled and examined future land use management effects on ecosystem services in the Wabe River catchment of the Gurage Mountain chain landscape, Ethiopia. In addition to the climate-resilient scenario designed to meet the strategic plan of Ethiopia, the business as usual (BAU) and an alternative agroforestry scenario were modeled for the year 2030 to align with the government long-term development plan. Through the statistical and biophysical modeling approach, this study quantified and mapped the food production, water provision, carbon storage, and sequestration and sediment retention ecosystem services. The land use land cover and the other datasets were obtained from various primary and secondary sources, and prepared according to the models requirement. The future scenarios were modeled through the Land Change Modeler for ArcGIS and InVEST Scenario Generator models. The simulated BAU scenario result revealed that all of the ecosystem services decreased from the baseline status. In contrast, implementation of the climate-resilient strategy could enhance the existing status of ecosystem services. In the agroforestry scenario, all of the quantified ecosystem services increased even more than the climate-resilient scenario. We conclude that landscape management activities described in the climate-resilient strategy could ensure sustainable production while conserving the environment. However, we recommend the enset-based agroforestry system expansion, which could boost food production and enhance other ecological services in the catchment. Further studies are suggested on the expansion of this system in the catchment and similar parts of Ethiopia.

Urban land-use change: The role of strategic spatial planning

To date land-change science has devoted little attention to spatial policy and planning in urban landscapes despite the widely accepted premise that planning affects urban land change. This is primarily due to lack of relevant data and an underdeveloped theoretical understanding regarding the impact of spatial planning on urban land change. To be able to better analyse the role of spatial planning in urban development we need to distinguish: 1) the intentions expressed in the plans; 2) the means of implementation of the plans through governance processes and 3) the role of external conditions influencing implementation. Based on a synthesis of the current literature on how spatial planning is implemented in land-change models, and drawing from the literature on planning evaluation, we sketch a research agenda to further develop the understanding of these three components and their interconnections as well as their application in quantitative land-change modelling approaches for urban regions.

Spatial model of land use change related to sediment yield (case study: Cipeles and Cilutung watershed, West Java)

Land use changes (LUC) as a result of increasing human need for space are likely to destroy the hydrological function of the watershed, increase land degradation, stimulate erosion and drive the process of sedimentation. This study aimed to predict LUC during the period 1990 to 2030 in relation to sediment yield in Cilutung and Cipeles Watershed, West Java. LUC were simulated following the model of Cellular Automata-Marcov Chain, whereas land use composition in 2030 was predicted using Land Change Modeler on Idrisi Selva Software. Elevation, slope, distance from road, distance from river, and distance from settlement were selected as driving factors for LUC in this study. Erosion and sediment yield were predicted using WATEM/SEDEM model based on land use, rainfall, soil texture and topography. The results showed that the areas of forest and shrub have slightly declined up to 5% during the period 1990 to 2016, generally being converted into rice fields, settlements, non-irrigated fields and plantations. In addition, rice fields, settlements, and plantations were expected to substantially increase up to 50% in 2030. Furthermore, the study also revealed that erosion and sediment yield tend to increase every year. This is likely associated with LUC occurring in Cipeles and Cilutung Watershed.

Modeling Land Use and Land Cover Changes and Their Effects on Biodiversity in Central Kalimantan, Indonesia

Land use and land cover (LULC) change causes biodiversity decline through loss, alteration, and fragmentation of habitats. There are uncertainties on how LULC will change in the future and the effect of such change on biodiversity. In this paper we applied the Land Change Modeler (LCM) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Scenario Generator tool to develop three spatially explicit LULC future scenarios from 2015 to 2030 in the Pulang Pisau district of Central Kalimantan, Indonesia. The district is experiencing a rapid loss of biodiversity as a result of unprecedented LULC changes. Further, we used the InVEST Habitat Quality model to map habitat quality as a proxy to biodiversity in each of the scenarios. We find habitat quality decline is largest in a scenario where past trends of LULC change continue, followed by a scenario with planned agricultural expansion. Alternately, a conservation-oriented scenario results in significant improvements in habitat quality for biodiversity. This information can support in developing appropriate land use policy for biodiversity conservation in Indonesia.

Modelisation Des Changements D’occupation Des Terres En Region Soudanienne Au Nord-Ouest Du Benin

In north-west Benin, land occupation is undergoing a significant dynamic due to intense human activities putting the ecosystem in precarious balance. The objective of this study is to quantify landscape dynamics and explore possible futures of land use using remote sensing, GIS and spatiotemporal modelling tools. Landsat socio-economic data and images (1987, 2000 and 2016) were used. A multi-date classification was used to quantify changes in land use. The Land Change Modeler (LCM) of the Terrset software was used to simulate and project land use to 2031. The results show that between 1987 and 2016, the landscape initially dominated by open wooded forests and savannahs (38,8 %) and wooded and shrubby savannahs (25,8 %) in 1987 was replaced in 2016 by land use categories including wooded and shrubby savannahs (40,5 %) and crop and fallow mosaics (50,6 %). The three scenarios Business-As-Usual (BAU), Rapid Economic Growth (CER) and Coordinated Environmental Sustainability (CED), extrapolating the current trends, show that by 2031 this landscape will be dominated by crop and fallow mosaics (+ 50 %). Wood resources occupy 26 % of the area of the sector in the CED scenario, 23 % in the BAU scenario and 19% in the CER scenario. The CED scenario gives hope that the restoration and preservation of plant resources is still possible if decision-makers become aware of it.

Assessment of the thermal response of variations in land surface around an urban area

The ancient city of Ibadan has experienced major development and this development has led to modifications of the land surface over the years. This study assessed the changes that have occurred in the Land Use Land Cover (LULC) of Ibadan city using satellite image from Landsat covering 1984, 2000 and 2016. Supervised classification scheme was done using the maximum likelihood classifier for classifying the images. The extent of change of the LULC classes was performed on the classified images using Land Change Modeller (LCM). The implication of the change in LULC on Land Surface Temperature (LST) and related indices was assessed. Over a period of 32 years (1984–2016), the area coverage of the built-up region of Ibadan increased from 11.23 to 54.64 hectares in thousands with a net change of 8%. Thick vegetation was identified as the major contributor to the increase in the built-up area thus indicating urban encroachment. The implication of this was observed in thermal hotspots distribution and increase in the average LST over Ibadan as there was a decrease in vegetated surfaces that dampen the LST and an increase in the impervious surface revealed by the impervious and built-up index. In general, this study showed the capability of impervious surface indices in depicting the variations in land use land cover around a region, majorly urban sprawl. Furthermore, the evaluation of the spectral indices showed Urban Index (UI) as the best predictor LST.

MONITORING AND PREDICTING LAND USE-LAND COVER (LULC) CHANGES WITHIN AND AROUND KRAU WILDLIFE RESERVE (KWR) PROTECTED AREA IN MALAYSIA USING MULTI-TEMPORAL LANDSAT DATA

Natural and anthropogenic activities surrounding a Protected Area (PA) may cause its natural area to change in terms of Land Use-Land Cover (LULC). Thus, there is need of environmental change monitoring within and around PA because of its significant values to ecosystem at conservation scales. Effects and influences of local community within and around PA turn into the major problems for natural resource and conservations management as well as environmental impact assessment. Ascertaining the complex interface in relations to changes and its driving factors over period of time within and around PA is significant in order to predict future LULC changes, build alternative scenarios and serve as tools for decision making. The main objective of this work was to evaluate temporal change detection and prediction of LULC as well as the trends of changes from 1989 to 2016 within and around Krau Wildlife Reserve (KWR). The cloud issues were mitigated by producing cloud free image and object-based image analysis (OBIA) was adopted after a comparison with pixel-based analysis for overall accuracy and kappa statistics. The comparison of classified maps had produced a satisfactory results of overall accuracies of 91%, 86% and 90% for 1989, 2004 and 2016 respectively. The natural/dense forest between periods of 1989-2016 was decreased whereas built-up and agricultural/sparse forest were increased. The simulation model of Land Change Modeler (LCM) was utilized with digital elevation model (DEM) and past LULC maps to project future LULC pattern using Markov chain. The predicted map trend showed an increase of dense forest converted to agricultural/sparse forest in the north-western, and urban/built-up in east-southern part of KWR. The study is important for the conservation of habitat species and monitoring the current status of the KWR

Simulating long-term effect of Hyrcanian forest loss on phosphorus loading at the sub-watershed level

Conversion of forest land to farmland in the Hyrcanian forest of northern Iran increases the nutrient input, especially the phosphorus (P) nutrient, thus impacting the water quality. Modeling the effect of forest loss on surface water quality provides valuable information for forest management. This study predicts the future impacts of forest loss between 2010 and 2040 on P loading in the Tajan River watershed at the sub-watershed level. To understand drivers of the land cover, we used Land Change Modeler (LCM) combining with the Soil Water Assessment Tool (SWAT) model to simulate the impacts of land use change on P loading. We characterized priority management areas for locating comprehensive and cost-effective management practices at the sub-watershed level. Results show that agricultural expansion has led to an intense deforestation. During the future period 2010–2040, forest area is expected to decrease by 34,739 hm2. And the areas of pasture and agriculture are expected to increase by 7668 and 27,071 hm2, respectively. In most sub-watersheds, P pollution will be intensified with the increase in deforestation by the year 2040. And the P concentration is expected to increase from 0.08 to 2.30 mg/L in all of sub-watersheds by the year 2040. It should be noted that the phosphorous concentration exceeds the American Public Health Association′s water quality standard of 0.2 mg/L for P in drinking water in both current and future scenarios in the Tajan River watershed. Only 30% of sub-watersheds will comply with the water quality standards by the year 2040. The finding of the present study highlights the importance of conserving forest area to maintain a stable water quality.

MODELAGEM TEMPORAL DINÂMICA DO USO E OCUPAÇÃO DO SOLO BASEADO EM SIG PARA A BACIA DO RIO TAPACURÁ (PE)

Este estudo teve por objetivo simular cenários de uso e ocupação do solo para t4 (2035), tendo como base as mudanças no uso do solo ocorridas em t1 (1989), t2 (2007) e t3 (2015) para a bacia do Rio Tapacurá, localizada no Estado de Pernambuco. Foi realizada a previsão do uso do solo para t3 (2015), usando três métodos: (a) Rede Neural Multi-Layer Perceptron (RNMLP), (b) Similarity-Weighted Instance-Based Machine Learning Algorithm (SimWeight) e (c) Regressão Logística (RL) e para a metodologia que mostrou melhor desempenho, foi realizada a predição dos cenários futuros para t4 (2035). Os cenários futuros simulados foram: (a) Cenário 1: de continuidade das transições e (b) Cenário 2: de continuidade das transições e intensificação da classe pecuária e expansão da área urbana, usando o módulo Land Change Modeler (LCM) do Idrisi TerrSet e imagens da cobertura do solo. Os resultados da previsão do uso do solo para 2015 mostraram que o melhor desempenho foi obtido usando o método RNMLP com treinamento de 84,22% e 10.000 iterações. A simulação dos cenários futuros para t4 mostrou intensificação das transições observadas nos três anos analisados, com previsão para expansão de cerca de 3% da classe pecuária para os dois cenários simulados.