Forecast Land Use Research Articles

Navigating the city: Role of property-market intelligence channels in urban governance networks

This study presents a novel perspective on the impact of market-intelligence channels on property markets and the subsequent effects on the formation of alliances among public- and private-sector actors in urban governance. I argue and demonstrate that property actors, especially investors, collaborate through knowledge coalitions by utilizing the market-intelligence channels they created, not only to navigate property markets but also to establish the mode of regulation in urban development. By using a strategic-relational perspective, it is possible to analyze the underlying institutional infrastructure of the property market as a dynamic system shaped by the relationships and interactions among various social and economic actors. In addition to forming various marketing or lobbying coalitions, property actors forge alliances by converting pertinent information—such as locational analysis, socioeconomic data, policy and land-use forecasts, permit conditions, and planning strategies—into advanced market intelligence. This process allows them to define the norms, rules, and principles to be negotiated with public policymakers and enhance their positions in knowledge-based governance networks. To obtain empirical evidence about Amsterdam’s property market, a comprehensive data set was created to identify the influential actors. Extensive deskwork was conducted to map the diverse channels and connections of market intelligence. Key players in the property market were also interviewed to provide a comprehensive view of the active market-intelligence channels in governance networks.

Forecasting Land Use Dynamics in Talas District, Kazakhstan, Using Landsat Data and the Google Earth Engine (GEE) Platform

This study employs the robust capabilities of Google Earth Engine (GEE) to analyze and forecast land cover and land use changes in the Talas District, situated within the Zhambyl region of Kazakhstan, for a period spanning from 2000 to 2030. The methodology involves thorough image selection, data filtering, and classification using a Random Forest algorithm based on Landsat imagery. This study identifies significant shifts in land cover classes such as herbaceous wetlands, bare vegetation, shrublands, solonchak, water bodies, and grasslands. A detailed accuracy assessment validates the classification model. The forecast for 2030 reveals dynamic trends, including the decline of herbaceous wetlands, a reversal in bare vegetation, and concerns over water bodies. The 2030 forecast shows dynamic trends, including a projected 334.023 km2 of herbaceous wetlands, 2271.41 km2 of bare vegetation, and a notable reduction in water bodies to 24.0129 km2. In quantifying overall trends, this study observes a decline in herbaceous wetlands, bare vegetation, and approximately 67% fewer water bodies from 2000 to 2030, alongside a rise in grassland areas, highlighting dynamic land cover changes. This research underscores the need for continuous monitoring and research to guide sustainable land use planning and conservation in the Talas District and similar areas.

Forecasting land use and land cover change as a tool for optimising adaptation to climate change: Examples of selected Second-Tier Cities of the V4 Group

Second-tier cities are an important element in the socio-economic development of each country, including V4 countries. The result is a dynamic change in land use/cover patterns. The article analyses the possibility of using Markov chains to predict changes in land use. The results show that it is possible to use the Markov chains method in proper decision-making in land-use policy, which is combined with the stimulation of sustainable land use. The research results show that Markov chains can be an important tool to guide cities’ climate policies and build their capacity to adapt to climate change.

Forecasting Land Use and Land Cover Changes in the Malaprabha Right Bank Canal Command Area through Cellular Automata and Markov Chain Modeling

Forecasting Land Use and Land Cover Changes in the Malaprabha Right Bank Canal Command Area through Cellular Automata and Markov Chain Modeling

Application of land-use change model for supporting land-use planning in An Bien district, Kien Giang province, Vietnam

Forecasting land-use change is a critical step in assisting land-use planning, especially for coastal areas that are influenced by many economic and natural environmental factors. This study aims to estimate land use changes based for An Bien district, Mekong Delta based on multi-criteria assessment of factors affecting land use change. The model has been calibrated with the 2015 land-use map and verified with the 2020 map. The results of land-use change analysis in An Bien district were carried out according two scenarios: (i) forecasting the area under present conditions and (ii) forecasting land use area in the case of economic benefits and suitable environmental conditions for aquaculture production. Scenario 1 showed that the results will not change much compared to the current situation, in which rice-shrimp plays a leading role locality, however Scenario 2 showed that shrimp area will rapidly rise. In general, the simulation results allow users to explore future planning possibilities and serve as a basis for selecting appropriate land-use types.

Studying Urbanization Pattern in Sambalpur City during 1992-2042 using CA-ANN, and Markov-Chain Model

Models of land use/land cover (LULC) are crucial for assessing changes in LULC, forecasting land use needs, and providing guidance for sensible land use planning and management especially in urban areas. Urban sprawl is one of the main causes of the erratic variation in LULC around the globe. In this study, we used the CA-ANN and Markov-Chain models to analyse the LULC simulation based on LULC patterns of previous decades as well as the directional changes of urban expansion in Sambalpur city. We used the random forest (RF) model and Landsat imageries to prepare LULC maps for the years 1992, 2002, 2012 and 2022, for better classification accuracy. The result showed that the overall accuracy and kappa value were 94.24%, 89%, 94%, and 90% and 0.92, 0.80, 0.90 and 0.85, respectively for the selective years. Based on the transition matrix model (1992-2012), the LULC map of the year 2022 was obtained, and validated, and subsequently, LULC maps for the years 2032 and 2042 were predicted with a Kappa value of 94.97% and 93.24% respectively. The results revealed that the maximum amount of bare land converted in the settlement area, and the maximum sprawl occur in NW and 4km buffer zones. These findings define current and future patterns in LULC and offer vital information for planning and sustainable land use management in Sambalpur city.

Applying GIS and Markov chain in establishing the land use change map and forecasting land use changes in Thach That district, Hanoi city

The study applied GIS and Markov chains to assess land use changes in the period 2015-2020 and forecast land use change trends in Thach That district, Hanoi city until 2030. Using two maps of the land use status quo map in 2015 and 2020 of Thach That district and 2 period land statistics, the research result has established a land use change map in Thach That district for the period 2015 - 2020 for 16 types of land with a land change rate of 9.63% of the total natural area. During this period, the land types that tend to increase are Perennial crop land; Special-use Forest land; Aquaculture land; Other agricultural land; Residential land; Special-use land; Land for religious establishments; Land for beliefs establishments and Specialized water surfaces with an increased rate of 0.05-2.28%. In contrast, land types with a decreasing trend are Annual crop land; Production Forest land; Protective Forest land; Cemetery land, cemetery, funeral home, crematorium; Land of rivers, streams, canals and streams; Other non-agricultural land, and Unused land which decreased at a rate of 0.02-2.34%. It is forecast that by 2030, agricultural land will have 10,271.28 hectares (accounting for 54.90%), non-agricultural land will have 8,432.22 hectares (accounting for 45.07%), unused land will have 4.76 hectares (accounting for 0.03%). Performing a T-Test (Paired two sample for Means) between the area from the forecast and the planned area until 2030 shows that ρ = 0.979 > 0.05. Thus, it proves that the results of forecasting land use changes to 2030 using the Markov chain compared to the land use planning plan of Thach That district do not have too big a difference. The results of assessing land use changes and making forecasts of land change trends in the Thach That district contribute to helping policymakers have an objective view of land-use planning and economic development sustainably.

A new assessment framework to forecast land use and carbon storage under different SSP-RCP scenarios in China

The vision of achieving “carbon neutrality” has created new requirements for the projection of land use and land cover (LULC), as well as the carbon storage (CS) of terrestrial ecosystem. Global-scale LULC scenario assessments with coarser resolution introduces uncertainties to national and regional-scale studies, which in turn has a negative impact on CS analysis based on land use perspective. Therefore, we proposed a new framework for scenario-based assessment that integrates the global-scale Land Use Harmonization (LUH2) dataset, Patch-generating Land Use Simulation (PLUS) model, and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which we called LUH2-PLUS-InVEST (LPI) model. Our aim is to investigate the potential impacts of the combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) on China's future LULC and CS. By calibrating the demands, we generated structural predictions that were consistent with the actual land use. Furthermore, we explored the spatial heterogeneity of potential land use changes using 500 m × 500 m downscale simulations. Additionally, we developed a quantitative evaluation of CS from a spatiotemporal perspective and made recommendations on potential ecological threats. Our findings indicate that the basic characteristics of LULC and CS are determined by the natural context and that the prospects of land use distribution and carbon sequestration capacity are influenced by global emission pressure, regional competition, and China's unique development pattern. The results demonstrate that the LUH2-PLUS-INVEST model can provide an effective method for modeling the feedbacks of LULC and CS to the climate-society system.

Habitat quality assessment and multi-scenario prediction of the Gansu-Qinghai section of the Yellow River Basin based on the FLUS-InVEST model

Research on the impact of land use change on regional habitat quality, in various future scenarios, can effectively aid planning and decision-making for sustainable development at a regional level. The study conducted its research in the Gansu-Qinghai Yellow River section and used ArcGIS and a land use transfer matrix to analyze the spatiotemporal pattern of land use and land cover changes. The study assessed the changes in habitat quality in the Gansu-Qinghai Yellow River region between 1990 and 2020, using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, by evaluating the gains and losses. Simultaneously, 15 elements of the natural economy were chosen and examined for their temporal and spatial impact on habitat quality using the random forest model and spatially weighted regression model. To forecast land use changes in the Gansu-Qinghai Yellow River section for 2030, the Future Land Use Simulation Model (FLUS) model was utilized and a series of four scenarios (cultivated land protection scenario, ecological protection scenario, natural development scenario, and rapid development scenario) were employed. The research results indicate that over 70% of the Gansu-Qinghai Yellow River is occupied by grasslands, and only a small portion of the area, about 0.22%, is developed for construction purposes. The quality of the habitat in the Gansu-Qinghai Yellow River had a minor drop between 1990 and 2020, followed by an improvement. Habitat quality changes are primarily attributed to improvements, with variations across different areas, i.e., enhanced in the east and reduced in the central and western parts. The habitat quality of the Gansu-Qinghai Yellow River has improved in all four scenarios compared to 2020, as evidenced by the decrease in low-value habitats and increase in high-value areas. The ecological protection scenario has the highest average habitat quality value. These research results can be used to support policy development and ecological restoration initiatives in the Gansu-Qinghai Yellow River.

SWAT model-based quantification of the impact of land use land cover change on sediment yield in the Fincha watershed, Ethiopia

In recent years, Ethiopia is facing problems due to soil erosion, mainly because of the conversion of natural vegetation into cultivated land to answer to increasing human pressure. Such changes in coverage are fostering soil erosion, which is also affecting dam reservoirs, because of the increasing sediment yield entering the lake. The present study focuses on the Ethiopian Fincha watershed and takes advantage of the potentiality of the Soil and Water Assessment Tool (SWAT) model, to investigate how land use land cover changes impact soil erosion and the consequent sediment yield entering the Fincha Dam. The SWAT model was calibrated and validated using time series data of stream flow and sediment covering the period 1986-2008, and its performance was evaluated by looking at the coefficient of determination, Nash-Sutcliffe simulation efficiency, and per cent bias. Once validated, the SWAT model was applied to derive sediment yield for the future 30 years, based on forecasted land use land cover conditions. The results show that the mean annual soil loss rate increased from 32.51 t ha−1 in 1989 to 34.05 t ha−1 in 2004, reaching 41.20 t ha−1 in 2019. For the future, a higher erosion risk should be expected, with the annual soil loss rate forecasted to be 46.20 t ha−1 in 2030, 51.19 t ha−1 in 2040, and 53.98 t ha−1 in 2050. This soil erosion means that sediments transported to the Fincha Dam, located at the watershed outlet, increased significantly in the last 30 years (from 1.44 in 1989 to 2.75 mil t in 2019) and will have the same trend in the future (3.08–4.42 mil t in 2020 and 2050, respectively), therefore highly affecting the Fincha reservoir services in terms of reduction of water volume for irrigation and hydroelectric power generation. By providing possible medium/long-term scenarios, the present work can help land planners and decision-makers in developing future land management strategies based on actual projections, eventually aiming to reduce soil erosion at the watershed scale and guarantee the sustainable development of the Fincha region and its key hydraulic infrastructures.

Estimating Land Use Change Effects on Groundwater Recharge in Nadi and Kabinburi, Thailand

The effect of land use change on groundwater recharge in Nadi and Kabinburi Districts, Prachinburi Province, Thailand was studied by forecasting land use change using the CLUE model and estimating groundwater recharge using the H08 model. The results suggested that compared to the current average groundwater recharge, the groundwater recharge estimates from scenario 1 (changing the miscellaneous areas of mostly wasteland to mixed perennial areas) and scenario 2 (predicting the land use scenario for the next 10 years based on trends of land use change from the past to the present) were greater by 1.46 and 2.25%, respectively. In scenario 1, the increase in forest and mixed perennial areas increased the groundwater recharge by helping to slow down the surface runoff and, thus, increased the chances of water seepage into the soil. However, increasing the perennial area or turning wasteland into mixed perennial area (scenario 1), resulted in an increase in the groundwater recharge that was similar to the results from simulating future land use scenarios in the next 10 years (scenario 2). Therefore, to increase the efficiency of groundwater management and drought relief, the relevant agencies should adopt appropriate land use planning, be encouraged to plant perennials or support mixed farming on wasteland, restore degraded forest areas, and improve the management of water use concurrently.

Scenario modeling of ecosystem service trade-offs and bundles in a semi-arid valley basin

Amidst the rapid urbanization process, substantial transformations have emerged within ecosystem services, exerting profound ramifications on the sustainability of ecosystems. Nevertheless, an existing dearth persists in delineating the intricate interplay of trade-offs and synergies, as well as ecosystem services bundles under diverse future scenarios. This study harnesses the Convolutional neural network-Long and short-term memory-Cellular automata model to prognosticate and dissect the temporal and spatial dynamics of four distinct ecosystem services (soil retention, water yield, carbon storage, and habitat quality) across the semi-arid valley city of Lanzhou from 2000 to 2030 under multiple scenarios. The SPSS model methodically quantifies the intricate trade-offs and synergies interwoven among these services, while cluster analysis reveals ecosystem services bundles across varying scales. Notably, (1) convolutional neural network-long and short-term memory-cellular automata model demonstrates remarkable proficiency in accurately forecasting land use, boasting an elevated precision level of 0.93. (2) The trajectories of carbon storage and water yield demonstrate a diminishing pattern between 2000 and 2020, against the ascending trends observed in soil retention and habitat quality. Within the ecological priority scenario for 2030, water yield experiences a sluggish decrease, while the abatement of soil retention, carbon storage, and habitat quality degradation is maximized. (3) The trade-offs (3 pairs) and synergies (3 pairs) among ecosystem services within the study locale exhibit a state of relative equilibrium. Noteworthy among these interactions is the prominent trade-off correlation (−0.101) between soil conservation and carbon storage within the city priority scenario. (4) Four discernible ecosystem services bundles manifest at the grid scale from 2000 to2030. Conversely, three bundles at the county scale in 2000, eventually decrease to two bundles between 2010 and 2030, hinting at a diminishing trend concerning the number of ecosystem services bundles as spatial scales coarsen. Furthermore, whereby these bundles progressively aggregate spatially over time.

Gray wolf range in the western Great Lakes region under forecasted land use and climate change

AbstractLand use and climate change alter species distributions worldwide, and detecting and understanding how species ranges shift can facilitate conservation planning and action. Following extirpation from most of the contiguous United States, gray wolves (Canis lupus) have partially recolonized former range in the western Great Lakes region, but it is unknown how land use and climate change may alter amounts of wolf habitat. Using wolf observation data collected during winters 2017–2020 in Minnesota, Wisconsin, and Michigan, we created ensemble models to predict how land use and climate change may affect the amount of wolf habitat within these states. A projection model for the western Great Lakes region suggested three of four scenarios of land use and climate change will lead to 9%–35% increases in wolf habitat, while a solely climate‐based projection model supported our expectation that changes in climate, in isolation, will have limited effect on current wolf range. Our results support stable or increasing amounts of wolf habitat in the western Great Lakes region during the 21st century, suggesting limited or no adverse effects on the current distribution or further recolonization of wolves. Our findings can inform policy development regarding wolf conservation and identify areas where recolonization is plausible, thus where promoting human–wolf coexistence is most pertinent.

Forecasting land use and land cover dynamics using combined remote sensing, machine learning algorithm and local perception in the Agoènyivé Plateau, Togo

Knowledge of land use and land cover (LULC) dynamics helps policymakers set sustainable land management plans. Population growth, migration, and rural exodus have led to spatial expansion in many cities worldwide, especially in coastal urban settings. This research aims at assessing and predicting LULC over the Agoènyivé plateau (AP) in Togo, using remote sensing and Markov Chain along with local perceptions on LULC change, thus considering four scenarios (i.e., Business-as Usual, afforestation, wetland policy, and building policy). LULC was classified based on the maximum likelihood algorithm using Landsat images. The future scenarios maps were produced using the Multilayer Perceptron (MLP) neural networks-Markov chain modelling approach by considering five driver variables considered to affect future development. The historical change analysis was performed for the periods of 1986–2001, 2001–2011, and 2011–2020, while 1986–2020 was used for model validation and change prediction. Image accuracy assessment was performed using the error or confusion matrices, kappa coefficients (Kscores), Relative Operating Characteristics (ROC), and accuracy scores, with an overall accuracy (Kappa coefficient) of 87% (0.81), 88% (0.81), 90% (0.82), and 92% (0.84) for the years 1986, 2001, 2011, and 2020, respectively. The results showed an increase in built-up areas (38.71% land gain), while a decrease was observed in mixed vegetation/savannah (22.69% land loss), croplands/bare surfaces (22.89%), and wetlands (3.36% land loss) over the period of 1986–2020. An increase in built-up areas (7.79% and 19.79% land gain by 2030 and 2050, respectively) and a decrease in mixed vegetation (1.13% and 2.54% land loss by 2030 and 2050, respectively), croplands and bare surfaces (6.58% and 16.98% loss by 2030 and 2050, respectively), and wetlands (0.13% and 0.31% loss by 2030 and 2050, respectively), are expected under the BAU scenario. Contrary to the wetland policy, which induces change only in the wetlands (0.02% and 0.047% land loss by 2030 and 2050, respectively), the afforestation scenario results in a gain in forest/savannah (0.11% and 0.73% land gain by 2030 and 2050 compared to the BAU scenario), but also a decrease in croplands/bare surfaces (0.64% by 2050 compared to the BAU scenario), whereas the building policy scenario results in a long-term increase in croplands/bare surfaces (2.88% by 2050, compared to the BAU), and wetlands (0.027 and 2.88% by 2050) in addition to the mixed vegetation/savannah. LULC dynamics (including future urbanization trends) for the AP under various scenarios is a step ahead of time, highlighting the importance of the implementation of existing laws and policies, but also providing information to deal with the observed inharmonious spatial development of urban centres, and serving as a dataset source for LULC change impact assessments on related resources.

Indicator plants of anthropogenic disturbances: Scientific approach, educational technologies

The technology of using the indicative properties of plants both for obtaining scientific results and teaching this technique to students and young scientists is proposed for implementation. Aspects of research organization and didactic work in the implementation of the environmental monitoring program of Donbass are highlighted. Over the period of research (1996-2023), high levels of pollution and anthropogenic transformation of ecosystems in the industrial areas of Eastern Europe have been established. The data are based both on experiments in open landscapes and laboratory conditions, special procedures for statistical processing and interpretation of the results. These localities of intensive economic use are the places of great scientific and applied interest to ecologists and educators in this area. Indicator plants are visual objects in the knowledge of the fundamental nature and practical use for information about the quality of the environment. In scientific and educational activities the following are important: organization of a laboratory, availability of equipment, functioning of a museum, a card-index, a herbarium fund, the possibility of introducing case studies technology, the theory of solving inventive tasks, conducting interactive lectures, seminars, demonstration experiments and special modern technologies for training environmental specialists – modeling and land-use forecasting and urbanized environment. Methods of implementation of scientific and pedagogical experiment on phytomonitoring and ecological expertise with the help of plants have been introduced into work with students, postgraduates and young scientists of the Department of Botany and Ecology of Donetsk State University.

Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World.

This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions—the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world—Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing of the urban space was used to train the models, and the remaining was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of for Dhaka, for Nairobi, for Jakarta, for Guangzhou city, for Mumbai, for Cairo, and for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities’ current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces.

Application of modified cellular automata Markov chain model: forecasting land use pattern in Lebanon

This article modifies the use of the Cellular Automata Markov Chain Model to predict future land use pattern in Lebanon, and compares it to the current developed model. LandSat images of years 2000, 2009 and 2018 are used to generate land use maps within the geographic information system. Current developed model was generated by integrating Population density data with land use classification maps to decompose the built-up development to three sub-classes: High, Medium and Low-density built-up land uses. Simulations of future land use pattern over the year 2018 based on these two prediction models reveal that the Modified Cellular Automata Markov Chain Modelling technique is more accurate than the Extended Markov Chain model. Spatial effects of built-up densities are validated in this study. Consequently, the extension of the Cellular Automata Markov Chain Model represents an innovative tool for regional and urban planning to forecast potential locative distribution of old and new urban agglomeration. The sequential shift of the urban areas among different density classes in addition to the interactions of urban agglomerations should be employed as a guiding tool for decision-makers and planners during the phase of developing new population and economic strategies, new urban Masterplan and during the process of enacting/developing new land-use policies. In the final part of the study, a simulation of land use pattern for the year 2036 is generated using TerrSet v.18 software and an analysis of the outcome for the forecasted map is discussed.

Predicting land use change base on GIS and remote sensing

The process of urbanization in Vietnam is developing rapidly, corresponding to the rapid changes in land use. Forecasting land use development assists policymakers in making accurate decisions in urban planning and development. The objective of this paper is to forecast and analyze land use development trends based on Landsat images in 2006, 2010, and 2016 with the help of remote sensing and GIS, a case study at Gio Linh district, Quang Tri province. The analysis results show that the rapid urbanization in Gio Linh district has led to a rapid decrease in the forest area from 28.14% compared to the total area, in 2016 to 13.5%, in 2026 to 2.67% and 2036 to 0.33%. The forecast processing was done on the neural network built-in module in the Selva version of IDRISI. The accuracy achieved is 80.5%.

Integrated framework of system dynamics and meta-heuristic for multi-objective land use planning problem

Land use optimization as a resource allocation problem can be defined as the process of assigning different land uses to a region. Sustainable development also involves the exploitation of environmental resources, investment orientation, technology development, and industrial changes in a coordinated form. This paper studies the multi-objective sustainable land use planning problem and proposes an integrated framework, including simulation, forecasting, and optimization approaches for this problem. Land use optimization, a multifaceted process, requires complex decisions, including selection of land uses, forecasting land use allocation percentage, and assigning locations to land uses. The land use allocation percentage in the selected horizons is simulated and predicted by designing a System Dynamics (SD) model based on socio-economic variables. Furthermore, land use assignment is accomplished with a multi-objective integer programming model that is solved using augmented e-constraint and non-dominated sorting genetic algorithm II (NSGA-II) methods. According to the results of the SD model, land use changes depend on population growth rate and labor productivity variables. Among the possible scenarios, a scenario focusing more on sustainable planning is chosen and the forecasting results of this scenario are used for optimal land use allocation. The computational results show that the augmented e-constraint method cannot solve this problem even for medium sizes. The NSGA-II method not only solves the problem at large sizes over a reasonable time, but also generates good-quality solutions. NSGA-II showed better performance in metrics, including number of non-dominated Pareto solutions (NNPS), mean ideal distance (MID), and dispersion metric (DM). Integrated framework is implemented to allocate four types of land uses consisting of residential, commercial, industrial, and agricultural to a given region with 900 cells.

Indigenous Knowledge Systems and Conservation of Settled Territories in the Bolivian Amazon

Landscapes settled by indigenous communities represent nuanced inter-relationships between culture and environment, where balance is achieved through Indigenous Knowledge Systems (IKS). Through IKS, native peoples worldwide live, farm, and consume resources in a manner that is responsive to natural systems and, as such, their lands present less deforestation and more sustainable production per capita than is exhibited by non-indigenous practices. In Bolivia, the Origin Farmer Indigenous Territory (TIOC) communities of Yaminahua-Machineri and Takana-Cavineño, located in the North Amazon, are facing external threats of non-indigenous anthropogenic land use change, such as road-building and industrial-scale resource extraction. In order to understand the potential environmental and cultural loss to these territories, the present work seeks to determine the present, base-line conservation state within these Bolivian communities, and forecast land use change and its consequences until the year 2030. This was undertaken using a three-stage protocol: (a) the TIOC communities’ current forest-based livelihoods, characteristics and management were determined using on-site observation techniques and extensive literature review; (b) the historical land use change (LUC) from natural vegetation to anthropogenic use was estimated using multitemporal satellite imagery; and, finally, (c) geographically explicit non-indigenous anthropogenic land-use change threat was extrapolated until 2030 using the GEOMOD modeler from the TerraSet software. Preliminary results show that both TIOCs case-sites are fairly conserved due to their forest dependence. However, deforestation and degradation could be evidenced, particularly within TIOC areas not officially recognized by the central government, due to pressures from surrounding, new non-indigenous settlements, road infrastructure, connection to markets, and the threat of the oil exploitation. Projected LUC suggest serious threats to the unrecognized TIOC areas if community governance is not reinforced, and if extractivist and non-indigenous development patterns continue to be promoted by state and central government.