Cover Change Models Research Articles

STAPLE: A land use/-cover change model concerning spatiotemporal dependency and properties related to landscape evolution

Cellular automata (CA) based models are practical tools to simulate the spatiotemporal landscape evolution induced by the land use/-cover change (LUCC). Existing models are struggling to comprehensively handle the spatiotemporal driving relationships amid the nonlinear LUCC process. Besides, the landscape patterns are not considered in most models, making them struggled to support the development strategies. Aiming at overcoming these obstacles, a novel land use/-cover change model concerning spatiotemporal dependency and properties related to landscape evolution (STAPLE) is proposed in this paper. A potential generating module establishing the nonlinear spatiotemporal driving relationship and a spatial allocating module employing a landscape-based CA are integrated for realistic LUCC simulations. As a case study, the proposed model is applied in Zhengzhou, China to assess its performance. It is indicated that the STAPLE model achieves a higher simulation accuracy, and the landscape properties are effectively manipulated. It provides a reproducible tool for policy-makers to explore a low-ecological-risk landscape under different future scenarios and achieve sustainable developments.

Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development

Urban expansion, a defining feature of the contemporary era, presents both challenges and opportunities for sustainable development [...]

Land use and land cover change simulation enhanced by asynchronous communicating cellular automata

Land use and land cover change (LUCC) modeling is crucial to urban planning and policy making. A well-used and effective paradigm for LUCC is the ANN-CA model which employs an artificial neural network (ANN) to calculate a transition probability over each land cell from various driving factors, and then uses a cellular automaton (CA) to evolve all cells consecutively to update their land usage and coverage according to the estimated probability distribution. This paper focuses on the effect of delays or perturbations possibly taking place between land cells on the LUCC modeling. To this end, a new ANN-CA model is proposed which adopts an asynchronous communicating cellular automaton (ACCA), rather than the conventional synchronous CA. Especially, the ACCA allows every cell to communicate with its neighbors independently at random times via a specific protocol, which offers a natural way to include stochastic delays in exchanging the current land usages between cells. As a result, every change of a cell's land use in the ACCA may not affect its neighbors immediately, but is subject to delays that might play an important role in modeling the practical LUCC. Numerical analysis of the new model are carried out over three regions of Chongqing city, China with different scales: Yongchuan District, Sanjiao Town, and Huangguashan Village, and experimental results demonstrate that the proposed ANN-ACCA model can achieve a higher accuracy for LUCC simulation as compared to conventional ANN-CA models.

Assessment of population dynamics and forest cover change in Yumbe District, Uganda

Sub-Saharan Africa is well endowed with both renewable and non-renewable natural resources critical in supporting several forms of development on the continent. Key among these is natural forest resources. However, the population explosion in sub-Saharan Africa in general and Uganda, in particular, is threatening the survival of these forests due to the associated increasing demand for food, fodder, energy, and land for settlement. The study was conducted in Yumbe district where the forests considered included woodland and bushland since tropical high forests have been depleted or degraded by human activities. We used a predictive model to map future forest cover loss amidst the rapidly increasing population in Yumbe district in Uganda. Specifically, the study analyzed the relationship between population dynamics and forest cover change to predict future forest cover changes. To analyze changes in forest cover, the study utilized Landsat satellite imagery for 1990, 2000, 2010, and 2021; while the population data for the respective years was obtained from the Uganda Bureau of Statistics (UBOS). To explain the role of anthropogenic forces on forest cover change, the study considered different land use types as explanatory variables: planted forests, subsistence farmland, built-up areas, and other land use types. It then explored the interactions between these variables and forest cover change in the study area. Population-forest cover change model was developed to evaluate three decades of population and trends of forest cover to predict forest cover for 2032. The results indicate that in the three decades, the population increased by more than sixfold, and land area under subsistence agriculture, a proxy of population increased by 195.2%, but the forest cover declined by 80.3%. It is predicted that the forest cover will be lost completely by 2032 when the population reaches an estimated 838,078 from the current 657,430 people. This study, therefore, recommends that off-land employment opportunities such as tourism, apiary, transport, and manufacturing industries should be expanded in order to save forest resources from spatially extensive agricultural land uses. Key words: Forest, Forest cover loss, Predictive modeling, Population dynamics, Land use

Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes

This study aimed to enhance land use and land cover (LULC) change models by addressing their main limitations, which include the lack of accountability and temporal stability of driving forces. Additionally, the study aimed to create area-based scenarios to forecast future LULCs, rather than solely relying on distribution-based scenarios. To accomplish this goal, the study developed a coupled System Dynamics (SD) and Cellular Automata (CA) modeling system to simulate possible LULC changes in the Gavkhooni Basin, central Iran. The study utilized LULC maps from Landsat images in 2001, 2011, and 2021 to analyze spatio-temporal land use changes in the region. Agricultural and residential transition suitability layers were produced using a spatial Multi-Criteria Evaluation procedure and applied to inform the CA model in the proper allocation of LULC changes. Three interconnected water supply, agricultural, and residential area projection subsystems were developed using system dynamics method to determine land requirements for LULC conversions from 2020 to 2041, taking into account factors such as water availability, land suitability, agricultural labor force, and economic development. Ten scenarios were developed based on changes in the key variables affecting the limiting factors, such as climatic conditions and water management policies, to project agricultural and residential areas in the future. The CA's spatial allocation informed by transition suitability layers was found to be satisfactory with a Kappa-location value of 0.85. The subsystems were competent in projecting water supply with Mean Absolute Error (MAE) values of 6.57% and the dynamics of agricultural and residential areas with MAE values of 2.94%, whereas those of the Markovian Chain model were found to be 23.02% and 7.5% for agricultural and residential areas, respectively. The study found that available agricultural areas varied significantly between 86.53 and 1480 sq.km under different climatic conditions, irrigation efficiency, and agricultural water assignment coefficients between 2024 and 2033. Residential area demand was found to be increasing with different rates under the scenarios between 47.40 and 73.01 sq.km. The SD-CA coupled framework presented in this research can be viewed as a decision support system to develop compensatory strategies for better management and planning of agricultural and residential lands.

A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects

Land use land cover (LULC) modeling is considered as the best tool to comprehend and unravel the dynamics of future urban expansion. The present paper provides a comprehensive review of existing LULC modeling techniques and novel approaches used by the research community. Moreover, the review also compares each technique’s applications, utility, drawbacks, and broader differences. The rationale behind such a comparison is to highlight the strengths/weakness of individual techniques. The review further highlights the utility of the hybridization of different techniques (e.g., machine learning model combined with statistical models) to LULC modeling to complement their strengths. Although significant progress has been made in LULC modeling, the review highlights the need to incorporate the policy framework into LULC modeling for better urban planning and management. The present review will help researchers and policymakers to achieve better land management practices and ultimately assist in achieving Sustainable Development Goal-15 (SDG-15) (i.e., life on land).

Genetic viability and habitat suitability of the critically endangered southern muriqui (Brachyteles arachnoides) in the Atlantic Forest's fragmented landscapes under land use and climate change scenarios

The joint effects of climate change and landscape fragmentation to the genetic viability of isolated populations has barely been addressed for the Atlantic Forest fauna. Therefore, this work explored the potential habitat suitability for the southern muriqui (Brachyteles arachnoides), by modeling climate change, landscape fragmentation, and genetic diversity loss of the species. Maxent was used to model its potential distribution in 2050, with two climate change scenarios. A land use and land cover change model was applied to describe current and future forest fragmentation patterns, and a Population and Habitat Viability Analysis (PHVA) was used to describe the retention of genetic diversity of the southern muriqui. Although PHVA modeling provided a low risk of extinction of the southern muriqui, climate change and fragmentation could result in the loss of >65% of the suitable forest patches, and reduce the habitat suitability to only 11% of the potential distribution area, which could lead to future genetic diversity loss and decreased capacity of self-sustained populations. In both climate change scenarios, the suitable areas for the southern muriqui in Paraná and Rio de Janeiro states will decrease more drastically. Areas where the primate occurs in the interior of São Paulo and Rio de Janeiro states will disappear or be climatically disconnected from the core potential habitat. Alike preventing further deforestation, Atlantic Forest restoration actions are needed to connect the viable populations for compensating the projected land use and climate change impacts to the long term persistence of the southern muriqui.

A Comparative Study of Various Land Use and Land Cover Change Models to Predict Ecosystem Service Value

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.

Can we avert an Amazon tipping point? The economic and environmental costs

The Amazon biome is being pushed by unsustainable economic drivers towards an ecological tipping point where restoration to its previous state may no longer be possible. This degradation is the result of self-reinforcing interactions between deforestation, climate change and fire. We assess the economic, natural capital and ecosystem services impacts and trade-offs of scenarios representing movement towards an Amazon tipping point and strategies to avert one using the Integrated Economic-Environmental Modeling (IEEM) Platform linked with spatial land use-land cover change and ecosystem services modeling (IEEM + ESM). Our approach provides the first approximation of the economic, natural capital and ecosystem services impacts of a tipping point, and evidence to build the economic case for strategies to avert it. For the five Amazon focal countries, namely, Brazil, Peru, Colombia, Bolivia and Ecuador, we find that a tipping point would create economic losses of US$256.6 billion in cumulative gross domestic product by 2050. Policies that would contribute to averting a tipping point, including strongly reducing deforestation, investing in intensifying agriculture in cleared lands, climate-adapted agriculture and improving fire management, would generate approximately US$339.3 billion in additional wealth and a return on investment of US$29.5 billion. Quantifying the costs, benefits and trade-offs of policies to avert a tipping point in a transparent and replicable manner can support the design of regional development strategies for the Amazon biome, build the business case for action and catalyze global cooperation and financing to enable policy implementation.

Quantifying the sensitivity of L-Band SAR to a decade of vegetation structure changes in savannas

Global savannas are the third largest carbon sink with large human populations being highly dependent on their ecosystem services. However, savannas are changing rapidly due to climate change, fire, animal management, and intense fuelwood harvesting. In southern Africa, large trees (>5 m in height) are under threat while shrub cover (<3 m) is increasing. The collection of multi-date airborne LiDAR (ALS) data, initiated over a decade ago in the Lowveld of South Africa, provided a rare opportunity to quantify the ability of L-band SAR to track changes in savanna vegetation structure and this study is the first to do so, to our knowledge. The objective was to test the ability of ALOS PALSAR 1&2, dual-pol (HH, HV) data to quantify woody cover and volume change in savannas over 2-, 8- and 10-year periods through comparison to ALS. For each epoch (2008, 2010, 2018), multiple PALSAR images were processed to Gamma0 (γ0) at 15 m resolution with multi-temporal speckle filtering. ALS data were processed to fractional canopy cover and volume, and then compared to 5 × 5 aggregated (75 m) SAR mean γ0. The ALS cover change (∆CALS) and volume change between pairs of years were highly correlated, with (R2 > 0.8), thus results for cover change applied equally to volume change. Cover change was predicted using (i) direct backscatter change or (ii) the difference between annual cover map product derived using the Bayesian Water Cloud Model (BWCM) and logarithmic models. The linear relationship between ∆γ0 and ∆CALS varied between year pairs but reached a maximum R2 of 0.7 for 2018–2010 and a moderate R2 of 0.4 for 2018–2008. Overall, 1 dB ∆γ0 corresponded to approximately 0.1 cover change. The three cover change models had very similar uncertainties with mean RMSE = 0.15, which is 13% of the observed cover change range (−0.6 to +0.6). The direct backscatter change approach had less underestimation of positive and negative cover change. The L-band backscatter had a higher sensitivity than suggested by previous studies, as it was able to reliably distinguish cover change at 0.25 increments. The SAR-derived cover change maps detected the loss of stands of big trees, and widespread increases in cover of 0.35–0.65 in communal rangelands due to shrub encroachment. In contrast, the maps suggest that cover generally decreased in conservation areas, forming distinct fence-line effects, potentially caused by significant increases in elephant numbers and frequent, intense wildfires in reserves.

Re-considering the status quo: Improving calibration of land use change models through validation of transition potential predictions

The increasing complexity of the dynamics captured in Land Use and Land Cover (LULC) change modelling has made model behaviour less transparent and calibration more extensive. For cellular automata models in particular, this is compounded by the fact that validation is typically performed indirectly, using final simulated change maps; rather than directly considering the probabilistic predictions of transition potential. This study demonstrates that evaluating transition potential predictions provides detail into model behaviour and performance that cannot be obtained from simulated map comparison alone. This is illustrated by modelling LULC transitions in Switzerland using both Logistic Regression and Random Forests. The results emphasize the need for LULC modellers to explicitly consider the performance of individual transition models independently to ensure robust predictions. Additionally, this study highlights the potential for predictor variable selection as a means to improve transition model generalizability and parsimony, which is beneficial for simulating future LULC change.

Remote sensing and geographic information systems technics for spatial-based development planning and policy

Indonesia's land-use and land-cover change (LULCC) is a global concern. The relocation plan of the capital city of Indonesia to East Kalimantan will be becoming an environmental issue. Knowing the latest land cover change modeling and prediction research is essential for fundamental knowledge in spatial planning and policies for regional development. Five articles related to integrated technology of geographic information systems (GIS) and remote sensing for spatial modeling were reviewed and compared using nine variables: title, journal (ranks), keywords, objectives, data sources, variables, location, method, and main findings. The results show that the variables that significantly affect LULCC are height, slope, distance from the road, and distance from the built-up area. The artificial neural network-based cellular automata (ANN-CA) method could be the best approach to model the LULCC. Furthermore, by the current availability of global multi-temporal and multi-sensor remote sensing data, the LULCC modeling study can be limitless

Editorial for the Special Issue: “Integrated Applications of Geo-Information in Environmental Monitoring”

Geo-information technology has been playing an increasingly important role in environmental monitoring in recent decades. With the continuous improvement in the spatial resolution of remote sensing images, the diversification of sensors and the development of processing packages, applications of a variety of geo-information, in particular, multi-resolution remote sensing and geographical data, have become momentous in environmental research, including land cover change detection and modeling, land degradation assessment, geohazard mapping and disaster damage assessment, mining and restoration monitoring, etc. In addition, machine learning algorithms such as Random Forests (RF) and Convolutional Neural Networks (CNN) have improved and deepened the applications of geo-information technology in environmental monitoring and assessment. The purpose of this Special Issue is to provide a platform for communication of high-quality research in the world in the domain of comprehensive application of geo-information technology. It contains 10 high-level scientific papers on the following topics such as desertification monitoring, governance of mining areas, identification of marine dynamic targets, extraction of buildings, and so on.

Editorial on Special Issue “Geo-Information Technology and Its Applications”

Geo-information technology plays a critical role in urban planning and management, land resource quantification, natural disaster risk and damage assessment, smart city development, land cover change modeling and touristic flow management. In particular, the development of big data mining and machine learning techniques (including deep learning) in recent years has expanded the potential applications of geo-information technology and promoted innovation in approaches to mining in different fields. In this context, the International Conference on Geo-Information Technology and its Applications (ICGITA 2019) was held in Nanchang, Jiangxi, China, 11–13 October 2019, co-organized by the Key Laboratory of Digital Land and Resources, East China University of Technology, the Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS), which was renamed in 2017 the Aerospace Information Research Institute (AIR), CAS, and the Institute of Space and Earth Information Science of the Chinese University of Hong Kong. The outstanding papers presented at this event and some other original articles were collected and published in this Special Issue “Geo-Information Technology and Its Applications” in the International Journal of Geo-Information. This Special Issue consists of 14 high-quality and innovative articles that explore and discuss the typical applications of geo-information technology in the above-mentioned domains.

Comparing the structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages

Research on the uncertainty of Land Use Cover Change (LUCC) models is still limited. Through this paper, we aim to globally characterize the structural uncertainty of four common software packages (CA_Markov, Dinamica EGO, Land Change Modeler, Metronamica) and analyse the options that they offer for uncertainty management. The models have been compared qualitatively, based on their structures and tools, and quantitatively, through a study case for the city of Cape Town. Results proved how each model conceptualised the modelled system in a different way, which led to different outputs. Statistical or automatic approaches did not provide higher repeatability or validation scores than user-driven approaches. The available options for uncertainty management vary depending on the model. Communication of uncertainties is poor across all models.

Urban land teleconnections in the United States: A graphical network approach

Cities influence land use change on neighboring and distal areas through sociopolitical or infrastructural connections between urban and non-urban regions, termed teleconnections. While teleconnections are generally recognized as important to land cover dynamics, many land use and land cover change (LULCC) modeling efforts do not explicitly account for non-contiguous spatial urban-land relationships. Here, we quantify and map urban land teleconnections in the US using information theory in concert with graphical networks. Evidence of teleconnections relied on long-term (1950–2016) changes in urban land cover and urban population intensity in urban areas (census defined “urban area”) relative to land dynamics in other cities and rural lands (rural portions of counties), located both proximate and distal to each city. While there are numerous definitions of rural and urban (see Richter, 2021 for review), we rely on national census definitions in this study for replicability. We find that optimal urban-rural land network complexity and network size ranged dramatically and depended on the information theory measure and variables under consideration. City-city networks were very large, suggesting the complex and simultaneous effects of globalization on cities. However, we also find that proximate urban-rural networks are small and influenced directly by individual cities, though each city may, in turn, be influenced by complex teleconnections with other cities. Distal rural areas providing agricultural commodities were characterized by decreasing agriculture land use yet increasing agricultural production intensity. Hence, distal rural commodity areas may support multiple cities, incurring increased land stress in those areas. Our results suggest that changes in population intensity, rather than urban land cover alone, induce more numerous effects on proximate and distal rural lands, as well as other cities. Predictive models of teleconnection strength explained 4.5% to 98% of variation in teleconnection measures and suggested that rural land characteristics were equally, if not more, important than variables characterizing city land dynamics. This indicates that local dynamics in rural areas are potentially more important drivers of land change relative to teleconnections with cities. Ultimately, the methodological approach presented in this paper holds promise in incorporating teleconnections in LULCC modeling efforts, providing important considerations for later studies on emissions modeling, economic supply chains and future land-use planning.

Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift

Modeling land use land cover (LULC) change is crucial to understand its spatiotemporal trends to protect the land resources sustainably. The appraisal of this study was to model LULC change from 1985 to 2050 owing to the business-as-usual scenario (BAU) in Gidabo River Basin (GRB) located in the Main Ethiopian Rift Valley. Different dependent and independent spatial datasets were used viz, 1985, 2003 and 2021 Landsat imagery; topography features, proximity variables, population density and evidence likelihood. Since the future projection requires the historical land use as a baseline, historical land use trends were detected using hybrid image classification procedure in ERDAS Imagine and nine major land cover classes were identified. Multi-Layer Perceptron Neural Network and Cellular Automata-Markov Chain model built-in TerrSet software were implemented to project the 2035 and 2050 LULC. The study depicts, GRB experienced significant LULC dynamics and will also be extended for the coming several years. Agriculture land, settlement and water body showed significant gains at the expense of forest, shrub and grasslands loss. Land use changes beyond land's capability played a significant role in triggering land degradation. To minimize these adverse consequences of land use change, environmentally-friendly management measures must be implemented. The outcome of this study will be helpful in providing the opportunity to develop adequate land and water resource conservation strategy plan for the future.

An Integrated CNN Model for Reconstructing and Predicting Land Use/Cover Change: A Case Study of the Baicheng Area, Northeast China

Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies.

Regional ecosystem health assessment based on landscape patterns and ecosystem services approach

Ecosystem health is one of the most important topics for ecosystem management and it is also the main core in ecosystem assessment. In this study, a regional ecosystem health assessment (REHA) method is adopted to develop a spatial ecosystem health framework for Gorgan Township, North-Eastern Iran. The REHA methodology refers to an ecosystem mosaic's ability to both maintain physical health and guarantee the supply of the Ecosystem Services (ES) necessary for human communities. In order to spatially quantify ecosystem health, an integrated evaluation of land use land cover (LULC) change and ecosystem services modelling was implemented. The analysis revealed that during 1987–2005, there was a slight decline in the mean regional ecosystem health that occurred in Gorgan Township (−0.86), especially within urban vegetation and suburban areas. The built-up areas and expansion of agricultural fields into natural lands were influential factors affecting the overall regional ecosystem health value. Our result showed that among all the ecosystem health indicators, ecosystem resilience and ecosystem vigour were the most important factors positively and negatively influenced the REH in the study area. Relations between land use patterns, Ecosystem Health (EH), and ES in the study area indicate high intricacy of the structure-functional interaction within the landscape. Providing local managers with their integrative model may become a useful tool for efficient natural resource management on the one hand, and the protection of valuable ecosystems on the other.

Analyzing the impacts of land use policies on selected ecosystem services in the upper Chattahoochee Watershed, Georgia, United States

The rapid rate of urbanization within the Upper Chattahoochee Watershed (UCW) is threatening the provision of ecosystem services (ESs) for six million residents of the Atlanta Metropolitan Area. This study uses the land cover change model TerrSet to project future land cover from 2016 to 2040. The modular toolset InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) is used to assess the efficacy of four land use policies in maintaining the provision of three ESs (carbon storage, wildlife habitat, and water quality) within the UCW. The Baseline scenario represents past urbanization trends, whereas the Urbanization scenario accounts for a higher urban growth rate. The Plan 2040 scenario includes existing policy guidelines, and the Conservation scenario adds forested riparian buffer areas. Two integrated indexes and an economic valuation of ESs were used to combine all ESs and analyze the overall performance of each policy. The first index uses unequal weights for ESs based on the Analytical Hierarchical Process, whereas the second index uses equal weights. The values of both integrated indexes and economic values were highest in the Conservation scenario and lowest in the Urbanization scenario. No significant differences in the provision of ESs were found between the Baseline and the Plan 2040 scenarios. However, the integrated indexes and economic values for both land use policies declined over time. Our study will feed into the ongoing movement of sustainable watershed management for ensuring the provision of ESs, especially for rapidly urbanizing cities worldwide, in general, and in the United States, in particular.