Research

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18 pages, 2790 KiB

Open AccessFeature PaperArticle

The Flow Matrix Offers a Straightforward Alternative to the Problematic Markov Matrix

by Jessica Strzempko and Robert Gilmore Pontius, Jr.

Land 2023, 12(7), 1471; https://doi.org/10.3390/land12071471 - 24 Jul 2023

Viewed by 1364

The Flow matrix is a novel method to describe and extrapolate transitions among categories. The Flow matrix extrapolates a constant transition size per unit of time on a time continuum with a maximum of one incident per observation during the extrapolation. The Flow [...] Read more.

The Flow matrix is a novel method to describe and extrapolate transitions among categories. The Flow matrix extrapolates a constant transition size per unit of time on a time continuum with a maximum of one incident per observation during the extrapolation. The Flow matrix extrapolates linearly until the persistence of a category shrinks to zero. The Flow matrix has concepts and mathematics that are more straightforward than the Markov matrix. However, many scientists apply the Markov matrix by default because popular software packages offer no alternative to the Markov matrix, despite the conceptual and mathematical challenges that the Markov matrix poses. The Markov matrix extrapolates a constant transition proportion per time interval during whole-number multiples of the duration of the calibration time interval. The Markov extrapolation allows at most one incident per observation during each time interval but allows repeated incidents per observation through sequential time intervals. Many Markov extrapolations approach a steady state asymptotically through time as each category size approaches a constant. We use case studies concerning land change to illustrate the characteristics of the Flow and Markov matrices. The Flow and Markov extrapolations both deviate from the reference data during a validation time interval, implying there is no reason to prefer one matrix to the other in terms of correspondence with the processes that we analyzed. The two matrices differ substantially in terms of their underlying concepts and mathematical behaviors. Scientists should consider the ease of use and interpretation for each matrix when extrapolating transitions among categories. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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25 pages, 13953 KiB

Open AccessArticle

Pre-Processing Training Data Improves Accuracy and Generalisability of Convolutional Neural Network Based Landscape Semantic Segmentation

by Andrew Clark, Stuart Phinn and Peter Scarth

Land 2023, 12(7), 1268; https://doi.org/10.3390/land12071268 - 21 Jun 2023

Cited by 2 | Viewed by 1221

Abstract

Data pre-processing for developing a generalised land use and land cover (LULC) deep learning model using earth observation data is important for the classification of a different date and/or sensor. However, it is unclear how to approach deep learning segmentation problems in earth [...] Read more.

Data pre-processing for developing a generalised land use and land cover (LULC) deep learning model using earth observation data is important for the classification of a different date and/or sensor. However, it is unclear how to approach deep learning segmentation problems in earth observation data. In this paper, we trialled different methods of data preparation for Convolutional Neural Network (CNN) training and semantic segmentation of LULC features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia. This was conducted by trialling and ranking various training patch selection sampling strategies, patch and batch sizes, data augmentations and scaling and inference strategies. Our results showed: a stratified random sampling approach for producing training patches counteracted class imbalances; a smaller number of larger patches (small batch size) improves model accuracy; data augmentations and scaling are imperative in creating a generalised model able to accurately classify LULC features in imagery from a different date and sensor; and producing the output classification by averaging multiple grids of patches and three rotated versions of each patch produced a more accurate and aesthetic result. Combining the findings from the trials, we fully trained five models on the 2018 training image and applied the model to the 2015 test image. The output LULC classifications achieved an average kappa of 0.84, user accuracy of 0.81, and producer accuracy of 0.87. Future research using CNNs and earth observation data should implement the findings of this project to increase LULC model accuracy and transferability. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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13 pages, 4425 KiB

Open AccessArticle

Estimating Advance of Built-Up Area in Desert-Oasis Ecotone of Cholistan Desert Using Landsat

by Sami Ullah, Yan Shi, Muhammad Yousaf Sardar Dasti, Muhammad Wajid and Zulfiqar Ahmad Saqib

Land 2023, 12(5), 1009; https://doi.org/10.3390/land12051009 - 04 May 2023

Cited by 1 | Viewed by 1815

Abstract

There have been few attempts to estimate the effects of land use and land cover (LULC) on ecosystem services in desert-oasis ecotones, which are recognized as critical ecological barriers and buffers that prevent deserts from expanding into oases. This research investigated how remote [...] Read more.

There have been few attempts to estimate the effects of land use and land cover (LULC) on ecosystem services in desert-oasis ecotones, which are recognized as critical ecological barriers and buffers that prevent deserts from expanding into oases. This research investigated how remote sensing and geographic information technology may be used to monitor changes in LULC in the Cholistan desert and the Bahawalpur region of Pakistan between the years 2015 and 2022. The objective of this research was to identify thematic and statistical shifts in LULC in the study area due to various human interventions in the area. Landsat-8 images were processed using the maximum likelihood supervised classification technique using 500 training samples to categorize the study area into four LULC classes, i.e., desert/barren land, waterbodies, vegetation, and built-up areas, with an overall accuracy of 93% and 98% for 2015 and 2022, respectively. Results indicate a significant expansion in built-up area in 2022, which is up to 43%, agriculture and vegetation area declined by 8%, waterbodies decreased by 41%, and desert area decreased by 2% when compared with 2015. The change detection approach revealed that agricultural land was directly encroached on by rapidly increasing built-up area and urbanization as the area had an overall 19% rise in population growth within eight years with an annual growth rate of more than 3%. This study will be helpful to assess the quantity of spatial and temporal changes in the desert ecosystem, which is usually ignored by policymakers and governments due to less economic activity, although it plays a huge role in biodiversity conservation and balancing the regional ecosystem. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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15 pages, 3764 KiB

Open AccessArticle

Identifying Particulate Matter Variances Based on Environmental Contexts: Installing and Surveying Real-Time Measuring Sensors

by Eunseo Shin, Yeeun Shin, Suyeon Kim, Sangwoo Lee and Kyungjin An

Land 2023, 12(4), 872; https://doi.org/10.3390/land12040872 - 12 Apr 2023

Viewed by 988

Abstract

Previous research suggests that there should be environmental solutions for the emerging health threats caused by poor air quality, such as particulate matters (PM, including PM_2.5 and PM₁₀). Research related to air quality (measured by PM) using land-use regression and [...] Read more.

Previous research suggests that there should be environmental solutions for the emerging health threats caused by poor air quality, such as particulate matters (PM, including PM_2.5 and PM₁₀). Research related to air quality (measured by PM) using land-use regression and geographically weighted regression shows some patterns among different environmental contexts which could reduce the threats from such elements; however, there is little concrete evidence for such threats. To fill this research gap, this study installed real-time PM sensors at human breathing heights at five locations in Seoul, South Korea, and recorded the PM values collected between November 2021 and January 2023. Three-phase time-series analyses were conducted on the collected data. Lower levels of PM concentration were found in more enclosed spaces. In particular, when a space was surrounded by vegetation, the air quality significantly increased. The purpose of this study is to explore variations in air quality, particularly PMs densities, in different types of land use within urban areas such as Seoul. Greater metropolitan areas such as Seoul have a great number of health problems caused by air quality. This study’s results contribute to policy and decision-making in urban design to tackle such problems and to provide spatial guidelines for public health and welfare. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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17 pages, 1157 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Framework for Data-Driven Agent-Based Modelling of Agricultural Land Use

by Giacomo Ravaioli, Tiago Domingos and Ricardo F. M. Teixeira

Land 2023, 12(4), 756; https://doi.org/10.3390/land12040756 - 27 Mar 2023

Cited by 5 | Viewed by 3888

Abstract

Agent-based models (ABMs) are particularly suited for simulating the behaviour of agricultural agents in response to land use (LU) policy. However, there is no evidence of their widespread use by policymakers. Here, we carry out a review of LU ABMs to understand how [...] Read more.

Agent-based models (ABMs) are particularly suited for simulating the behaviour of agricultural agents in response to land use (LU) policy. However, there is no evidence of their widespread use by policymakers. Here, we carry out a review of LU ABMs to understand how farmers’ decision-making has been modelled. We found that LU ABMs mainly rely on pre-defined behavioural rules at the individual farmers’ level. They prioritise explanatory over predictive purposes, thus limiting the use of ABM for policy assessment. We explore the use of machine learning (ML) as a data-driven alternative for modelling decisions. Integration of ML with ABMs has never been properly applied to LU modelling, despite the increased availability of remote sensing products and agricultural micro-data. Therefore, we also propose a framework to develop data-driven ABMs for agricultural LU. This framework avoids pre-defined theoretical or heuristic rules and instead resorts to ML algorithms to learn agents’ behavioural rules from data. ML models are not directly interpretable, but their analysis can provide novel insights regarding the response of farmers to policy changes. The integration of ML models can also improve the validation of individual behaviours, which increases the ability of ABMs to predict policy outcomes at the micro-level. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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13 pages, 3724 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Global Maps of Agricultural Expansion Potential at a 300 m Resolution

by Mirza Čengić, Zoran J. N. Steinmann, Pierre Defourny, Jonathan C. Doelman, Céline Lamarche, Elke Stehfest, Aafke M. Schipper and Mark A. J. Huijbregts

Land 2023, 12(3), 579; https://doi.org/10.3390/land12030579 - 28 Feb 2023

Cited by 3 | Viewed by 2602

Abstract

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing [...] Read more.

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using artificial neural network (ANN) models relating locations of recent past conversions (2007–2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil, and accessibility. Cross-validation of the models indicated good performance with area under the curve (AUC) values of 0.88–0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83–0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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17 pages, 5196 KiB

Open AccessArticle

A Positive-Unlabeled Learning Algorithm for Urban Flood Susceptibility Modeling

by Wenkai Li, Yuanchi Liu, Ziyue Liu, Zhen Gao, Huabing Huang and Weijun Huang

Land 2022, 11(11), 1971; https://doi.org/10.3390/land11111971 - 04 Nov 2022

Cited by 3 | Viewed by 1241

Abstract

Flood susceptibility modeling helps understand the relationship between influencing factors and occurrence of urban flooding and further provides spatial distribution of flood risk, which is critical for flood-risk reduction. Machine learning methods have been widely applied in flood susceptibility modeling, but traditional supervised [...] Read more.

Flood susceptibility modeling helps understand the relationship between influencing factors and occurrence of urban flooding and further provides spatial distribution of flood risk, which is critical for flood-risk reduction. Machine learning methods have been widely applied in flood susceptibility modeling, but traditional supervised learning requires both positive (flood) and negative (non-flood) samples in model training. Historical flood inventory data usually contain positive-only data, whereas negative data selected from areas without flood records are prone to be contaminated by positive data, which is referred to as case-control sampling with contaminated controls. In order to address this problem, we propose to apply a novel positive-unlabeled learning algorithm, namely positive and background learning with constraints (PBLC), in flood susceptibility modeling. PBLC trains a binary classifier from case-control positive and unlabeled samples without requiring truly labeled negative data. With historical records of flood locations and environmental covariates, including elevation, slope, aspect, plan curvature, profile curvature, slope length factor, stream power index, topographic position index, topographic wetness index, distance to rivers, distance to roads, land use, normalized difference vegetation index, and precipitation, we compared the performances of the traditional artificial neural network (ANN) and the novel PBLC in flood susceptibility modeling in the city of Guangzhou, China. Experimental results show that PBLC can produce more calibrated probabilistic prediction, more accurate binary prediction, and more reliable susceptibility mapping of urban flooding than traditional ANN, indicating that PBLC is effective in addressing the problem of case-control sampling with contaminated controls and it can be successfully applied in urban flood susceptibility mapping. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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22 pages, 5297 KiB

Open AccessArticle

The Trade-Offs/Synergies and Their Spatial-Temporal Characteristics between Ecosystem Services and Human Well-Being Linked to Land-Use Change in the Capital Region of China

by Mengxue Liu, Xiaobin Dong, Xuechao Wang, Bingyu Zhao, Hejie Wei, Weiguo Fan and Chenyang Zhang

Land 2022, 11(5), 749; https://doi.org/10.3390/land11050749 - 19 May 2022

Cited by 9 | Viewed by 2606

Abstract

With the rise of the strategy of Coordinated Development for the Beijing-Tianjin-Hebei Region, it is necessary to evaluate the trade-offs/synergies of the survival environment and human well-being in Hebei, the capital region of China. However, existing methods cannot analyze and express trade-offs/synergies of [...] Read more.

With the rise of the strategy of Coordinated Development for the Beijing-Tianjin-Hebei Region, it is necessary to evaluate the trade-offs/synergies of the survival environment and human well-being in Hebei, the capital region of China. However, existing methods cannot analyze and express trade-offs/synergies of two or more variables simultaneously. Therefore, this paper proposes a new framework to express the trade-offs/synergies among land-use intensity, ecosystem services, and human well-being. In this paper, we first identified the land-use intensity change and land-use transformation and evaluated ecosystem services and human well-being in Hebei from 2000–2015 under the Millennium Ecosystem Assessment framework. Then, the trade-offs/synergies of the three indicators were determined by GIS-based methods and MATLAB. The results show that land-use intensity and human well-being mainly present a synergistic relationship, while ecosystem services and land-use intensity mainly present a trade-off relationship, and ecosystem services and human well-being also present a trade-off relationship in Hebei during 2000–2015. In addition, some regional solutions to achieve sustainable development were proposed: region 1 needs to adjust land-use structure, region 2 needs to protect the ecological environment to improve the supply of ecosystem services, and region 3 needs to commit to improving the regional comprehensive human well-being. This study not only proposes a new framework for analyzing trade-offs/synergies of land use intensity, ecosystem services, and human well-being, but it also provides regional solutions for Hebei to achieve sustainable development. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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32 pages, 12302 KiB

Open AccessArticle

Spatio-Temporal Evolution Characteristics and Influencing Factors of Urban Service-Industry Land in China

by Sidong Zhao, Kaixu Zhao, Yiran Yan, Kai Zhu and Chiming Guan

Land 2022, 11(1), 13; https://doi.org/10.3390/land11010013 - 22 Dec 2021

Cited by 26 | Viewed by 3339

Abstract

The level of service-industry development has become an important symbol of the competitiveness and influence of cities. The study of the dynamic evolution characteristics and patterns of urban service-industry land use, the driving factors and their interactions is helpful to provide a basis [...] Read more.

The level of service-industry development has become an important symbol of the competitiveness and influence of cities. The study of the dynamic evolution characteristics and patterns of urban service-industry land use, the driving factors and their interactions is helpful to provide a basis for decision making in policy design and land use planning for the development of service economies. In this study we have conducted an empirical study of China, based on the methods of spatial cold- and hot-spot analysis, Tapio’s decoupling model, and GeoDetector. We found that: (1) the scales of land use, output efficiencies and development intensities of service-industries are increasing with a trend that takes the form of a “J”, “U” and “inverted U”, respectively; (2) Spatial variabilities and agglomerations are significant, with a stable spatial pattern of the scale of service-industry land use, and a gradient in the distribution of cold- and hot-spots. The dominant spatial units of output efficiency and development intensity have changed from low and lower to high and higher, and the cold- and hot-spots gather in clusters; (3) The development of service-industries is highly dependent on the input of land-resources, and only a few provinces are in a state of strong decoupling, while most are in a state of weak decoupling, with quite a few still in a state of expansive coupling, expansive negative decoupling, or even strong negative decoupling; (4) There are many driving factors for land use changes in the service-industry, with increasingly complicated and diversified relationships between each other, ranked in intensity as the scale effect > informatization > globalization > industrialization > urbanization. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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21 pages, 7248 KiB

Open AccessArticle

A Fractal Approach to Urban Boundary Delineation Based on Raster Land Use Maps: A Case of Shanghai, China

by Chong Zhao, Yu Li and Min Weng

Land 2021, 10(9), 941; https://doi.org/10.3390/land10090941 - 07 Sep 2021

Cited by 7 | Viewed by 2263

Abstract

Given the diverse socioecological consequences of rapid urban sprawl worldwide, the delineation and monitoring of urban boundaries have been widely used by local governments as a planning instrument for promoting sustainable development. This study demonstrates a fractal method to delineate urban boundaries based [...] Read more.

Given the diverse socioecological consequences of rapid urban sprawl worldwide, the delineation and monitoring of urban boundaries have been widely used by local governments as a planning instrument for promoting sustainable development. This study demonstrates a fractal method to delineate urban boundaries based on raster land use maps. The basic logic is that the number of built-up land clusters and their size at each dilation step follows a power-law function. It is assumed that two spatial subsets with distinct fractal characteristics would be obtained when the deviation between the dilation curve and a straight line reaches the top point. The top point is regarded to be the optimum threshold for classifying the built-up land patches, because the fractality of built-up land would no longer exist beyond the threshold. After that, all the built-up land patches are buffered with the optimum threshold and the rank-size distribution of new clusters can be re-plotted. Instead of artificial judgement, hierarchical agglomerative clustering is utilized to automatically classify the urban and rural clusters. The approach was applied to the case of Shanghai, the most rapidly urbanizing megacity in China, and the dynamic changes of the urban boundaries from 1994 to 2016 were analyzed. On this basis, urban–rural differences were further explored through several fractal or nonfractal indices. The results show that the proposed fractal approach can accurately distinguish the urban boundary without subjective choice of thresholds. Extraordinarily different fractal dimensions, aggregation and density and similar average compactness were further identified between built-up land in urban and rural areas. The dynamic changes in the urban boundary indicated rapid urban sprawl within Shanghai during the study period. In view of the popularization and global availability of raster land use maps, this paper adds fuels to the cutting-edge topic of distinguishing the morphological criteria to universally describe urban boundaries. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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17 pages, 3244 KiB

Open AccessArticle

Improving an Urban Cellular Automata Model Based on Auto-Calibrated and Trend-Adjusted Neighborhood

by Xinhao Pan, Zichen Wang, Miao Huang and Zhifeng Liu

Land 2021, 10(7), 688; https://doi.org/10.3390/land10070688 - 30 Jun 2021

Cited by 8 | Viewed by 1918

Abstract

Accurately simulating urban expansion is of great significance for promoting sustainable urban development. The calculation of neighborhood effects is an important factor that affects the accuracy of urban expansion models. The purpose of this study is to improve the calculation of neighborhood effects [...] Read more.

Accurately simulating urban expansion is of great significance for promoting sustainable urban development. The calculation of neighborhood effects is an important factor that affects the accuracy of urban expansion models. The purpose of this study is to improve the calculation of neighborhood effects in an urban expansion model, i.e., the land-use scenario dynamics-urban (LUSD-urban) model, by integrating the trend-adjusted neighborhood algorithm and the automatic rule detection procedure. Taking eight sample cities in China as examples, we evaluated the accuracies of the original model and the improved model. We found that the improved model can increase the accuracy of simulated urban expansion in terms of both the degree of spatial matching and the similarity of urban form. The increase of accuracy can be attributed to such integration comprehensively considers the effects of historical urban expansion trends and the influences of neighborhoods at different scales. Therefore, the improved model in this study can be widely used to simulate the process of urban expansion in different regions. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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19 pages, 6571 KiB

Open AccessArticle

Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

by Yabo Zhao, Dixiang Xie, Xiwen Zhang and Shifa Ma

Land 2021, 10(6), 633; https://doi.org/10.3390/land10060633 - 15 Jun 2021

Cited by 11 | Viewed by 2947

Abstract

Urban agglomeration is an important spatial organization mode in China’s attempts to attain an advanced (mature) stage of urbanization, and to understand its consequences, accurate simulation scenarios are needed. Compared to traditional urban growth simulations, which operate on the scale of a single [...] Read more.

Urban agglomeration is an important spatial organization mode in China’s attempts to attain an advanced (mature) stage of urbanization, and to understand its consequences, accurate simulation scenarios are needed. Compared to traditional urban growth simulations, which operate on the scale of a single city, urban agglomeration considers interactions among multiple cities. In this study, we combined a spatial Markov chain (SMC) (a quantitative composition module) with geographically weighted regression-based cellular automata (GWRCA) (a spatial allocation module) to predict urban growth in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), an internationally important urban agglomeration in southern China. The SMC method improves on the traditional Markov chain technique by taking into account the interaction and influence between each city to predict growth quantitatively, whereas the geographically weighted regression (GWR) gives an empirical estimate of urban growth suitability based on geospatial differentiation on the scale of an urban agglomeration. Using the SMC model to forecast growth in the GBA in the year 2050, our results indicated that the rate of smaller cities will increase, while that of larger cities will slow down. The coastal belt in the core areas of the GBA as well as the region’s peripheral cities are most likely to be areas of development by 2050, while established cities such as Shenzhen and Dongguan will no longer experience rapid expansion. Compared with traditional simulation models, the SMC-GWRCA was able to consider spatiotemporal interactions among cities when forecasting changes to a large region like the GBA. This study put forward a development scenario for the GBA for 2050 on the scale of an urban agglomeration to provide a more credible scenario for spatial planning. It also provided evidence in support of using integrated SMC-GWRCA models, which, we maintain, offer a more efficient approach for simulating urban agglomeration development than do traditional methods. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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19 pages, 6614 KiB

Open AccessArticle

Commercial Classification and Location Modelling: Integrating Different Perspectives on Commercial Location and Structure

by Rui Colaço and João de Abreu e Silva

Land 2021, 10(6), 567; https://doi.org/10.3390/land10060567 - 28 May 2021

Cited by 6 | Viewed by 2921

Abstract

Commercial classification is essential to describe and compare the spatial patterns of commercial activity. Most classification systems consider a large set of dimensions that include detailed features such as store ownership or development type. Since new business models are continually being developed, the [...] Read more.

Commercial classification is essential to describe and compare the spatial patterns of commercial activity. Most classification systems consider a large set of dimensions that include detailed features such as store ownership or development type. Since new business models are continually being developed, the need to revise classification systems is constant. This makes generalisation hard, thus hindering the comparison of commercial structures in different places and periods. Recent studies have focused on cluster analysis and a smaller number of variables to gain insights into commercial structures, directly addressing this issue. Systematic bottom-up classification generates comparable structures, which is essential to contrast policy results in different situations. Furthermore, since form or accessibility are usually considered in classifications, cluster membership is precluded from most retail location models, often relying on the latter as an explanatory variable. Hence, a new classification system is proposed, based on cluster analysis (k-means) and a minimal set of variables: density, diversity, and clustering. This classification was implemented in 1995, 2002, and 2010 in Lisbon. Cross-sectional analysis of the commercial structures shows the system accurately describes commercial location and change, suggesting it can be generalised as a classification system. Since the minimal dataset also allows for cluster membership to be used on location models, the relationship between commercial classification and location modelling could be strengthened, reinforcing the role of commercial studies in urban planning and policymaking. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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Review

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21 pages, 4540 KiB

Open AccessReview

Integrating Ecosystem Services into Planning Practice: Situation, Challenges and Inspirations

by Linrun Qiu, Yuxiang Dong and Hai Liu

Land 2022, 11(4), 545; https://doi.org/10.3390/land11040545 - 08 Apr 2022

Cited by 8 | Viewed by 2644

Abstract

Ecosystem services (ES)-related decision-making is important to promote sustainable conservation and urban development. However, there is limited information regarding the use of ES research in a planning context. We explored this gap between ES research and planning practice by evaluating whether and to [...] Read more.

Ecosystem services (ES)-related decision-making is important to promote sustainable conservation and urban development. However, there is limited information regarding the use of ES research in a planning context. We explored this gap between ES research and planning practice by evaluating whether and to what extent the ES concept is explicitly used in planning and decision-making processes. This paper selected 101 pieces of target literature, reviewed their research status and characteristics, discussed the motivation and interests, and summarized the research content. In particular, we discussed the contributions that demonstrated the significance of incorporating ES into planning and achieved beneficial results. A series of abstract strategic methods and quantitative methodological approaches were used for subsequent reference research. The ES concept existed earlier than the perception in early-stage planning documents, while its practical application was superficial, with insufficient depth, which was a challenge worthy of attention. To identify the research paradigm in previous planning related to ES, we found that ES analyses for planning were largely theory-inspired, rather than practice-inspired, and used the Schön–Stokes model of the wicked and tame to theorize problems in socio-ecological systems. Our study highlighted that Pasteur’s paradigm may be an essential and useful research style for maintaining and improving ES in socio-ecological practice. Full article

(This article belongs to the Special Issue New Approaches to Land Use/Land Cover Change Modeling)

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