land Review Land Change Science and the STEPLand Framework: An Assessment of Its Progress Pere Serra 1, * and Luca Salvati 2 1 Grumets Research Group, Departament of Geography, Building B, Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain 2 Department of Methods and Models for Economics, Territory and Finance, Faculty of Economics, Sapienza University of Rome, Via del Castro Laurenziano 9, I-00161 Rome, Italy; luca.salvati@uniroma1.it * Correspondence: pere.serra@uab.cat Abstract: This contribution assesses a new term that is proposed to be established within Land Change Science: Spatio-TEmporal Patterns of Land (‘STEPLand’). It refers to a specific workflow for analyzing land-use/land cover (LUC) patterns, identifying and modeling driving forces of LUC changes, assessing socio-environmental consequences, and contributing to defining future scenarios of land transformations. In this article, we define this framework based on a comprehensive meta- analysis of 250 selected articles published in international scientific journals from 2000 to 2019. The empirical results demonstrate that STEPLand is a consolidated protocol applied globally, and the large diversity of journals, disciplines, and countries involved shows that it is becoming ubiquitous. In this paper, the main characteristics of STEPLand are provided and discussed, demonstrating that the operational procedure can facilitate the interaction among researchers from different fields, and communication between researchers and policy makers. Keywords: In-deep reading analysis; LUCC patterns; spatial modeling of driving forces; socio-environmental consequences; future scenarios Citation: Serra, P.; Salvati, L. Land Change Science and the STEPLand Framework: An Assessment of Its 1. Introduction Progress. Land 2022, 11, 1065. Landscape and ecosystem transformations, and their implications for global envi- https://doi.org/10.3390/ ronmental change and sustainability, are a major research challenge for both ecological land11071065 and social sciences [1]. As a result, the analysis of changes in land use and land cover is Academic Editor: Le Yu considered essential for researching major environmental issues such as desertification, Received: 10 June 2022 eutrophication, acidification, greenhouse effects, biodiversity loss, and climate warming [2]. Accepted: 9 July 2022 The progress in these studies was extensively coordinated from 1994 to 2005 by the Land- Published: 13 July 2022 Use and Land-Cover Change (LUCC) core project of the International Geosphere-Biosphere Programme (IGBP) and the International Human Dimensions Programme (IHDP) [3,4], Publisher’s Note: MDPI stays neutral which represent the starting point of other global and regional initiatives, such as the ESA with regard to jurisdictional claims in GlobCover [5] and the NASA LCLUC Programme [6]. In 2006, an even broader initiative, published maps and institutional affil- the Global Land Project, was established with the aim of synthesizing and integrating iations. insights, knowledge, and research methodologies, thereby identifying scientific priorities and a research agenda [7]. Two major challenges faced by this initiative were (i) a refined un- derstanding of complex feedbacks between societal and environmental components of the Copyright: © 2022 by the authors. integrated land system, and (ii) up-scaling local and regional processes of change to reach a Licensee MDPI, Basel, Switzerland. “truly global” understanding [8]. Consequently, a new research field, called Land Change This article is an open access article Science (LCS), has attracted increasing interest and efforts to better understand LUCC distributed under the terms and patterns and dynamics that affect the structure and functioning of Earth systems [9]. This conditions of the Creative Commons perspective requires using specific analysis tools, e.g., Geographical Information Systems Attribution (CC BY) license (https:// (GIS), to integrate specific knowledge in socio-economic and ecological subsystems. creativecommons.org/licenses/by/ LCS has carried out research at many spatial and temporal scales with the objectives 4.0/). of explaining human–environmental system dynamics that generate landscape changes; Land 2022, 11, 1065. https://doi.org/10.3390/land11071065 https://www.mdpi.com/journal/land Land 2022, 11, 1065 2 of 38 improving spatially explicit LUCC models that are compatible with Earth systems mod- els; and, finally, assessing system intrinsic properties and the related outcomes, such as vulnerability, resilience, and sustainability [1]. The refined analysis of interactions be- tween biophysical and socio-economic subsystems has implied the active involvement of researchers from natural, social, and spatial disciplines. The complexity of integrating diverse data, space–time patterns, and socio-biophysical processes has led to some method- ological issues related to merging analytical traditions, mainly when people, places and environment are linked [10]. Spatial accuracy and uncertainty, trans-scalar mismatches, and the choice of aggregation levels (from county/district to households/individuals) are examples of these issues [11]. A seminal contribution by [7] defined LCS as an interdisciplinary research field that engages scientists across the social, economic, geographical, and natural sciences, and is conceptually and operationally separate from the broader discipline of Land System Science. This distinction was motivated by the increasing interest in (i) the role of drivers and impacts, (ii) the interactions between socio-ecological systems, and (iii) the connection between world regions, cities, and their rural hinterlands. Changes over time in human interactions with the natural surroundings, including land management and the provi- sioning of ecosystem services, are the focus of emerging socio-ecological interpretative paradigms [8]. The usual aims of LUCC studies are to identify drivers of change and, based on this knowledge, envisage future scenarios, proposed in both individual (local) cases and more globalized research. Nevertheless, generalization and validation processes can sometimes be difficult due to the diversity of driving forces, study areas, indicators, and modeling approaches [12]. Giving value to long-lasting research, LCS is a (rapidly evolving) disciplinary field that has produced a wealth of methodological innovations and empirical observations when it has been used to assess and interpret LUCC patterns, drivers, and inter-linkages [13]. From this perspective, its development has been characterized by a focus on local case studies and a specific emphasis on methodological developments that have improved remote sensing tools and geo-spatial analysis. It has also promoted the adoption of theoretical and empirical frameworks derived from disciplines such as geography, landscape ecology, and regional science. After more than 25 years of LUCC research, our hypothesis is that the progress of LCS seems unquestionable in many social and natural disciplines, and it has become relatively ubiquitous because it is applied by experts worldwide. Based on an original meta-analysis of selected literature, our main objective is to assess a specific workflow resulting from LCS that we propose to call STEPLand (Spatio-TEmporal Patterns of Land). By delineating specific analysis criteria of the reference sample, we summarize the main attributes, data typology, and methodological steps, in addition to technical details and issues of practical applications, typical of LCS approaches. Our review reports on the reliability, robustness, and global applicability of operational definitions and frameworks oriented toward the STEPLand perspective, demonstrating the specific contribution of this approach to selecting appropriate methods and models for LUCC research. 2. STEPLand Framework STEPLand is defined as an operation framework typical of LCS, and composed of four steps, moving from broader to narrower epistemological perspectives (Figure 1): 1. LUCC patterns and dynamics. The main objective of the first step is LUCC assess- ment using, e.g., remote techniques and geo-spatial approaches, and answering, at least, the following questions: “What LUC are changing?”, “Where?”, “When?”. 2. LUCC modeling. The main objective is to describe the core drivers of LUCC using qualitative or quantitative methods and answering the following questions: “Which causal factors are involved in LUC changes?”, “Why?”, “How?”. 3. Assessment of socio-environmental consequences. The aim is to delineate and un- derstand the socio-environmental consequences of LUCC using qualitative or quantitative Land 2022, 11, 1065 3 of 38 methodologies, and explicitly considering landscape transformations and ecosystem service Land 2022, 11, x FOR PEER REVIEWdynamics to answer the question: “And then?”. 4. Compiling future scenarios. The goal of this last step is to establish future LUCC projections, answering questions such as “What happens if ( . . . )?”. Figure Figure 1. 1. A workflow A workflow fromfrom broader broader to narrower to narrower knowledge knowledge issuesthe issues illustrating illustrating STEPLand the per- STEPLa perspective. spective. Source:Source: own elaboration. own elaboration. 2.1. LUCC Patterns and Dynamics 1. first The LUCCstep ofpatterns STEPLandand dynamics. includes The main a formal assessment andobjective of the characterization first step i of LUCC, assessment mainly based on using, Earthe.g., remote(EO) Observation techniques techniques, and geo-spatial e.g., remote sensingapproaches, (RS). EO andand answ GIS data facilitate trans-sectoral research and provide a platform least, the following questions: “What LUC are changing?”, “Where?”, “When?”. for integrating multiple information 2. LUCC layersmodeling. that may include The mainancillary (or validation) objective data [14,15]. is to describe theRScore toolsdrivers provide of LUC valuable multitemporal data for monitoring LUCC patterns and processes at different qualitative or quantitative methods and answering the following questions: spatial scales (local, regional, national, continental, and global), and GIS techniques make it causal tofactors possible analyzeare theminvolved explicitlyin LUCSpatial, [16–18]. changes?”, spectral, “Why?”, “How?”. and radiometric RS resolutions 3. Assessment affect the size, condition,of andsocio-environmental precision of the explicitconsequences. The aim isin to features to be discriminated a deline landscape scene, whereas the temporal resolution of understand the socio-environmental consequences of LUCC using qualit satellite imagery determines the in- trinsic territorial, environmental, and socio-economic dynamics of local systems [10,19–21]. quantitative methodologies, and explicitly considering landscape transformati Both dimensions represent large technical challenges. During the past 40 years, significant ecosystem advances service in sensor dynamicshave technologies to answer improved thethequestion: “And then?”. spatial, spectral, radiometric, and temporal4. Compiling resolutions, and future scenarios. the coverage, The goal of satellite imagery of this lastclassification [22]. The step is to establish phase is futur the process of identifying projections, answering spectral similarities questions suchinasthe“What multidimensional happens ifspectral (…)?”.space and linking them to LUC categories. This involves assigning pixels to different LUC classes identified in a given study. Diverse classification taxonomy options are now available but, 2.1. LUCC Patterns and Dynamics traditionally, these have been divided according to the pixel technique used (per-pixel or The or sub-pixel), first step of according to STEPLand the application includes of traininga formal samples assessment (supervised methods and characteriz or unsupervised methods), among others [23–25]. LUCC, mainly based on Earth Observation (EO) techniques, e.g., remote sensing Similarity in spectral reflectance properties of natural surfaces at a given moment often and GIS data facilitate trans-sectoral research and provide a platform for int impedes consistent identification and mapping of a large range of LUC, such as agricultural multiple crops information or individual layers constituted communities that may by include natural ancillary vegetation.(or validation) Furthermore, thedata [14 tools provide spectral responsevaluable of many cover multitemporal data for monitoring types varies throughout the year: LUC LUCC patterns categories that and p appear similar in spring may become distinguishable at earlier at different spatial scales (local, regional, national, continental, and global),or later stages of the annual cycle. Therefore, techniques a multi-temporal make it possibleapproachto analyzebased on themlargeexplicitly RS databases is more suitable [16–18]. Spatial, spect for revealing the complex LUC patterns characteristic, for instance, of the Mediterranean radiometric Basin [26–28]. In RSorder resolutions to minimize affect the size, condition, the classification errors dueand precision to seasonal of theanexplicit changes, to be discriminated appropriate strategy is to in a landscape select images fromscene, the same whereas month or,the temporal at least, from the resolution same of imagery season determines [29,30]. the intrinsic After a classification territorial, is obtained, the nextenvironmental, step is to assess theand socio-economic accuracy or how d of local systems [10,19–21]. Both dimensions represent large technical challenges the past 40 years, significant advances in sensor technologies have improved the spectral, radiometric, and temporal resolutions, and the coverage, of satellite imag Land 2022, 11, 1065 4 of 38 well a classification has worked. This assessment measures the correlation between satellite imagery classification and ground reference samples to quantify the overall agreement between RS classification (global accuracy and user and producer accuracy by categories) and ground truthing data [31]. Once LUC maps are generated and tested, among the available change detection tech- niques, the most usual method is the post-classification or map-to-map comparison (i.e., a comparative analysis of independently produced LUC classifications from different dates) associated with a transition or change matrix [32,33]. According to [34], post-classification is an accurate procedure that bypasses the difficulties associated with analyzing images acquired at different times of the year or by different sensors, and thus delineating the nature of change [35]. Nevertheless, this method has two well-known critical issues [36,37]: first, misregistration of the polygon boundaries (locational inaccuracy) in the different classifications, and, therefore, the presence of border pixels with false positive or negative changes. With rasters, this is basically the consequence of non-matching pixels [38]; with vector shapes, this problem is known as “sliver”, i.e., narrow polygons of uncertain inter- pretation [39–41]. The second issue relates to problems derived from classification errors: a false positive change may be recorded when no change has taken place because a pixel in one map (or in both maps) is misclassified, or false negative changes, when no change is identified but a change has taken place in the field. Therefore, this approach requires a good accuracy level in both classification maps because the change map accuracy is the product of the accuracies of the individual classifications, and therefore, it is subject to error propagation [42–44]. 2.2. LUCC Modeling Modeling is a process that provides a platform for encoding inferred (or deduced) re- lationships, thus allowing simulations and projections based on mathematical (algorithmic) specifications or procedures [45]. LUCC modeling is a tool for supporting planners and policymakers in developing robust policies and decisions. At the same time, models can be used to provide an ex ante assessment of policies or serve as an early warning system for environmental impacts. Six conceptual dimensions are considered to be particularly important for carrying out LUCC modeling [46]: (i) analysis level, (ii) cross-scale dynamics, (iii) driving forces, (iv) spatial interactions and neighborhood effects, (v) temporal dynam- ics, and (vi) level of data integration. In general terms, LUCC is often modeled as a function of a selection of socio-economic and biophysical variables acting as driving forces that shape land change [2,47–49]. Modeling LUCC driving forces comprises a wide variety of methodological ap- proaches, which can be classified in different ways. For instance, [3] divided driving forces into three groups: socio-economic drivers, biophysical drivers, and land manage- ment variables; [50] differentiated proximate causes, those actions that directly affect land use, such as wood extraction, from underlying causes, i.e., those “fundamental forces” that underpin the proximate causes, including demographic, economic, technological, institutional, and cultural factors [51]. In general, Ref. [52] identified five driving forces: (i) biophysical constraints and potentials, (ii) economic factors, (iii) social factors, (iv) spa- tial policies, and (v) spatial interactions and neighborhood characteristics. Others [48,53,54] differentiated actors of change from driving forces. Actors are the decision-making and me- diating agents, including individuals (e.g., farmers), households, neighborhoods, agencies (e.g., planning organizations), and institutions, whereas driving forces are the (sometimes materialized) expression of their decisions or acts, e.g., through laws, subsidies, or incen- tives [55]. Moreover, Ref. [53] provided a specific definition of the “spatial domain” as the institutional (and geographical) context where a given agent interacts with the landscape (e.g., countries, regions, prefectures/provinces, municipalities). Representation of the domain can be facilitated in a geographically explicit model using boundary maps or vector layers. The extent of the study area also influences the selection: larger areas imply a diversification of LUC contexts and may require analyzing a larger variety of Land 2022, 11, 1065 5 of 38 driving forces. Therefore, the analysis scale (local, regional, national, or global) can produce a specific representation based on different driving forces. For instance, the presence of small ecologically valuable areas can be the main determinant of LUCC patterns at a local scale, whereas the distance from the market can be a more important factor at a regional scale [46]. Specification and quantification of the intrinsic relationship between driving forces and LUCC are particularly important in model implementation. According to [56], modelers should select the drivers or explanatory variables that are supposed to play an active role in land change. Even in automated approaches, input variables are selected based on expert knowledge, although data availability (e.g., the lack of data for some economic variables, such as land ownership, or, for earlier times, e.g., gross domestic product in 1850) is often a major limitation, as documented in [57]. According to [45], models used for LUCC analysis range from those oriented to- wards pattern description/recognition to those quantifying and interpreting dynamics. One study [58] provided a more generalized classification, arguing that models can be static or dynamic, spatial or non-spatial, inductive or deductive, and/or agent-based or pattern-based (e.g., emulation of individual decision makers vs. inference of the underlying behavior derived from LUCC patterns). Models can use a large range of information (satellite imagery, official statistics, maps or field surveys, among others) often implemented in GIS and eventually combined in composite indicators. Actor- based, bottom-up models based on household surveys represent the land change agent explicitly by emulating individual decision makers through agent-based approaches. Land evaluation pattern-based top-down models use RS and census data to simulate LUCC through parameterized transition equations that convert land from one cover type to another [59]. Finally, Ref. [60] separated inductive pattern-based models from cellular automata approaches, sector-based economic models, spatially disaggregated economic approaches, and agent-based models. Therefore, driving forces form a tangled system of interactions that affect multiple temporal and spatial levels, making it difficult to carry out adequate analyses and ob- tain representation systems. Combining data from the social and natural sciences is a particularly complicated task due to the different operational scales, the complexity in relating social science data to a specific geographic place, and the difficulty involved in integrating qualitative data, which is more common in social science, and less common in ecological disciplines [47]. Moreover, driving forces—and not only LUCC—are also subject to changes, which influence the identification of representative study periods, and thus affects the model’s results [61]. Precision of statistical methodologies and data availability are also important. As a summary, identification of LUCC drivers (sensu [26]) implies (i) the clarification of latent relationships between landscape patterns and driving forces (explorative models), and (ii) the projection of future landscapes under different scenarios (predictive models). Two of the main issues arising when socio-economic and biophysical variables are combined spatially, as a process of data integration or data equalization [62–64] charac- teristic of STEPLand, are the different data formats and spatial scales. Socio-economic data are usually available from official statistics in tabular format at some administrative boundaries: neighborhoods, districts, census tracks, municipalities, regions, and countries, among others [65,66]. By comparison, biophysical variables are mainly extracted from EO sources, from spatial interpolation techniques (e.g., exploiting point data derived from climate stations) or from other sources (e.g., rasterization of archive maps) having a specific pixel size [67]. When the two types of data are combined, these issues may lead to a loss in spatial precision [68], given the need to integrate data at one specific scale, namely, according to administrative boundaries (i.e., native vector file) or to lattices (i.e., raster file with a given pixel size). As socio-economic data are mostly available for administrative areas, an option is to convert biophysical data into class intervals and calculate the area (or percentage) occupied by each interval within the appropriate spatial (polygon) domain. Land 2022, 11, 1065 6 of 38 For instance, Ref. [69] adopted the area option, whereas [70] calculated the proportion of three different intervals of slopes within each district, and adopted a similar solution for soil moisture. Another study [71] clipped LUC data to the boundaries of each of the 25 watersheds considered in the study, and then calculated zonal statistics for each water- shed to extract the relative proportions of each LUC type for subsequent use in statistical analyses. Another option is to calculate the mean value of cardinal variables per spatial (polygon) domain, thus producing a high generalization. It is also possible to assign central (median) or dominant (mode) values for each administrative area. This latter option is appropriate for discrete variables, such as soil type. When the option is to work at a pixel size, the problem is the inverse, as it affects the socio-economic data: a rasterization is required in order to match the pixel size of biophysical variables and LUCC. The main issue in this case is whether a statistical anal- ysis is applied because all the pixels included in the administrative boundary have the same socio-economic value, showing a maximum spatial autocorrelation, which leads to a violation of the assumption of independent residuals. To minimize this situation, a convenient “solution” is to apply a “reduced factor” through, for instance, a stratified random sampling at a lower number of pixels. This option was applied in [72] using a “contraction factor” amounting to 10. Another option is “data generalization”, e.g., using a grid lattice having larger pixel sizes, which is representative of a lower spatial resolution; for example, in [73], all the variables describing deforestation and the respective drivers of change were aggregated to 25 km grid cells. 2.3. Socio-Environmental Consequences The third step of STEPLand takes the socio-environmental consequences of LUCC into account. For instance, LUCC may affect weather and climate variability by altering biophysical, biogeochemical, and energy exchange processes at local, regional, and global scales. Therefore, the consequences of these processes are scale-dependent because some of them affect the local environment (e.g., local water quality), whereas other impacts extend far beyond the location where they arise (e.g., carbon cycle, climate change). Because not all LUCCs have global effects, and LUCCs are not irreversible, there are several multi- directional impacts that can reinforce, mitigate, or offset multiple consequences, enriching the debate regarding on-site and off-site factors of change [74,75]. One specific repercussion considered in this review is related to landscape, given its particular nature because it contributes significantly to well-being and quality of life. Quantification of spatio-temporal landscape dynamics and the underlying drivers is key for planning appropriate decisions in this field [76]. Landscape metrics are common tools for measuring spatial changes in landscape composition and configuration, including fragmentation and diversity [77]. From a STEPLand perspective, they can be applied to RS and GIS data and simultaneously used with LUCC models and statistical methods. There are several quantitative measures, for example, those in [78], that can be used to assess landscape composition, such as the number of patches (patch richness) and uniformity and variety (evenness and diversity), and to assess the configuration or the spatial distribution of patches in the landscape (landscape pattern); these include patch shape, isolation, spread between classes (contagion), mean patch size, and density. Another additional consequence considered in STEPLand is the impact on ecosystem services because, in recent years, these services have attracted the increasing attention of researchers, policymakers, and other stakeholders worldwide [79]. The main reason for this is that, when land is used, society changes and modifies the quantity and quality of the provision of these services. According to [80], supply and demand of ecosystem services can be assessed at, and transferred to, different spatial and temporal scales by linking LUCC (e.g., extracted from remote sensing) with other data (e.g., obtained from interviews). Their results reveal patterns of human activities over time and space, and the capacities of different ecosystems to provide ecosystem services under changing LUC. However, Ref. [81] reviewed the “ecosystem services” concept and the various methods Land 2022, 11, 1065 7 of 38 applied for mapping and assessing quantitative methods, and the significant problem of there not being a clear distinction between services, functions, and benefits. 2.4. Futures Scenarios The last stage included in the STEPLand perspective corresponds to the simulation and prediction of future LUCC scenarios. This is a significant process for policy makers because it enables them to better anticipate actions, especially in the context of urban planning and the protection of natural land [82]. A previous study [83] provided a literature review of models applied to predict LUCC, including Markov chains (MCs), landscape models, CLUE- S models, cellular automata (CA), integration of Markov chains and cellular automata (MC-CA), and artificial neural networks. Markov chains, for instance, quantify LUCC probabilities between different states that are recorded in a transition matrix [84]. This matrix is the result of cross-tabulation between satellite images derived from two sequential dates, adjusted by proportional error and translated into a set of probability images, one for each LUC category [85,86]. In addition, CA models are one of the most relevant tools for understanding complex systems, particularly LUC patterns, given their intrinsic sensitivity to both spatial configuration and neighborhood relationships. The future state of the cells is determined by the current state of the cell itself and that of the neighborhood cells, following transition functions based on a set of rules. A variety of methods has been used for calculating transition rules in CA, such as logistic regression, multinomial logit, linear and geometric formulations, support vector machines, and, more recently, artificial neural networks [87]. 3. Methodology Results of the meta-analysis presented in this chapter aim to provide a snapshot of the STEPLand framework, rather than a complete and systematic overview of the whole body of literature, which is particularly vast. The chosen option was to review in great detail, from 2000 to 2019 (20 years of analysis), a relatively large sample of scientific articles, published in journals having an impact factor, with the aim of extracting key variables and attributes useful for assessing the evolution of the STEPLand framework. To identify articles representative of the STEPLand workflow (excluding books), we first selected published papers from the Web of Science database according to pre-determined criteria, such as target keywords including “land-use”, “land-cover”, and “changes”. The search engine reported a total of 9574 entries. Figure 2 shows the total number of publications by year, outlining an exponential increase since 2000. From the total sample, we selected 250 articles. This sample size was considered significant and representative given the deep reading required to extract the information that was the object of this review. These entries were extracted in four stages with the objective of assessing the STEPLand framework progress. These steps were to determine: (i) the presence of target keywords in the title or in the keywords; and (ii) the presence of the main objectives and applied methodology that are considered characteristic of STEPLand (based on a deep, manual reading). A third criterion was to choose papers published in different scientific journals and years, with the objective of determining the evolution of the STEPLand framework. In the last step, we chose papers with a high number of Web of Science citations (having at least 50 citations until 2007) (Figure 3). The full list of articles can be found in Appendix A. From the 250 publications selected, and after a deep review, the following data for the first step (LUCC patterns and dynamics) were extracted: time period; the country or countries of the study area; number of LUC categories; RS classification method, if applied, and its accuracy; and, finally, whether an overlay of individual classifications was applied (Table 1). EER REVIEW 8 of 38 R REVIEW 8 of 38 Land 2022, 11, 1065 8 of 38 Figure 2. Total number of articles identified by year from Web of Science as potential examples of Figure 2. Total number of articles identified by year from Web of Science as potential examples of the the STEPLand framework and the 250 manuscripts analyzed. Figure 2. Total number of articles STEPLand identified framework and the 250 bymanuscripts year from Web of Science as potential examples of analyzed. the STEPLand framework and the 250 manuscripts analyzed. Figure 3. Total number of citations per year. Figure 3. Total number of citations per year. In the second step (“LUCC modeling”), considering the previous work by [88], our Figure From 3. Totalthe number of citations classification 250 publications per year. of statistical and empirical selected, techniques and after a deepfor identifying review, thethemain driving forces following data for of LUCC included: type of analysis (exploratory, training, regression) and objective (data the first step (LUCC patterns reduction anddetection, or structure dynamics) were association, classification, extracted: time period; graphical the country or model, summarization From the 250 publications countries of the andstudy selected, area; number data transformation); and ofof LUC type after dependent a deep categories; review, the following RS classification and independent data for variables (drivingmethod, forces if considered); scale of analysis; and model type (Table 2). the first step (LUCC patterns and dynamics) were extracted: time period; the country or applied, and its accuracy; and, finally, whether an overlay of individual classifications countries of the was applied study (Table 1). area; number of LUC categories; RS classification method, if applied, and its accuracy; and, finally, whether an overlay of individual classifications Table was 1. Parameters applied collected in the first STEPLand step, corresponding to “LUCC patterns and (Table 1). dynamics”. Table 1. Parameters collected in the first STEPLand step, corresponding to “LUCC patterns and and Aim Parameters Reviewed dynamics”. Affiliation of first author or co-author Land 2022, 11, 1065 9 of 38 Table 1. Parameters collected in the first STEPLand step, corresponding to “LUCC patterns and dynamics”. Step STEPLand Aim Parameters Reviewed Affiliation of first author or co-author Journal of publication EO source: Land-cover maps produced by scientific or governmental agencies The same but combined with aerial photographs or remote sensing imagery Only aerial photographs Aerial photographs and remote sensing imagery Only remote sensing imagery Temporal extent: Up 10 years From 11 to 15 years From 16 to 20 years From 21 to 25 years From 26 to 30 years More than 30 years Country/ies of the study area Number of LULC categories: 1st LUCC patterns and dynamics From 1 to 5 From 6 to 10 From 11 to 15 From 16 to 20 More than 20 Classification method: Photointerpretation Pixel classification, differentiating unsupervised from supervised (maximum likelihood, support vector machine; kNN; decision tree or random forest; artificial neural network) Subpixel classification Object classification LULC map accuracy of automatic classifications: Between 60 and 70% Between 71 and 80% Between 81 and 90% More than 90% Overlay method In the third step, the main objective was to report the nature of the socio-environmental consequences of LUCC (e.g., water quality or climate), differentiating a quantitative spatial model from more qualitative patterns (Table 3). If the focus was on landscape, then two variables were analyzed: the software used, and the metrics quantified at class (or landscape) level. Finally, a third option was whether the focus was on ecosystem services (Table 3). In this case, various approaches have been applied, including market prices, productivity, and benefit transfer methods. This latter option is the procedure used to estimate the economic benefits gathered from one site (e.g., considering each biome or LUC type) and applying them to another site with related demographic, economic, and ecological characteristics [79]. The early valuation study by [89] used this approach to extrapolate the overall economic value of 17 ecosystem services provided by 16 main biomes. These estimates were updated based on a larger database of more than 300 case studies from all over the world [90]. Some value modifications are often made according to expert opinion surveys or based on statistical models [91]. Land 2022, 11, 1065 10 of 38 Table 2. Parameters collected in the second STEPLand step, corresponding to “LUCC modeling”. Step STEPLand Aim Definition Type and objective of modelling techniques: CLUE Exploratory data reduction Data transformation Data classification Data graphical model Data summarization Data training Regression Type of explanatory driving forces: Only biophysical or geophysical Only socioeconomic Socioeconomic and biophysical Socioeconomic, biophysical and distances 2nd LUCC modelling Biophysical and distances Type of dependent or response variables: LUC LUCC Spatial scale of analysis or domain: Plots, parcels o fields Grids, cells or pixels Administrative boundaries Causal nomenclature: Factors Causes Drivers Determinants Table 3. Parameters considered in the third step, corresponding to “Socio-environmental conse- quences of LUCC”. Step STEPLand Aim Definition Socio-environmental: Water quality Agriculture and forestry (agricultural land degradation) Biodiversity (habitats, native species, . . . ) Natural disasters (forest fires, droughts, land or soil degradation, . . . ) Climate 3rd LUCC consequences Weather Energy Landscape: Software used Main metrics at class level or/and landscape level Ecosystem services: Evaluation model Finally, in the fourth step, the future scenarios, the main objective was to report which forecasting model was applied and for which horizon (short, medium, or long-term). 4. Results The results from the entire sample of manuscripts (250) showed that the partial ap- proaches most common in STEPLand include step 1 and 2 together (LUCC patterns and modeling), followed by step 1 and 3 together (LUCC patterns and socio-environmental The results from the entire sample of manuscripts (250) showed that the 4. Results approaches most common in STEPLand include step 1 and 2 together (LUCC patter modeling), The followed results from by step the 1entire and 3 sample togetherof(LUCC patterns(250) manuscripts and socio-environ showed tha consequences), approaches mostand common step 1 alone (LUCC patterns) in STEPLand include (Figure step 1 4). and Only a few studies 2 together (LUCCa Land 2022, 11, 1065 11 of 38 a more complete framework, integrating step 1 with 2 and modeling), followed by step 1 and 3 together (LUCC patterns and socio-en 3 (13%); and only 4% sample adopted steps consequences), and 1, 2, and step 4 synergistically. 1 alone (LUCC patterns) Interestingly, (Figure 4). none Only of athefew250stud re papers included a more complete all the steps. consequences), and stepframework, 1 alone (LUCCintegrating step4).1Only patterns) (Figure with 2 and a few 3 (13%); studies adoptedand on a sample adopted more complete steps 1, framework, 2, and 4step integrating synergistically. Interestingly, 1 with 2 and 3 (13%); and only 4% none of theof the 2 sample adopted steps 1, 2, and papers included all the steps. 4 synergistically. Interestingly, none of the 250 reviewed papers included all the steps. Figure 4. Percentage of articles categorized according to STEPLand steps. Source: own elab 4.1. Step 1: LUC Changes Figure Figure Figure4. Percentage 4. Percentage 5 shows of of articles articles that 68.0% categorized categorized according according of the tofirst STEPLand to STEPLand steps. authors own steps. Source: affiliated were Source: elaboration. own with geog environmental sciences, forestry, ecology, rural development, natural resources, 4.1. Step 1: LUC Changes 4.1. Step management. 1: LUC Figure 5 shows Changes Therefore, that 68.0%STEPLand can of the first honestly authors werebe considered affiliated a framework with geography, en- that h followed vironmental mainly Figure 5 by sciences, geographers, forestry, shows ecology, that 68.0% environmentalists, rural development, and ecologists, natural of the first authors resources, since the pures or land were affiliated with management. scientists Therefore, STEPLand can honestly be considered a framework that has been environmental sciences, forestry, ecology, rural development, natural resou4 (including economists, for instance) were represented followed mainly by geographers, environmentalists, and ecologists, since the purest social in only manuscripts, management. scientists included (includingTherefore, in 32% economists, for of “Others” STEPLand instance) were category honestlyin canrepresented be in the figure. considered only Nonetheless, a framework 4.4% of manuscripts, thet papers followed included were in 32% published mainly incategory 65 scientific by geographers, of “Others” journals. Figure 6the in theenvironmentalists, figure. Nonetheless, shows and thepapers top ten ecologists, sample journals since were the thescientists (including economists, for instance) were represented in for most published publications, in 65 scientific totaling journals. 164 Figure papers 6 shows (65.6%); the top ten four journals journals having accounted the most on publications, whereas the totaling impact 164 papers factor (65.6%); four quartiles of journals all ten accounted were Q1 for Q2. and 71.9%, whereas the manuscripts, impact included factor quartiles inwere of all ten 32%Q1ofand “Others” Q2. category in the figure. Nonetheless papers were published in 65 scientific journals. Figure 6 shows the top ten jou the most publications, totaling 164 papers (65.6%); four journals accounte whereas the impact factor quartiles of all ten were Q1 and Q2. Figure 5. Affiliation discipline of the first author from all papers. Source: own elaboration. Land 2022, Land 11,11, 2022, x FOR PEER x FOR REVIEW PEER REVIEW 12 of 3812 o Land 2022, 11, 1065 12 of 38 Figure 5. Affiliation Figure discipline 5. Affiliation of the discipline offirst the author from from first author all papers. Source: all papers. own elaboration. Source: own elaboration. Figure 6. The top ten journals, totaling 164 papers, with most publications analyzed in this review. 6. The Figure Source: own Figure Thetop ten journals, totaling 164 papers, with most publications analyzed in this review. 6. elaboration. top ten journals, totaling 164 papers, with most publications analyzed in this revi Source: own elaboration. Source: own elaboration. From a geographical point point From a geographical of view, the the of view, results showed results showedthat that74 74countries wereincluded countries were included in theFrom reviewed articles. in the areviewed geographical Most American, articles.point European, of view, the Most American, and resultsand European, Asian showed countries were that 74 countries Asian countries represented, were include were represented, andtheAfrica and was the continent that was least represented (Figure 7). reviewed articles. Most American, European, and Asian countries were represen Africa was the continent that was least represented (Figure 7). and Africa was the continent that was least represented (Figure 7). 75° 75° 50° 50° 25° 25° 0° 0° -25° -25° -50° N -50° -75° 0 5500 N km -75° -175° -150° -125° -100° -75° -50° -25° 0° 25° 50° 075° 100° 5500 125° km 150° 175° Figure 7. Study Figure areas 7. Study per areas percountry country from the 250 from the 250papers papersanalyzed. analyzed. Source: Source: ownown elaboration. elaboration. -175° -150° -125° -100° -75° -50° -25° 0° 25° 50° 75° 100° 125° 150° 175° Figure FigureFigure8 documents 8 documents 7. Study that areas per that most of the most from country thethe250 250 manuscripts manuscripts 250 were were papers analyzed. developed developed Source: using own using only only RS RS elaboration. data as an EO source (51.2% of the total), followed by cartographic databases data as an EO source (51.2% of the total), followed by cartographic databases produced produced by scientific/governmental by scientific/governmental agencies (22.4%). agencies The remaining (22.4%). works The remainingused aerial photographs Figure 8 documents that most of the 250 manuscripts wereworks developedusedusingaerial only only, associated photographs with only, other associatedsources, with and othercartographic sources, databases based and cartographic on other sources. databases A basedprodu on data as an EO source (51.2% of the total), followed by cartographic databases combination of aerial photographs with satellite imagery was quite common during the first other by sources. A combination of agencies scientific/governmental aerial photographs with satellite imagery was used quite ae analysis period before the 1970s. From the 128(22.4%). papers thatThe remaining adopted works only satellite images, common most ofduring photographs theLandsat only, them used first analysis associated period with exclusively before other (78.3%) the 1970s. sources, or combinedand From the 128databases cartographic with other sensors papers that (15.5%). based adopted other only satellite sources. A images, most of combination ofthem used aerial Landsat exclusively photographs (78.3%)imagery with satellite or combined was q with other sensors common during(15.5%). the first analysis period before the 1970s. From the 128 papers adopted only satellite images, most of them used Landsat exclusively (78.3%) or combi with other sensors (15.5%). Land 2022, 11, x FOR PEER REVIEW Land 2022, 11, 1065 13 of 38 Figure 8. EO data sources from all papers. Source: own elaboration. The temporal extent analysis showed that long-term analysis (m was the most usual situation, accounting for more than 80 papers (34.8 group between 16 and 20 years (16.4), equivalent to the medium term. Figure 8. EO data sources from all papers. Source: own elaboration. Figure 8. EO data sources from all papers. Source: own elaboration. total number of works that used methods for identifying and classify The temporal extent analysis showed that long-term analysis (more than 30 years) 8.0% The was the did most not explain temporal usual situation, the methods extentaccounting analysis showed for more than used, that whereas, 80 long-term in 14.4%, analysis papers (34.8%), followed (more the by thethan 30 photointerpretation group was between the most 16 and 20 usual yearsof situation, satellite (16.4), equivalentfor accounting images. to the medium more The than term. rest However, 80 papers (77.6%) from the (34.8%), carrie followed total number of works that used methods for identifying and classifying LUC with RS, classifications, group between 16with and 20 diverse options. years (16.4), The supervised equivalent to the medium methods were thfr term. However, 8.0% did not explain the methods used, whereas, in 14.4%, the method used was photoint- total they number were erpretation of works used of satellite that in 45.6% images. Theused methods of (77.6%) rest the total. forThe carried identifying out and classifying most common automatic classifications, with LUC w supervised 8.0% did not explain diverse options.likelihood maximum the The supervised methods used, methods were to (equivalent whereas, the 71.9% most common, in 14.4%, of allas the they werethe method used in supervised use cla photointerpretation 45.6% of the total. The mostof common satellitesupervised images.classification The restwas(77.6%)maximumcarried likelihoodout au whereas the second most common method for automatic classificati classifications, with diverse options. The supervised methods were themost (equivalent to 71.9% of all the supervised classifiers) (Figure 9), whereas the second most comm based commonclassifier (OBIA) method for automatic (11.2% classificationof the was thetotal). object-based classifier (OBIA) (11.2% they were used in 45.6% of the total. The most common supervised classificati of the total). maximum likelihood (equivalent to 71.9% of all the supervised classifiers) (Fig whereas the second most common method for automatic classification was the based classifier (OBIA) (11.2% of the total). Figure 9. Methods used for obtaining LUC maps only including those developed with RS (128 papers). Figure 9. Methods used for obtaining LUC maps only including those dev NE: not explained; Figure 9. Methods Photo: photointerpretation used for obtaining ofLUC satellite images; maps OBIA: only object-based including image those analysis; with developed papers). NE: not explained; Photo: photointerpretation of satellite images; OBIA Superv: supervised classification, without more specifications; Unsuperv: unsupervised classification; papers). NE: not explained; Photo: photointerpretation of satellite images; OBIA: object-base analysis; Superv: Hybrid: hybrid classifier;supervised classification, ML: maximum likelihood; without SVM: support vector more machine;specifications; kNN: k-nearest Uns analysis; Superv: supervised classification, without more specifications; Unsuperv: unsup classification; neighbors; Tree classification; reg:Hybrid: tree Hybrid: hybrid regression hybrid classifier; analysis. Source: classifier; own ML: maximumML: maximum likelihood;likelihood; elaboration. SVM: support SVM: supm vector kNN:k-nearest kNN: k-nearest neighbors; neighbors; Tree Tree reg:regression reg: tree tree regression analysis. analysis. Source: own Source: own elab elaboration. The Themost mostcommon common numbers of LUC numbers of categories used in RS LUC categories usedclassification in RS c between betweensixsix and andtenten (60.0%) and below (60.0%) six (28.8%). and below WorkingWorking six (28.8%). with a rather withlowa num rat categories was, in many cases, the result of reclassification processes. Consider and 2022, 11, x FOR PEER REVIEW Land 2022, 11, 1065 14 of 38 between 81% and 90% and above 90% were the most frequent (36.8% The most common numbers of LUC categories used in RS classifications were between respectively). six and ten (60.0%) and below six (28.8%). Working with a rather low number of categories was, in many cases, the result of reclassification processes. Considering the accuracy assessment, 4.2. Step 2:53% LUCCof works did not mention any value (133 entries); this percentage includes Modeling the studies working with cartographical databases produced by scientific/governmental A total agencies of 129 manuscripts (e.g., CORINE). includedofLUCC In terms of the percentage modeling overall accuracy (51.6%), extracted and, of t from the aautomatic statistical modeling classifications technique, mentioned before, whereas 55 presented the groups between 81% and 90%a qualitative and above anal 90% were the most frequent (36.8% and 24.0%, respectively). cases supported by interviews and field surveys. The most-used quantitativ regression (38.0%) 4.2. Step 2: LUCC followed by exploratory data reduction (7.7%), such as fa Modeling canonical analysis, A total of or correspondence 129 manuscripts analysis(51.6%), included LUCC modeling (Figure and,10). It is 74 of these, necessary used to a statistical modeling technique, whereas 55 presented a qualitative analysis, in some conversion of land use and its effects (CLUE) framework [92]. This framewo cases supported by interviews and field surveys. The most-used quantitative model was an empirical regression (38.0%)spatial followedanalysis for identifying by exploratory data reduction the most (7.7%), such important biophysic as factor analysis, economic drivers canonical analysis, and their relationships or correspondence with analysis (Figure the 10). It extension is necessary and intensity to highlight the conversion of land use and its effects (CLUE) framework [92]. This framework is previous study [93] provided core publications and examples of applica based on an empirical spatial analysis for identifying the most important biophysical and different socio-economicCLUE versions drivers and their(CLUE, CLUE-S, relationships Dyna-CLUE, with the extension and of and intensity CLUE-Scanne LUCC. shows that the most-used regression technique was LOR, including A previous study [93] provided core publications and examples of applications for the dif- mu ferent CLUE versions (CLUE, CLUE-S, Dyna-CLUE, and CLUE-Scanner). Figure 11 shows regression (53.1%) and LIR (28.6%). GWR and BRT were the other import that the most-used regression technique was LOR, including multiple logistic regression mainly (53.1%) and since LIR 2016 (28.6%).for theand GWR latter. BRT were the other important methods, mainly since 2016 for the latter. Figure 10. Statistical techniques used for analyzing driving forces of LUCC (129 papers). CLUE: Figure 10. Statistical techniques used for analyzing driving forces of LUCC (129 p conversion of land use and its effects; EDR: exploratory data reduction; EDT: exploratory data conversion transformation;ofEC: land use and exploratory its effects; classification; EGM:EDR: exploratory exploratory data reduction; graphical model; ES: exploratoryEDT: ex transformation; EC: exploratory summarization; TR: training; classification; REG: regression; EGM: QUAL: qualitative exploratory analysis. graphical Source: own elaboration.model; E summarization; TR: training; REG: regression; QUAL: qualitative analysis. Source: ow The analysis of the dependent (or response) variable showed that the most-used variable corresponded to LUC (25.3%), followed by LUCC (21.1%) and, to a lesser degree, to forestry (19.7%), specifically related to deforestation, afforestation, and/or reforestation. The group of driving forces most used in modeling techniques was the combination of biophysical, socio-economic, and distance variables (40.8%), followed by biophysical and socio-economic variables (21.1%), and socio-economic variables only (18.3%). The pixel was the most usual spatial domain (61.9%), followed by administrative boundaries (25.3%). Figure 10. Statistical techniques used for analyzing driving forces of LUC Land 2022, 11, 1065 conversion of land use and its effects; EDR: exploratory data reduction; 15 of 38 transformation; EC: exploratory classification; EGM: exploratory graphical summarization; TR: training; REG: regression; QUAL: qualitative analysis. So Finally, the word “drivers”, together with “driving forces”, was used from the first work, in 2000, until the last year of analysis (2019), showing that it has spread successfully. Figure 11. Regression techniques (49 papers). GWR: geographically weighted regression; PLSR: partial least squares regression; LOR: logistic regression; LIR: linear regression; SUR: seemingly unrelated regression; BRT: boosted regression trees. Source: own elaboration. 4.3. Step 3: Socio-Environmental Consequences A total of 105 manuscripts (42.0% of the total) presented an analysis related to socio- environmental consequences of LUCC. Of these, 41.9% analyzed the socio-environmental consequences using both quantitative (43.2%) and qualitative (56.8%) tools, whereas 43.8% were linked to landscape dynamics, and the rest were mainly connected to ecosystem services (14.3%). Of the total number of manuscripts that analyzed socio-environmental consequences (44), 55.8% corresponded to biodiversity and disasters, followed by agricul- ture (23.2%). Qualitative analysis was used in 100% of studies of the socio-environmental consequences of LUCC in agriculture, 76.9% of studies of biodiversity, and 45.4% of studies of disasters, whereas analyses of water, climate, weather, and energy were 100% quantita- tive. The models used in the quantitative analysis were very diverse; some used export coefficients, whereas others used operational indications provided by the Panel on Climate Change, the Unit Stream Power Erosion and Deposition model (USPED), the Soil and Water Assessment Tool (SWAT), the Revised Wind Erosion Equation (RWEQ), or investment in carbon storage. In the case of landscape consequences, 93.5% of manuscripts calculated some metrics, and the qualitative option was used in the minority. In 63.6% of these papers, FRAGSTATS software was used, followed by Patch Analysis in 13.6% of cases. For both landscape and class levels, the most-used metrics are shown in Figure 12. Those corresponding to configuration were, by order of importance: number of patches, mean patch size, patch density, largest patch index, edge density, landscape shape index and the interspersion and juxtaposition index. For composition, the most-used metric was Shannon’s diversity index. landscape and class levels, the most-used metrics are shown in corresponding to configuration were, by order of importance: numb patch size, patch density, largest patch index, edge density, landsca Land 2022, 11, 1065 the interspersion and juxtaposition index. For composition, 16 of 38the mo Shannon’s diversity index. Figure Figure12. Most-used calculatedcalculated 12. Most-used landscape metrics by numbermetrics landscape of manuscripts. Own elaboration. by number of manuscript The results of ecosystem services models showed that the benefit transfer method was clearly the most-used option (66.7%). According to some researchers, this is a commonly applied method for assessing ecosystem services due to its feasibility for making a quick assessment, at regional and national scales, and the low cost of collecting the required primary data; however, the local ecosystem conditions are an evident limitation [91]. InVEST was the second most-used option (20%). This is a set of spatial models that analyze and predict the provision of ES from LUCC maps and related biophysical, economic, and institutional data for a given region [94–96]. 4.4. Step 4: Future Scenarios The results of future scenario modeling showed an increasing interest for this topic. A clear predominance of Markov chains as the computation method was evident during the initial years, from 2000 to 2008, and MC-CA dominated the literature in the latter years, with a very fragmented use of the remaining methodologies (Figure 13). The dominance of CA is even greater if we consider (i) the Monitoring Land Cover/Use Dynamics (MOLAND) model, an improved CA version developed by [97]; and (ii) the artificial neural network- based cellular automata (ANN-CA) model, because CA is used to model the LUCC by applying the transition probabilities from the ANN learning process [98]. Another choice is the Land Change Modeler (LCM), which provides a tool for evaluat- ing and modeling LUCC by performing three steps: change analysis, transition sub-models, and LUCC predictions [99]. From the three separate empirical models, the multi-layer perceptron (MLP) neural network is the only procedure that can model multiple transitions at the same time, and it was the selected option in most of the papers analyzed. Other options that should be highlighted are those models developed specifically for future scenario analysis, such as, for instance, CLUE-S, which is an updated version of the early CLUE model. CLUE-S is based on an empirical analysis of location suitability combined with the dynamic simulation of competition and interactions between the spa- tiotemporal dynamics of LUC systems, and was specifically developed for the spatially explicit simulation of LUCC [100]. The model includes spatial and non-spatial modules that combine statistical analyses and decision rules that determine the sequence of LUC types [101]. Another alternative is the CLUMondo model, which is the latest addition to the CLUE series, and is a spatially explicit and dynamic land system change model based The results of future scenario modeling showed an increasing interest for this topic. A clear predominance of Markov chains as the computation method was evident during the initial years, from 2000 to 2008, and MC-CA dominated the literature in the latter years, with a very fragmented use of the remaining methodologies (Figure 13). The Land 2022, 11, 1065 17 of 38 dominance of CA is even greater if we consider (i) the Monitoring Land Cover/Use Dynamics (MOLAND) model, an improved CA version developed by [97]; and (ii) the artificial neuralon network-based cellular the highest transition automata potential. (ANN-CA) This variable is the resultmodel, of a sumbecause CA is used of local suitability, to model the LUCCconversion byresistance, applying andthe competitive advantages transition of a land system probabilities from [102]. the ANNThe finallearning pa- rameter analyzed was the temporal extent of future scenarios. The majority was between process [98]. 11 and 20 years (50%), followed by less than 10 years (26.9%). Figure 13. Number of manuscripts Figure (29 in total) 13. Number of manuscripts including (29 in models total) including ofoffuture models scenarios, future scenarios, by year. by year. CLUE- CLUE-S: S: conversion of land use and conversion itsuse of land effects and itsateffects smallat regional extent; small regional Land extent; LandCM: LandChange CM: Land Change Modeler; Modeler; MLP + ANN: multi-layer MLP + ANN:perceptron neural network; multi-layer perceptron ANN-CA: neural network; ANN-CA: artificial neural artificial neural network-based network-based cellular automata; CA: cellular automata; CA: cellular automata. cellular automata. 5. Discussion Another choice Theis theobjective main Land ofChange Modeler this research (LCM), was to assess whichof provides the progress a tool for a specific workflow that we proposed to call STEPLand (Spatio-TEmporal Patterns evaluating and modeling LUCC by performing three steps: change analysis, transition of Land), which involves four identified steps. The study provides a comprehensive picture of the STEPLand sub-models, and LUCC predictions [99]. From the three separate empirical models, the framework by analyzing a sample of 250 scientific articles published during the past multi-layer perceptron 20 years. The (MLP) neural requirements network for inclusion isanalysis in the the only procedure were that thatpublished the papers were can model in impact multiple transitions factor at the journals same time,andand had it at was least 50 thecitations selected (until 2007), in option with the presence most of of the papers target keywords (land use, land cover, changes) and objectives characteristic of STEPLand analyzed. (LUCC patterns, spatial modeling of driving forces, socio-environmental consequences, Other options that should and future scenarios).beThe highlighted analysis alsoare those models attempted to cover adeveloped broad samplespecifically of journals. for future scenario These analysis, criteriasuch as, for provided instance, a provisional CLUE-S, framework which the delineating is an updated STEPLand version framework thatof the can be improved in future works by changing the conditions if required, and increasing or early CLUE model. CLUE-S is based on an empirical analysis of location suitability decreasing the minimum number of citations, for instance. This assumption is made even combined withclearer the dynamic in the map ofsimulation of competition analyzed countries (Figure 7), where and interactions parts between of Africa and the Middle the spatiotemporalEastdynamics of LUC systems, are underrepresented, and was and other countries suchspecifically as New Zealand developed or Malaysia for are the missing. Therefore, future work is needed to try to include all countries, while substantially spatially explicit simulation of LUCC [100]. The model includes spatial and non-spatial increasing the number of entries. modules that combine Oncestatistical analyses the best indicators, and decision according rules to the authors’ that point determine of view, the sequence to characterize the STEPLand framework were established, data extraction was undertaken based on an in- depth reading analysis by both researchers, which was an empirical task for quantifying the results. Step 1, LUCC patterns and dynamics, is the basis of the STEPLand framework. From the 250 publications analyzed, and according to the affiliation of the first author and the Land 2022, 11, 1065 18 of 38 journal titles, a robust outcome was that geography and ecology were the most common fields. Therefore, the “pure” social vision was much less represented. The citation impact of the first ten journals was high, and was mostly Q1 and Q2. According to EO sources, the use of satellite images was the predominant option, but it should be noted that a non-negligible percentage of cases (22.4%) were carried out using cartographic databases already produced by scientific or governmental agencies. This option is clearly less time consuming because an RS classification can be a complicated task, especially for non-experts, and it also allows other research steps to be carried out more comfortably. Nevertheless, this situation can only be useful if the temporal extent, the spatial resolution, and the number of LUC categories from cartographic databases are suitable for the specific research target. Landsat images were the most used for analyzing LUCC having a temporal extent longer than 30 years. Therefore, the medium spatial resolution (30 m × 30 m) was the preferred resolution compared to other options such as MODIS, SPOT, or IKONOS. In fact, this outcome was expected given the difficulties involved in obtaining uninterrupted and continuous time series of data from 1972 onwards, which is the longest-running archive [20,103]. Without doubt, the change to an open (free) data policy in 2012 benefitted its use. However, the use of optical imagery is affected by cloud cover, and, consequently, all of the publications mentioned that images that are free of clouds should be selected as much as possible. In addition to photointerpretation, supervised classifications were frequently chosen and maximum likelihood was the most-used parametric classifier. This finding strongly agrees with a remote sensing literature meta-analysis carried out by [104], in which 32% of the reviewed articles employed maximum likelihood algorithms, although the highest accuracies were obtained with machine learning methods. This dominance is attributed to the wide availability of these algorithms in conventional remote sensing image-processing software packages. This argument was verified by [105]: these authors stated that the un- certainties regarding the effective use and implementation of machine learning techniques were the main barrier to their use. Another clear outcome is that researchers adopting the first step of the STEPLand workflow seemed to be aware of the classification issues in differentiating a high number of LUC categories. Conversely, according to the classification accuracy, some LUCC maps using the post-classification technique showed low accuracies (below 80%). This is a technical issue that merits a more in-depth discussion. A characteristic of the second step, LUCC modeling, is the inclusion, in the materials or data sections, of a table showing the explanatory or independent variables or driving forces used in the model. From our analyzed papers, the table in [106] includes the variable name and a small description indicating the corresponding unit of measurement (proportion, percentage, or index, among others). The tables of other later works also provided the source, the scale, and the model used (raster or vector). Our review indicates that LUCC modeling is a very important stage in STEPLand because 51.6% of works applied quantitative techniques for identifying the relationship between LUCC and the driving forces [107,108]. The most-used quantitative procedure was regression (38.0%), of which logistic regression was the most frequent, followed by linear regression. Our results agree with earlier studies; as stated by [109], multiple regression is frequently used by ecologists and biologists to identify factors influencing response variables, such as species richness or occurrence. Another study [110] noted that regression models are one of the most-common techniques for identifying relationships between variables, including drivers, and modeling transition potentials. They also stated that logistic regressions were the dominant statistical tool for describing relationships between a dichotomous variable (the categorical nature of LUCC) and a set of explanatory drivers. Some solving methods were applied to avoid criticisms about multicollinearity and spatial autocorrelation [60], such as random selection of pixels, generalization/aggregation of pixel size (coarser resolution), correlation coefficients, or analysis of variance [111]. Land 2022, 11, 1065 19 of 38 Furthermore, this review found that the combined uses of biophysical, socio-economic, and distance variables are the most-common drivers applied at the pixel scale. Nevertheless, some studies do not explain, in detail or at all, the combined use of different sources, units of measurement, and scales, i.e., the interrelationships between the driving forces. This is even worse when administrative boundaries are involved [35], although the method chosen to combine them is determinant in the acquisition of useful results. In step 3, socio-environmental consequences were mainly analyzed, followed by landscape and ecosystem services. Biodiversity and disasters were the topics most studied using generally qualitative models, whereas quantitative models were predominant in the remaining contributions. In ecosystem services, the benefit transfer method was the most applied, followed by the InVEST model. According to [112], this latter model, together with others (e.g., ARIES or CLASSIC), has two problems: it fails to fully consider or simplify the ecological process, and its evaluation accuracy is still controversial. Finally, in future scenarios, in 2005, Ref. [113] asserted that the Markov-type models were some of the most common methods for predicting change among various categorical states. This clear preponderance can be explained by their spatial and temporal robustness because GIS and RS data can be efficiently incorporated to define the initial conditions to parameterize Markov models, calculate transition probabilities, and determine the neighborhood rules with transitional potential maps [114]. 6. Conclusions STEPLand is a consolidated framework within LCS that has been applied by experts worldwide, mainly from the geographical and natural sciences. The high diversity of journals and countries involved in the research shows that it has become relatively ubiqui- tous. Issues related to LUCC have attracted interest among a wide variety of researchers, ranging from those who wish to model spatial and temporal patterns of land conversion, to those who aim to understand causes and consequences of land transformation. Therefore, STEPLand facilitates communications and interactions among researchers from different fields, and between researchers and policy makers. Based on this review, we conclude that step 1 of STEPLand research is characterized by: • Researchers mainly involved in geographic or environmental sciences. • Articles published in journals mainly specialized in ecology, geography, or environ- mental sciences, often having a high impact factor. • RS data as the main EO source used to obtain patterns, of which, Landsat images were the most used, after reducing the number of images included in the study due to the presence of clouds. Use of the Landsat open (free) archive has been essential to the spread of the STEPLand framework. • A relatively long horizon (over 30 years), which was the most-used temporal extent. • Supervised methods, in general, and the maximum likelihood classifier, in particular, which were predominant in the automatic classifications, with LUC legends primarily composed of 6 to 10 classes. • Post-classification methods (with transition matrix), which were the predominant tool in LUCC analysis. Step 2 was characterized by: • The combination of analysis of patterns and dynamics, and modeling. • Quantitative spatial modeling based on statistics, mainly using regressions, headed by LOR and LIR. • A dependent variable extracted from LUC or LUCC, with a considerable weight assigned to forest variables. • The combination of biophysical, socio-economic, and distance variables as the main driving forces, in which data integration was very important. • Pixels from regular lattices as the main spatial domain. Step 3 was characterized by: Land 2022, 11, 1065 20 of 38 • Analysis of general socio-environmental consequences using qualitative methods, mainly related to biodiversity and natural disasters, and quantitative models, although very diverse, applied in the rest of topics. Surveys have been used to determine driving forces and the perception of socio-environmental consequences for local people. Spatially combining RS with other data can be difficult. • A high weight attached to the analysis of LUCC landscape consequences, mainly using the FRAGSTATS software for calculating, at least in many cases, the number of patches and the mean patch size. • In the analysis of ecosystem services, the application of the benefit transfer method, which was the most common model given its feasibility at regional or national scales to develop a quick assessment and the low cost of collecting the required primary data. Finally, step 4 was characterized by: • A clear predominance of Markov chain (between 2000 and 2008) and Markov chain–cellular automata models. • A substantial weight of CA in other options, such as in the MOLAND model. • The more recent application of other models, such as CLUMondo or Land Change Modeler. To conclude, a future scenario for STEPLand includes using improved spatial and temporal resolutions of satellite imagery, which may be implemented by adopting the free Sentinel imagery or with the contributions of drones. This may provide a more refined modeling of LUCC and the resulting socio-environmental consequences. Another challenge is the synergistic application of the four STEPLand stages, which was not documented in the sample of papers reviewed in this work. Author Contributions: All authors were involved in the research conceptualization, data processing, and manuscript writing. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Appendix A contains the references of the 250 selected articles analyzed in this review. 1. Abd El-Kawya, O.R.; Rød, J.K.; Ismail, H.A.; Suliman, A.S. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Appl. Geogr. 2011, 31, 483–494. 2. Abrantes, P.; Fontes, I.; Gomes, E.; Rocha, J. Compliance of land cover changes with municipal land use planning: Evidence from the Lisbon metropolitan region (1990–2007). Land Use Pol. 2016, 51, 120–134. 3. Acheampong, M.; Yu, Q.; Enomah, L.D.; Anchang, J.; Eduful, M. Land use/cover change in Ghana’s oil city: Assessing the impact of neoliberal economic policies and implications for sustainable development goal number one—A remote sensing and GIS approach. Land Use Pol. 2018, 73, 373–384. 4. Adhikari, A., Hansen, A.J. Land use change and habitat fragmentation of wildland ecosystems of the North Central United States. Landsc. Urban Plann. 2018, 177, 196–216. 5. 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