Land Change Science and the STEPLand Framework : An Assessment of Its Progress

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

Driving Factors of Land Change in China’s Loess Plateau: Quantification Using Geographically Weighted Regression and Management Implications

Land change is a key topic in research on global environmental change, and the restoration of degraded land is the core component of the global Land Degradation Neutrality target under the UN 2030 Agenda for Sustainable Development. In this study, remote-sensing-derived land-use data were used to characterize the land-change processes in China’s Loess Plateau, which is experiencing large-scale ecological restoration. Geographically Weighted Regression was applied to capture the spatiotemporal variations in land change and driving-force relationships. First, we explored land-use change in the Loess Plateau for the period 1990–2015. Grassland, cropland and forestland were dominant land cover in the region, with a total percentage area of 88%. The region experienced dramatic land-use transitions during the study period: degraded grassland and wetland, expansion of cropland and built-up land and weak restoration of forestland during 1990–2000; and increases in grassland, built-up land, forestland and wetland, concurrent with shrinking cropland during 2000–2015. A Geographically Weighted Regression (GWR) analysis revealed altitude to be the common dominant factor associated with the four major land-use types (forestland, grassland, cropland and built-up land). Altitude and slope were found to be positively associated with forestland, while being negatively associated with cropland in the high, steep central region. For both forestland and grassland, temperature and precipitation behaved in a similar manner, with a positive hotspot in the northwest. Altitude, slope and distance to road were all negatively associated with built-up land across the region. The GWR captured the spatial non-stationarity on different socioeconomic driving forces. Spatial heterogeneity and temporal variation of the impact of socioeconomic drivers indicate that the ecological restoration projects positively affected the region’s greening trend with hotspots in the center and west, and also improved farmer well-being. Notably, urban population showed undesired effects, expressed in accelerating grassland degradation in central and western regions for 1990–2000, hindering forestland and grassland restoration in the south during 2000–2015, and highlighting the long-term sustainability of the vegetation restoration progress. Such local results have the potential to provide a methodological contribution (e.g., nesting local-level approaches, i.e., GWR, within land system research) and spatially explicit evidence for context-related and proactive land management (e.g., balancing urbanization and ecological restoration processes and advancing agricultural development and rural welfare improvement)

GIS-based spatial analysis coupled with geophysical imaging to identify and evaluate factors that control the formation of karst sinkholes in southwestern Missouri

Sinkholes are inherent features of the karst terrain underlying much of Greene County, Missouri. These features present hazards and engineering challenges to existing infrastructure unknowingly constructed on a seemingly benign ground surface. The primary objective of this research was to investigate the physical processes chiefly responsible for triggering the seemingly random distribution of sinkholes in the study area. This research employed an integrated approach encompassing regional scale GIS-based spatial analyses and site-specific geophysical data. GIS-based spatial analysis was employed to identify significant physical factors that appeared to influence the formation and distribution of sinkholes. Seven out of the twelve most cited factors influencing sinkhole development were identified in the study area. These factors were: overburden thickness, depth-to-groundwater, slope of the ground surface, distance to the nearest water course, distance to the nearest geologic structures, distance to nearest springs, and distance to the nearest roads. In the site-specific geophysical investigations, two dimensional (2D) and pseudo three dimensional (3D) - ERT, MASW, and borehole data were used to characterize the subsurface morphology of the karstified soil-bedrock interface in five selected sinkholes. From the interpretation of the 2D and pseudo 3D-ERT profiles, it was determined that four of the five sinkholes occurred at the intersections of regional systematic joint sets. The joint sets are characterized by a linear, visually prominent zones of low resistivity. The relatively low resistivity values are attributed to vertical seepage and the associated piping of fine-grained soils through preexisting fractures (often widened by solutioning) --Abstract, page iv

Assessment of Potential Impacts of Climate Change on Hydrology and Water Resource Availability in the Passaic River Basin, New Jersey

Streamflow dynamics in a basin is known to be a major driver of available water resources. In the context of climate change, it is expected that global warming will accelerate the global hydrologic cycle, which will drive more intense floods and droughts leading to changes in streamflow and water resource availability. Most researchers agree that the amount and intensity of precipitation have a direct impact on runoff. Yet, there is no consensus as to how warming can affect streamflow. Evapotranspiration (ET) plays a crucial role here. However, there is a shortage of real-world observations on it. And yet, ET is considered as the primary determinant of available water resources. It is the water that would otherwise become streamflow if not released into the atmosphere. In the Passaic River Basin (PRB), this water loss constitutes on average 50 percent of the approximately 49-inches precipitation. Because of its substantial heterogeneity in land use, soils, geology, reservoirs, vegetation, slope, and topography, the PRB exhibit a highly complex river system. This complexity amidst the heterogeneous biophysical arrangement within the basin present a multifaceted mix of competing interests and water related issues. In a region where predicted temperature increases are anticipated to amplify evapotranspiration and reduce snowpack, the resulting impact on streamflow could be significant. It is with this consideration that this dissertation attempts to better understand the mechanism behind streamflow dynamics in the basin, noting that it is a major driver of available water resource. That way, the impacts of climate change can be properly assessed. In this work, three independent research studies using available hydrological and climate data for the Passaic River Basin were conducted to achieve this goal. In the first study, I used Gridded datasets from Parameter-elevation Regressions on Independent Slopes Model (PRISM), TerraClimate, and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product to develop spatially-varying monthly ET models. Beyond the widely used traditional type regression that has the effect of producing ‘global’ parameter estimates, assumed to be uniform throughout a study area, a more localized spatially non-stationary technique — the geographically weighted regression (GWR) — was utilized to estimate mean monthly ET in the Passaic River Basin (PRB). Key environmental controls of ET have been identified and new sets of spatially varying empirical ET models based on variable combinations that produced the best- fit model have been developed. The analysis showed that temporal and spatial variabilities in ET over the PRB are driven by climatic and biophysical factors. It was found that the key controlling factors were different from month to month, with wind speed being dominant throughout the year in the study basin. Monthly mean ET index map was further generated from the model to illustrate areas where ET exceeds precipitation. In the second study, I bypassed the frequently used Mann-Kendal trend test in a novel application using the wavelet transform tool to identify the hidden monotonic trends in the inherently noisy hydro-climatic data. By this approach, the use of Mann Kendal trend test directly on the raw data whose results are almost always ambiguous and statistically insignificant in respect of precipitation data for instance, no longer pose a challenge to the reliability of trend results. The results showed that whereas trends in temperature and precipitation are increasing in the PRB, streamflow trends are decreasing. Based on results from the hydrological modelling, streamflow is more sensitive to actual ET than it is to precipitation. The general observation from climate elasticity results showed that in decades where water is available, energy limits actual evapotranspiration which makes streamflow more sensitive to precipitation increase. However, in meteorologically stressed or dry decades, water limits actual ET thereby making streamflow more sensitive to increases in actual evapotranspiration. It was found that the choice of baseline condition constitutes an important source of uncertainty in the sensitivities of streamflow to precipitation and evapotranspiration changes and should routinely be considered in any climate impact assessment. In the third study, I forced a duly calibrated and verified hydrological model with advanced downscaled and bias-corrected climate scenarios in a rare application in the Rockaway sub- catchment of the Passaic River Basin to assess the impacts of climate change on water resource availability. A priori analysis however involved the selection of subset models from twenty (20) Multivariate Adaptive Constructed Analog (MACA) climate models that characterized the change in temperature and precipitation according to LEAST WARM, HOT, DRY, and WET at mid-21st century (2041—2070) as well as a mild future that typifies the MIDDLE of the temperature and precipitation range. In all, nine (9) different models, relative to two baseline periods, and under two different climate scenarios were selected. Results showed that against the 2041—2070 period, the margin of error owing to the use of different baseline conditions were +/- 0.3 — +/-0.23 oC for temperature and +/-8.15— +/-6.9% for precipitation, indicating the extent to which the time perspective used in climate change impacts assessment significantly affect outcomes. Across all five (5) climate projections, and the two scenarios, a consistent warming from +1.21 to + 4.70 oC is projected in the Rockaway catchment at mid-21st century relative to the 1981—2010 baseline period. While precipitation is generally projected to increase, streamflow prediction shows an overall decreasing signal, a trend likely induced by the projected increase in actual evapotranspiration. In terms of climate extremes, an increase in the number heavy rainy days of approximately 2 days is projected in the coldest future whiles an increase of about 4 days is expected in the wettest future. In similar vein, the number days with consecutive dry spells is expected to decrease by approximately 2 days in the driest future whereas an increase of about 3 days is projected in the wettest future. Overall, climate change is expected to fuel flooding and drought conditions in the study catchment, and to cause alterations in river flows which will in turn affect reservoir operations. With this advance knowledge in hand, swift mitigation and adaptation plans are therefore needed. The results presented in this dissertation show that climate change will threatened available water resources through evapotranspiration. Because the availability of water resource is largely driven by river flows in channels, possible increase or decrease in flow as depicted in the study will fuel flooding and drought conditions. Given that streamflow is highly sensitive to precipitation increases in decades where water is sufficiently available, even higher risk of extreme floods can be expected. On the other hand, longer dry spells will lead to water scarcity and higher risk of drought potentials. Either way, alterations in river flows will affect routine reservoir operations under a changing climate. Particularly, a crucial basis for examining possible environmental impacts on dam failure, including physical sedimentation, erosion from floodwaters, and chemical contamination has been established in this study. With this advance knowledge in hand, swift mitigation and adaptation plans are therefore needed

The Application of Geographic Information Systems Cellular Automata Based Models to Land Use Change Modelling of Lagos, Nigeria

The urban expansion of Lagos continues unabated and calls for urgent concern. This thesis explored the use of both the conventional and unconventional techniques for modelling land use change. Two conventional methods (ordinary least squares and geographically weighted regression) were based on geographic information systems, while four unconventional methods (logistic regression, artificial neural networks, and two proposed types of support vector machine) were based on cellular automata. These techniques were evaluated using three land use epochs: 1963-1978, 1978-1984, and 1984-2000. The conventional methods make quite strong statistical assumptions, some of which are shown not to be met by the land use data at hand. Despite this, these methods do exhibit substantial agreement between observed and the predicted maps. The non cellular automata and cellular automata modelling were then implemented with the logistic regression, artificial neural network, support vector machine, and fuzzy support vector machine models, with model parameters set by k-fold cross-validation. The cellular automata predicted maps were more accurate than those of the non cellular automata. The cellular automata modelling results from the proposed support vector machine and fuzzy support vector machine were compared with those from the geographic information systems based geographically weighted regression, logistic regression, and artificial neural network. The results from the geographic information systems based geographically weighted regression were the best, followed by those from the support vector machine and fuzzy support vector machine, followed by the artificial neural network, and logistic regression. This research demonstrated that the proposed support vector machine and fuzzy support vector machine based cellular automata models are promising tools for land use change modelling

Urbanization, land use, and sustainable development in China

pre-printChina's economic reforms and unprecedented growth have generated many fascinating issues for scholarly research. An understanding of urbanization and land use change in China is required for appropriate strategies and policies to facilitate future sustainable development. This paper reviews the literature on urbanization, land use and sustainable development in China with a focus on land use change. We argue that land use and environmental research are embedded in the complex economic-geographical processes and multiple trajectories of development and urbanization in China. This paper highlights the important role of space-time modeling in a multi-disciplinary setting in the study of urbanization, land use and sustainable development. It also points out potential areas for future research

Social Disorganization and Sex Offenders in Minneapolis, MN: A Socio-Spatial Analysis

Using a combination of techniques stemming from the spatial analysis approach of Geography, structural-functionalist theory in Sociology, and an ecological perspective of Criminology, this thesis addresses where sex offenders reside and why. Analyses were performed using the twin cities of Minneapolis and St. Paul, Minnesota as a typical urban setting. The study fuses multiple disciplines work on the complex social problem of released risk level III sex offender management in a spatially-conscious, micro-scale analysis attempting to understand the distribution of released offenders and the relevance of social disorganization theory in explaining their distribution. Socio-economic status and family disruption are tested and found to be important components of a generalized or fuzzy correlation between calculated social disorganization and offender settlement. In concert with other recent research in the U.S., residential stability is a variable of limited determinate capability. In an attempt to understand the fuzzy correlation, this fused analysis develops urban design considerations for mitigation of offender concentrations as well as other insights for policy and management. Inclusive in this analysis is the revelation that offenders often settle in physically and socially disrupted `wedge,\u27 or isolated neighborhoods. It suggests the merit of complimentary quantitative and qualitative analysis techniques in urban socio-spatial analysis

An Application of Cellular Automata (CA) and Markov Chain (MC) Model in Urban Growth Prediction: A case of Surat City, Gujarat, India

The main purpose of this study is to detect land use land cover change for 1990-2000, 2000-2010, and 2010-2020 using multispectral Landsat images as well as to simulate and predict urban growth of Surat city using Cellular Automata-based Markov Chain Model. Maximum likelihood supervise classification was used to generate LULC maps of the years 1990,2000,2010, and 2020 and the overall accuracy of these maps were 90%, 95%, 91.25%, and 96.25%, respectively. Two transition rules were commuted to predict the LULC of 2010 and 2020. For validation of these LULC maps, the Area Under Characteristics curve was used, and these maps' accuracy was 95.30% and 86.90%. This validation predicted LULC maps for the years 2035 and 2050. Transition rules of 2010-2035 showed that there will be a probability that 36.33% of vegetation area and 40.27% of the vacant land area will be transited into built-up by the year 2035, and it will be 49.20 % of the total area. Also, 57.77% of the vegetation area and 60.24% of the built-up area will be transformed into urban areas by the year 2050, almost 62.60 %. Analysis of LULC maps 2035 and 2050 exhibits that there will be abundant growth in all directions except the South Zone and Southwest Zone. Therefore, this study helps urban planners and decision-makers decide what to retain, where to plan for new development and type of development, what to connect, and what to protect in coming years

Methods in general model localization

The aim of this study was to evaluate and test methods which could improve local estimates of a general model fitted to a large area. In the first three studies, the intention was to divide the study area into sub-areas that were as homogeneous as possible according to the residuals of the general model, and in the fourth study, the localization was based on the local neighbourhood. According to spatial autocorrelation (SA), points closer together in space are more likely to be similar than those that are farther apart. Local indicators of SA (LISAs) test the similarity of data clusters. A LISA was calculated for every observation in the dataset, and together with the spatial position and residual of the global model, the data were segmented using two different methods: classification and regression trees (CART) and the multiresolution segmentation algorithm (MS) of the eCognition software. The general model was then re-fitted (localized) to the formed sub-areas. In kriging, the SA is modelled with a variogram, and the spatial correlation is a function of the distance (and direction) between the observation and the point of calculation. A general trend is corrected with the residual information of the neighbourhood, whose size is controlled by the number of the nearest neighbours. Nearness is measured as Euclidian distance. With all methods, the root mean square errors (RMSEs) were lower, but with the methods that segmented the study area, the deviance in single localized RMSEs was wide. Therefore, an element capable of controlling the division or localization should be included in the segmentation-localization process. Kriging, on the other hand, provided stable estimates when the number of neighbours was sufficient (over 30), thus offering the best potential for further studies. Even CART could be combined with kriging or non-parametric methods, such as most similar neighbours (MSN).Spatiaalisen tilastotieteen keinoin pystytään kalibroimaan yleisten, koko laajan tutkimusalueen kattavien, regressiomallien ennusteita, jolloin saadaan yhä tarkempia paikallisia estimaatteja ja arvioita. Yleisen mallin käytölle on ollut esteenä tämä alueellinen epätarkkuus, mutta jos epätarkkuutta pystytään pienentämään, yleisiä malleja voidaan liittää esim. laajojen metsäalueiden inventointi- ja arviointisysteemeihin. Yleisten mallien etuna on niiden yksinkertaisuus ja helppous käytössä. Väitöskirjassa tarkastelualueena on ollut eteläinen Suomi. Väitöskirjassa on tutkittu ja vertailtu erilaisia menetelmiä, joilla regressiomallin antamia ennusteita voidaan paikallistaa eli lokalisoida. Lokalisoinnissa paikallista harhaa, joka on todellisen mitatun arvon ja mallin antaman ennusteen välinen erotus, pienennetään tai poistetaan alueellisesti kokonaan. Yhteistä menetelmille on, että ne hyödyntävät havaintojen välistä spatiaalista autokorrelaatiota. Spatiaalisen autokorrelaation (SA) perusajatuksena on, että kaksi lähekkäin sijaitsevaa kohdetta ovat todennäköisemmin samankaltaisempia kuin kaksi toisistaan kauempana sijaitsevaa kohdetta ja siksi ympäristön poikkeamia yleisestä keskiarvosta voidaan käyttää naapurin arvioimiseen. Tarkempia estimaatteja voidaan saavuttaa erilaisilla menetelmillä. Osassa menetelmistä tutkimusalue on jaettu pienempiin mahdollisimman yhtenäisiin alueisiin, joille alkuperäinen malli on uudelleen sovittettu eli lokalisoitu, ja toisissa lokalisointi tehdään aina kunkin havainnon lähiympäristön havaintojen, nk. naapuruston, avulla. Kaikilla menetelmillä jäännösvirhe (RMSE) pieneni, mutta niillä menetelmillä, joilla lokalisointiin pyrittiin aluetta jakamalla, lokalisoiduissa RMSE:issä oli suurta vaihtelua. Siksi näihin menetelmiin pitäisi liittää jokin lisämuuttuja, jolla pystyisi kontrolloimaan jakamista ja lokalisointia. Tällöin pystyttäisiin arvioimaan, ovatko tietyt jaot kokonaisuudessaan tai yksittäiset alueet lokalisoinnin kannalta kannattavia. Toisaalta naapurustoa hyödyntävä lokalisointi antoi vakaita ennusteita, kun naapureiden määrä oli riittävä (yli 30). Tämä vaihtoehto tarjoaakin parhaimmat mahdollisuudet jatkotutkimuksille; sillä siihen voidaan yhdistää muita väitöskirjassa käytettyjä menetelmiä tai ei-parametrisiä menetelmiä