Revisiting Kappa to account for change in the accuracy assessment of land-use change models

Land-use change models are typically calibrated to reproduce known historic changes. Calibration results can then be assessed by comparing two datasets: the simulated land-use map and the actual land-use map at the same time. A common method for this is the Kappa statistic, which expresses the agreement between two categorical datasets corrected for the expected agreement. This expected agreement is based on a stochastic model of random allocation given the distribution of class sizes. However, when a model starts from an initial land-use map and makes changes to it, that stochastic model does not pose a meaningful reference level. This paper introduces K_Simulation, a statistic that is identical in form to the Kappa statistic but instead applies a more appropriate stochastic model of random allocation of class transitions relative to the initial map. The new method is illustrated on a simple example and then the results of the Kappa statistic and K_Simulation are compared using the results of a land-use model. It is found that only K_Simulation truly tests models in their capacity to explain land-use changes over time, and unlike Kappa it does not inflate results for simulations where little change takes place over time.     

Parcelization in rural agricultural and forested landscapes in Wisconsin, 1972–2007: evaluating multiple dimensions of human decision-making over time

Many land-use change studies have relied on geographical explanatory factors. Unfortunately, they are but a single perspective in the multidimensional process of human decision-making. This project was designed to model the social, economic, geographic, and regulatory factors at the most appropriate unit of analysis, the landowner. By examining parcelization, a window is opened into the antecedent event of land-use change. A logistic regression model determined the likelihood a tax parcel would split in three time periods between 1953 and 2007. Geographic variables showed expected relationships to a parcelization event, while economic and regulatory variables illustrated some unexpected relationships. Social variables demonstrated scale issues that challenged their efficacy. A temporal analysis showed that historic parcelization was explained more robustly than more contemporary parcelization. The results could indicate that contemporary parcelization is driven by new and more complex factors not yet represented in similar models.     

Application of analytical hierarchy process and geographic information systems in land-use suitability evaluation: a case study of Dümrek village (Çanakkale,Turkey)

This study used both analytical hierarchy process (AHP) and geographic information systems (GIS) to make a land-use suitability analysis for the village of Dümrek, NW Turkey. Primarily, alternative land uses for agriculture, meadow–pasture and forest were determined along with criteria for these alternatives and a hierarchical structure was produced and used to determine the weights of the criteria. Spatial data were identified by means of GIS and calculations were made using the suitability values specified and weights obtained from AHP. Suitability maps were then produced for the above land use alternatives. Subsequently, a synthesized suitability map was formed from these maps. According to the weights specified by AHP, the order of land use preferences among the alternatives for rural development of Dümrek was agriculture, forest and meadow. The synthesized suitability map showed that the areas allocated for forest and agriculture were close to the present ratios of use; however, meadow land, which does not exist at present, should be allocated as a land use to constitute 12.5% of the study area. Achieving sustainability in land use is possible by planners and administrators considering results obtained from land suitability mapping studies at the stage of allocating land uses. The method applied in this research is considered useful when taking policy decisions covering the evaluation of rural land use, particularly for subunits of the state administration.     

Economic-based projections of future land use in the conterminous United States under alternative policy scenarios

Land-use change significantly contributes to biodiversity loss, invasive species spread, changes in biogeochemical cycles, and the loss of ecosystem services. Planning for a sustainable future requires a thorough understanding of expected land use at the fine spatial scales relevant for modeling many ecological processes and at dimensions appropriate for regional or national-level policy making. Our goal was to construct and parameterize an econometric model of land-use change to project future land use to the year 2051 at a fine spatial scale across the conterminous United States under several alternative land-use policy scenarios. We parameterized the econometric model of land-use change with the National Resource Inventory (NRI) 1992 and 1997 land-use data for 844 000 sample points. Land-use transitions were estimated for five land-use classes (cropland, pasture, range, forest, and urban). We predicted land-use change under four scenarios: business-as-usual, afforestation, removal of agricultural subsidies, and increased urban rents. Our results for the business-as-usual scenario showed widespread changes in land use, affecting 36% of the land area of the conterminous United States, with large increases in urban land (79%) and forest (7%), and declines in cropland (-16%) and pasture (-13%). Areas with particularly high rates of land-use change included the larger Chicago area, parts of the Pacific Northwest, and the Central Valley of California. However, while land-use change was substantial, differences in results among the four scenarios were relatively minor. The only scenario that was markedly different was the afforestation scenario, which resulted in an increase of forest area that was twice as high as the business-as-usual scenario. Land-use policies can affect trends, but only so much. The basic economic and demographic factors shaping land-use changes in the United States are powerful, and even fairly dramatic policy changes, showed only moderate deviations from the business-as-usual scenario. Given the magnitude of predicted land-use change, any attempts to identify a sustainable future or to predict the effects of climate change will have to take likely land-use changes into account. Econometric models that can simulate land-use change for broad areas with fine resolution are necessary to predict trends in ecosystem service provision and biodiversity persistence.     

Are existing vegetation maps adequate to predict bird distributions?

Bird species are selective on the vegetation types in which they are found but predictive models of bird distribution based on variables derived from land-use/land-cover maps tend to have limited success. It has been suggested that accuracy of existing maps used to derive predictors is in part responsible for the limited success of bird distribution models. In two areas of 4900 km² of Western Andalusia, Spain, we compared the predictive ability of bird distribution models derived from two existing general-purpose land-use/land-cover maps, which differ in their resolution and accuracy: a coarse scale vegetation map of Europe, the CORINE land-cover map, and a detailed regional map, the 1995 land-use/land-cover map of Andalusia from the SINAMBA (Consejerı́a de Medio Ambiente, Junta de Andalucı́a). We compared the bird distribution models derived from these general-purpose vegetation maps with models derived from two more accurate structural vegetation maps built considering directly variables that influence bird habitat selection, one built from satellite images for this study and another obtained by improving the resolution and accuracy of the SINAMBA map with satellite data. We sampled the presence/absence of bird species at 857 points using 15-min point surveys. Predictive models for 54 bird species were built with generalised additive models (GAMs), using as potential predictors the same set of landscape and vegetation structure variables measured on each map. We compared for each bird species the predictive accuracy of the best model derived from each map. Vegetation structure measured at bird sample points was used as ground-truth for comparing the accuracy of vegetation maps. Although maps differed in their resolution and accuracy, the results show that all of them produced similarly accurate bird distribution models, with a mixed map produced with both thematic and satellite information being the best. The models derived from the more accurate vegetation structure maps obtained from satellite data were not more accurate than those derived directly from the SINAMBA or CORINE maps. Our results suggest that some general-purpose land-use/land-cover maps are accurate enough to derive bird distribution models. There is a certain limit to improve vegetation maps above which there is no effect in their power to predict bird distribution.     

Framing the search for a theory of land use

ABSTRACT

Land system science and affiliated research linked to sustainability require improved understanding and theorization of land and its change as a social-ecological system (SES). The absence of a general land-use theory, anchored in the social subsystem but with explicit links to the environmental subsystem, hampers this effort. Drawing on land-use explanations, meta-analyses, and associated frameworks, we advance a broad framework structure of eight elements – aggregations of explanatory variables – with links to the biophysical subsystem, for systematic comparisons of extant explanations. Tests and models can be employed to identify which set of variables and their configurations provide robust explanations of across land uses, identifying the potential for theory development. The framework and its application are applicable to both top-down and bottom-up explanatory approaches employed in the social sciences. Links to the environmental subsystem invite future exploration of SES explanations that reach across the different dimensions of global change and sustainability science.     

Effects of Land Use on Threatened Species

Abstract:  There is widespread agreement that biodiversity loss must be reduced, yet to alleviate threats to plant and animal species, the forces driving these losses need to be better understood. We searched for explanatory variables for threatened-species data at the country level through land-use information instead of previously used socioeconomic and demographic variables. To explain the number of threatened species in one country, we used information on land-use patterns in all neighboring countries and on the extent of the country's sea border. We carried out multiple regressions of the numbers of threatened species as a function of land-use patterns, and we tested various specifications of this function, including spatial autocorrelation. Most cross-border land-use patterns had a significant influence on the number of threatened species, and land-use patterns explained the number of threatened species better than less proximate socioeconomic variables. More specifically, our overall results showed a highly adverse influence of plantations and permanent cropland, a weaker negative influence of permanent pasture, and, for the most part, a beneficial influence of nonarable lands and natural forest. Surprisingly, built-up land also showed a conserving influence on threatened species. The adverse influences extended to distances between about 250 km (plants) and 2000 km (birds and mammals) away from where the species threat was recorded, depending on the species. Our results highlight that legislation affecting biodiversity should look beyond national boundaries.     

Ecological effects associated with land-use change in China's southwest agricultural landscape

Research on land-use and land-cover change, with associated effects on the ecoenvironment, is a key to understanding global change. The concept of 'ecosystem services' is also a hot issue in ecology and ecological economics. In this study, ecosystem service values are used to assess the ecological values of corresponding land-use types, so as to evaluate the ecological effects of regional land-use change. China's southwest agricultural landscape has unique ecological functions; but it also belongs to an ecologically fragile zone. Consequently, land-use change and associated ecological effects must be monitored to assure sustainable development of this area. Based on TM images in 1988, 1994 and 1999, the landscape classification maps of Yongsheng County were assessed using supervised classification and interactive modification methods. The transition matrix of land-use types and three indices of spatial patterns, patch-level, class-level and landscape-level indices, were calculated using models and GIS to examine the spatial patterns and dynamics of land use in the study area. The results show the influences of human activities and natural environmental elements, and that unused land has decreased rapidly, together with a continuous increase in forest during the past 11 years. There are also frequent intermediate transitions between farmland and unused land. An index for landscape diversity shows a tendency to increase, indicating that the proportions of each landscape element tended to average. Also, the decrease in the fractal dimension of unused land reveals that the effects of human activity are increasing. Ecological value calculations show that land-use change in Yongsheng County from 1988–1999 has resulted in positive ecological effects, with distinct spatial differences.     

Hierarchical Bayesian scaling of soil properties across urban, agricultural, and desert ecosystems.

Ecologists increasingly use plot-scale data to inform research and policy related to regional and global environmental change. For soil chemistry research, scaling from the plot to the region is especially difficult due to high spatial variability at all scales. We used a hierarchical Bayesian model of plot-scale soil nutrient pools to predict storage of soil organic carbon (oC), inorganic carbon (iC), total nitrogen (N), and available phosphorus (avP) in a 7962-km2 area including the Phoenix, Arizona, USA, metropolitan area and its desert and agricultural surroundings. The Bayesian approach was compared to a traditional approach that multiplied mean values for urban mesic residential, urban xeric residential, nonresidential urban, agricultural, and desert areas by the aerial coverage of each land-use type. Both approaches suggest that oC, N, and avP are correlated with each other and are higher (in g/m2) in mesic residential and agricultural areas than in deserts or xeric residential areas. In addition to traditional biophysical variables, cultural variables related to impervious surface cover, tree cover, and turfgrass cover were significant in regression models predicting the regional distribution of soil properties. We estimate that 1140 Gg of oC have accumulated in human-dominated soils of this region, but a significant portion of this new C has a very short mean residence time in mesic yards and agricultural soils. For N, we estimate that 130 Gg have accumulated in soils, which explains a significant portion of "missing N" observed in the regional N budget. Predictions for iC differed between the approaches because the Bayesian approach predicted iC as a function of elevation while the traditional approach employed only land use. We suggest that Bayesian scaling enables models that are flexible enough to accommodate the diverse factors controlling soil chemistry in desert, urban, and agricultural ecosystems and, thus, may represent an important tool for ecological scaling that spans land-use types. Urban planners and city managers attempting to reduce C emissions and N pollution should consider ways that landscape choices and impervious surface cover affect city-wide soil C, N, and P storage.     

Economics and Land-Use Change in Prioritizing Private Land Conservation

Abstract:  Incentive-based strategies such as conservation easements and short-term management agreements are popular tools for conserving biodiversity on private lands. Billions of dollars are spent by government and private conservation organizations to support land conservation. Although much of conservation biology focuses on reserve design, these methods are often ineffective at optimizing the protection of biological benefits for conservation programs. Our review of the recent literature on protected-area planning identifies some of the reasons why. We analyzed the site-selection process according to three important components: biological benefits, land costs, and likelihood of land-use change. We compared our benefit-loss-cost targeting approach with more conventional strategies that omit or inadequately address either land costs or likelihood of land-use change. Our proposed strategy aims to minimize the expected loss in biological benefit due to future land-use conversion while considering the full or partial costs of land acquisition. The implicit positive correlation between the likelihood of land-use conversion and cost of land protection means high-vulnerability sites with suitable land quality are typically more expensive than low-vulnerability sites with poor land quality. Therefore, land-use change and land costs need to be addressed jointly to improve spatial targeting strategies for land conservation. This approach can be extended effectively to land trusts and other institutions implementing conservation programs.     

Forest Management Patterns in the Floodplain of the Amazon Estuary

In the Amazon basin, few studies have focused on environmentally sound land-use alternatives that are linked to markets. This paper analyzes land uses carried out by traditional inhabitants ( ribeirinhos ) on three islands in the Amazon estuary. Management of the native floodplain forest is the most extensive form of land use on these islands. Such management varies in response to local economic and ecological conditions but invariably targets key nontimber forest resources destined for local markets, requires minimal input of labor and capital, and maintains a considerable stock of biotic resources. While restricted to a specific forest type, this form of land use is associated with relatively high population densities and, if supported by appropriate development policies, could provide an environmentally sound land-use alternative for hundreds of thousands of rural inhabitants of Amazonia.     

Simulating the impacts of land-use changes on non-point source pollution in Lugu Lake watershed

Protection of the water quality of Lugu Lake is important because it is a unique geographic and cultural resource. Not only point source pollution but also non-point source pollution contribute to degradation of water quality. A GIS-NPS model, with long-term hydrologic impact assessment (L-THIA), was used to evaluate long-term implications of land-use change impacts on non-point source (NPS) pollution. The land-use patterns of 1995 and 2005 were analysed to determine the changes in Lugu Lake watershed. A 30-year (1974–2003) precipitation dataset was used to estimate mean annual surface runoff and NPS pollutant loads. The contributions of different land-use categories to average annual runoff and NPS pollutant production were assessed with a unit contribution index (UCI). Results show loss of agricultural land (by 44.9%), while forest, grass/pasture and residential land increased to different degrees from 1995 to 2005. At the same time, annual average NPS pollutants, TN, TP, TSS and BOD loads all decreased, while heavy metal lead increased by 6.87%. The UCI formulated in this research was a more useful method to assess land-use impact on NPS pollutants than simple investigations of the percentage land-use change. Agricultural and residential land changes had more impact on NPS pollutants and were identified as the main source types. Suggestions on regulating land uses and management proposals for protecting lake water quality in Lugu Lake watershed are made.     

Risk,Risk Aversion,and On-Farm Soil Depletion

This paper studies the production and attendant soil depletion choices of a risk-averse farmer in two related models. The first is a two-date model with uncertainty in both production and end-of-period land price. The second is a three-date model in which production and consumption choices are made in both periods, but there is uncertainty only in the second period. In both models, the paper identifies plausible conditions under which a higher level of initial farmer wealth and/or a lower level of production and land risk lead to a lower level of output and, as a result, a lower level of output-induced soil depletion.     

Plant-trait-based modeling assessment of ecosystem-service sensitivity to land-use change

Evidence is accumulating that the continued provision of essential ecosystem services is vulnerable to land-use change. Yet, we lack a strong scientific basis for this vulnerability as the processes that drive ecosystem-service delivery often remain unclear. In this paper, we use plant traits to assess ecosystem-service sensitivity to land-use change in subalpine grasslands. We use a trait-based plant classification (plant functional types, PFTs) in a landscape modeling platform to model community dynamics under contrasting but internally consistent land-use change scenarios. We then use predictive models of relevant ecosystem attributes, based on quantitative plant traits, to make projections of ecosystem-service delivery. We show that plant traits and PFTs are effective predictors of relevant ecosystem attributes for a range of ecosystem services including provisioning (fodder), cultural (land stewardship), regulating (landslide and avalanche risk), and supporting services (plant diversity). By analyzing the relative effects of the physical environment and land use on relevant ecosystem attributes, we also show that these ecosystem services are most sensitive to changes in grassland management, supporting current agri-environmental policies aimed at maintaining mowing of subalpine grasslands in Europe.     

Using the land transformation model to forecast vacant land

Growing or shrinking cities can experience increases in vacant land. As urban populations and boundaries fluctuate, holes can open in once tight urban areas. Many cities chase growth-oriented approaches to dealing with vacancies. It is critical to understand land-use alteration to accurately predict transformations of physical change in order to make better informed decisions about this phenomenon. This research utilizes the land transformation model (LTM), an artificial neural networking mechanism in Geographic Information Systems, to forecast vacant land. Variable influence on vacant land prediction and accuracy of the LTM is assessed by comparing input factors and patterns, using time-series data from 1990 to 2010 in Fort Worth, Texas, USA. Results indicate that the LTM can be useful in simulating vacant land-use changes but more precise mechanisms are necessary to increase accuracy. This will allow for more proactive decisions to better regulate the process of urban decline and regeneration.     

Environmental filtering and land-use history drive patterns in biomass accumulation in a mediterranean-type landscape

Aboveground biomass (AGB) reflects multiple and often undetermined ecological and land-use processes, yet detailed landscape-level studies of AGB are uncommon due to the difficulty in making consistent measurements at ecologically relevant scales. Working in a protected mediterranean-type landscape (Jasper Ridge Biological Preserve, California, USA), we combined field measurements with remotely sensed data from the Carnegie Airborne Observatory's light detection and ranging (lidar) system to create a detailed AGB map. We then developed a predictive model using a maximum of 56 explanatory variables derived from geologic and historic-ownership maps, a digital elevation model, and geographic coordinates to evaluate possible controls over currently observed AGB patterns. We tested both ordinary least-squares regression (OLS) and autoregressive approaches. OLS explained 44% of the variation in AGB, and simultaneous autoregression with a 100-m neighborhood improved the fit to an r2 = 0.72, while reducing the number of significant predictor variables from 27 variables in the OLS model to 11 variables in the autoregressive model. We also compared the results from these approaches to a more typical field-derived data set; we randomly sampled 5% of the data 1000 times and used the same OLS approach each time. Environmental filters including incident solar radiation, substrate type, and topographic position were significant predictors of AGB in all models. Past ownership was a minor but significant predictor, despite the long history of conservation at the site. The weak predictive power of these environmental variables, and the significant improvement when spatial autocorrelation was incorporated, highlight the importance of land-use history, disturbance regime, and population dynamics as controllers of AGB.     

How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10–20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.     

Evaluating land-use suitability of an industrial city in northeast China

Distribution of industrial land use has a crucial influence on the regional environment. Nanfen is a developing industrial city affiliated with Benxi City, northeastern China. The suitability of industrial land use in Nanfen was evaluated to provide a scientific basis for industrial land-use planning and to minimise negative impacts on the local environment. Multi-criteria evaluation (MCE) within GIS was employed to derive a suitability map. The suitability map was compared with current industrial land use to identify areas for relocation of current industries. A total of 25.7% of the urban area in Nanfen was rated as suitable, 28.1% as somewhat suitable, 37.9% as unsuitable and 8.2% as most unsuitable. About 20.6% of the current industrial land was in suitable areas, 46.5% in somewhat suitable areas and 32.9% in unsuitable areas. We further verified the influence of selected factors on the suitability of industrial land use using a field survey. The results confirmed that industrial land-use relocation in Nanfen is required. This study uses ecological suitability evaluation as a basis for land-use planning and has implications for other industrial cities in northeast China that have similar environmental and ecological problems.     

The land-cover cascade: relationships coupling land and water

We introduce the land-cover cascade (LCC) as a conceptual framework to quantify the transfer of land-cover-disturbance effects to stream biota. We hypothesize that disturbance is propagated through multivariate systems through key variables that transform a disturbance and pass a reorganized disturbance effect to the next hierarchical level where the process repeats until ultimately affecting biota. We measured 31 hydrologic, geomorphic, erosional, and substrate variables and 26 biotic responses that have been associated with land-use disturbance in third- and fourth-order streams in the Blue Ridge physiographic province in western North Carolina (USA). Regression analyses reduced this set of variables to include only those that responded to land cover and/or affected biota. From this reduced variable set, hypotheses were generated that predicted the disturbance pathways affecting each biotic response following the land-cover-cascade design. Cascade pathways began with land cover and ended with biotic responses, passing through at least one intermediate ecosystem abiotic component. Cascade models were tested for predictive ability and goodness-of-fit using path analysis. Biota were influenced by near-stream urban, agricultural, and forest land cover as propagated by hydrologic (e.g., discharge), geomorphic (e.g., stream bank height), erosional (e.g., suspended sediments), and depositional streambed (e.g., substrate size) features occurring along LCC pathways, reflecting abiotic mechanisms mediating land-cover disturbance. Our results suggest that communities are influenced by land-cover change indirectly through a hierarchy of associated abiotic components that propagate disturbance to biota. More generally, the land-cover cascade concept and experimental framework demonstrate an organized approach to the generic study of cascades and the complex relationships between landscapes and streams.     

Effects of rarity form on species’ responses to land use

Anthropogenic land-use change causes substantial changes in local and global biodiversity. Rare and common species can differ in sensitivity to land-use change; rare species are expected to be affected more negatively. Rarity may be defined in terms of geographic range size, population density, or breadth of habitat requirements. How these 3 forms of rarity interact in determining global responses to land use is yet to be assessed. Using global data representing 912 vertebrate species, we tested for differences in responses to land use of species characterized by different types of rarity. Land-use responses were fitted using generalized linear mixed-effects models, allowing responses to vary among groups of species with different forms of rarity. Species considered rare with respect to all 3 forms of rarity showed particularly strong declines in disturbed land uses (>40% of species and 30% of individuals in the most disturbed land uses). In contrast, species common both geographically and numerically and with broad habitat requirements showed strong increases (up to 90% increase in species and 40% in abundance in some land uses). Our results suggest that efforts to understand the vulnerability of species to environmental changes should account for different types of rarity where possible. Our results also have potentially important implications for ecosystem functioning, given that rare species may play unique roles within ecosystems.