Predicting land cover changes and their impact on the sediment influx in the Lake Balaton catchment

The land cover pattern in the Lake Balaton catchment (Hungary) has been changing since decollectivization in the 1990s. These land cover changes significantly impact the landscape connectivity, controlling the influx of sediments into the lake. A comparison of high resolution land cover maps from 1981, 2000 and 2005 showed a significant extensification of the agriculture with land cover conversions from arable land and vineyards to grassland and forest. For each land unit transition probabilities were assessed using logistic regression techniques to evaluate to which extent land cover changes are controlled by physical or socio-economic parameters. A stochastic land cover allocation algorithm was applied to generate future land cover patterns. The landscape connectivity for each of the simulated land cover patterns was assessed by means of a distributed routing algorithm. The simulations suggest that further land abandonment in the upslope parts of the catchment will cause a non-linear reduction of average soil erosion rates. The changes, however, have a relatively low impact on the sediment volume entering the lake because of the land unit’s poor connectivity with permanent river channels. The major contributors to the lakes sediment load are the vineyards near the lakeshore. They are likely to be maintained because of their touristic value. A significant reduction of the total sediment input in the lake can be expected only if soil conservation measures in the vineyards near the shorelines are undertaken.     

Prediction of multinomial probability of land use change using a bisection decomposition and logistic regression

Land use change is an important research area in landscape ecology and urban development. Prediction of land use change (urban development) provides critical information for making the right policies and management plans in order to maintain and improve ecosystem and city functions. Logistic regression is a widely used method to predict binomial probabilities of land use change when just two responses (change and no-change) are considered. However, in practice, more than two types of change are encountered and multinomial probabilities are therefore needed. The existing methods for predicting multinomial probabilities have limits in building multinomial probability models and are often based on improper assumptions. This is due to the lack of proper methodology and inadequate software. In this study, a procedure has been developed for building models to predict the multinomial probabilities of land use change and urban development. The foundation of this procedure consists of a special bisection decomposition system for the decomposition of multiple-class systems to bi-class systems, conditional probability inference, and logistic regression for binomial probability models. A case study of urban development has been conducted to evaluate this procedure. The evaluation results demonstrated that different samples and bisection decomposition systems led to very similar quality and performance in the developed multinomial probability models, which indicates the high stability of the proposed procedure for this case study.     

Multi-scale responses of bird species to tree cover and development in an urbanizing landscape

Landscape change is an ongoing process for even the most established landscapes, especially in context to urban intensification and growth. As urbanization increases over the next century, supporting bird species’ populations within urbanizing areas remains an important conservation challenge. Fundamental elements of the biophysical structure of urban environments in which bird species likely respond include tree cover and human infrastructure. We broadly examine how tree cover and urban development structure bird species distributions along the urban-rural gradient across multiple spatial scales. We established a regional sampling design within the Oak Openings Region of northwestern, Ohio, USA, to survey bird species distributions across an extensive urbanization gradient. Through occupancy modeling, we obtained standardized effects of bird species response to local and landscape-scale predictors and found that landscape tree cover influenced the most species, followed by landscape impervious surface, local building density, and local tree cover. We found that responses varied according to habitat affiliation and migratory distance of individual bird species. Distributions of short-distance, edge habitat species located towards the rural end of the gradient were explained primarily by low levels of urbanization and potential vegetative and supplemental resources associated with these areas, while forest species distributions were primarily related to increasing landscape tree cover. Our findings accentuate the importance of scale relative to urbanization and help target where potential actions may arise to benefit bird diversity. Management will likely need to be implemented by municipal governments and agencies to promote tree cover at landscape scale, followed by residential land management education for private landowners. These approaches will be vital in sustaining biodiversity in urbanizing landscapes as urban growth expands over the next century.     

Predicting land use change: comparison of models based on landowner surveys and historical land cover trends

To make informed planning decisions, community leaders, elected officials, scientists, and natural resource managers must be able to evaluate potential effects of policies on land use change. Many land use change models use remotely-sensed images to make predictions based on historical trends. One alternative is a survey-based approach in which landowners’ stated intentions are modeled. The objectives of our research were to: (1) develop a survey-based landowner decision model (SBM) to simulate future land use changes, (2) compare projections from the SBM with those from a trend-based model (TBM), and (3) demonstrate how two alternative policy scenarios can be incorporated into the SBM and compared. We modeled relationships between land management decisions, collected from a mail survey of private landowners, and the landscape, using remotely-sensed imagery and ownership parcel data. We found that SBM projections were within the range of TBM projections and that the SBM was less affected by errors in image classification. Our analysis of alternative policies demonstrates the importance of understanding potential effects of targeted land use policies. While policies oriented toward increasing enrollment in the Conservation Reserve Program (CRP) resulted in a large (11–13%) increase in CRP lands, policies targeting increased forest thinning on private non-industrial lands increased low-density forest projections by only 1%. The SBM approach is particularly appropriate for landscapes including many landowners, because it reflects the decision-making of the landowners whose individual actions will result in collective landscape change.     

Urban forest structure and land cover composition effects on land surface temperature in a semi-arid suburban area

Using multispectral imagery and LiDAR data, we developed a high-resolution land cover dataset for a semi-arid, Colorado (USA) suburb. These data were used to evaluate patterns of land cover composition and vertical structure in relation to land use and age of development. Landsat 5 TM thermal band data for six separate dates were used to compare land surface temperature (LST) in urbanized and remnant shortgrass steppe reference areas. We used 2010 census blocks to extract LST and various explanatory variables for use in Random Forest models evaluating the relative importance of land cover composition, LiDAR-derived vertical structure variables, and the Normalized Difference Vegetation Index (NDVI) on LST patterns.We found that land cover, vertical structure, and LST varied between areas with different land use and neighborhood age. Older neighborhoods supported significantly higher tree cover and mean tree height, but differences in LST were inconsistent between Landsat image dates. NDVI had the highest variable importance in Random Forests models, followed by tree height and the mean height difference between trees and buildings. Models incorporating NDVI, vertical structure, and land cover had the highest predictive accuracy but did not perform significantly better than models using just vertical structure and NDVI. Developed areas were cooler on average than shortgrass steppe reference areas, likely due to the influence of supplemental irrigation in urbanized areas. Patterns of LST were spatially variable, highlighting the complex ways land cover composition and vertical structure can affect urban temperature.     

Declining urban and community tree cover in the United States

Paired aerial photographs were interpreted to assess recent changes (c. 2009–2014) in tree, impervious and other cover types within urban/community and urban land in all 50 United States and the District of Columbia. National results indicate that tree cover in urban/community areas of the United States is on the decline at a rate of about 175,000 acres per year, which corresponds to approximately 36 million trees per year. Estimated loss of benefits from trees in urban areas is conservatively valued at $96 million per year. Overall, for both urban and the broader urban/community areas, 23 states/districts had statistically significant declines in tree cover, 25 states had non-significant decreases or no change in tree cover, and three states showed a non-significant increase in tree cover. The most intensive change occurred within urban areas, with tree cover in these areas dropping one percent over the 5-year period, compared to a 0.7 percent drop in urban/community areas. States/districts with the greatest statistically significant annual decline in percent urban tree cover were: Oklahoma (−0.92%/yr), District of Columbia (−0.44%/yr), Rhode Island (−0.40%/yr), Oregon (−0.38%/yr) and Georgia (−0.37%/yr). Coinciding with the loss of tree cover was a gain in impervious cover, with impervious cover increasing 0.6 percent in urban/community areas and 1.0 percent in urban areas over the 5-year period. Such changes in cover types affect the benefits derived from urban forests and consequently the health and well-being of urban residents.     

An investigation of uncertainty in field habitat mapping and the implications for detecting land cover change

Land cover data for landscape ecological studies are frequently obtained by field survey. In the United Kingdom, temporally separated field surveys have been used to identify the locations and magnitudes of recent changes in land cover. However, such map data contain errors which may seriously hinder the identification of land cover change and the extent and locations of rare landscape features. This paper investigates the extent of the differences between two sets of maps derived from field surveys within the Northumberland National Park in 1991 and 1992. The method used in each survey was the Phase 1 approach of the Nature Conservancy Council of Great Britain. Differences between maps were greatest for the land cover types with the smallest areas. Overall spatial correspondence between maps was found to be only 44.4%. A maximum of 14.4% of the total area surveyed was found to have undergone genuine land cover change. The remaining discrepancies, equivalent to 41.2% of the total survey area, were attributed primarily to differences of land cover interpretation between surveyors (classification error). Differences in boundary locations (positional error) were also noted, but were found to be a relatively minor source of error. The implications for the detection of land cover change and habitat mapping are discussed.