Avian biodiversity is threatened, and in order to prioritize limited conservation resources and conduct effec- tive conservation planning a better understanding of avian species richness patterns is needed. The use of image texture measures, as a proxy for the spatial structure of land cover and vegetation, has proven useful in explaining patterns of avian abundance and species richness. However, prior studies that modeled habitat with texture measures were conducted over small geographical extents and typically focused on a single habitat type. Our goal was to evaluate the performance of texture measures over broad spatial extents and across multiple habitat types with varying levels of vertical habitat structure. We calculated a suite of texture measures from 114 Landsat images over a study area of 1,498,000 km^2 in the Midwestern United States, which included habitats ranging from grassland to forest. Avian species richness was modeled for several functional guilds as a function of image texture. We subsequently compared the explanatory power of texture-only models with models ?tted using landscape composition metrics derived from the National Land Cover Dataset, as well as models ?tted using both texture and composition metrics. Measures of image texture were effective in modeling spatial patterns of avian species richness in multiple habitat types, explaining up to 51% of the variability in species richness of permanent resident birds. In comparison, landscape composition metrics explained up to 56% of the variability in permanent resident species richness. In the most heavily forested ecoregion, texture-measures outperformed landscape metrics, and the two types of measurements were complementary in multivariate models. However, in two out of three ecoregions examined, landscape composition metrics consistently performed slightly better than texture measures, and the variance explained by the two types of measures overlapped considerably. These results show that image texture measures derived from satellite imagery can be an important tool for modeling patterns of avian species richness at broad spatial extents, and thus assist in conservation planning. However, texture measures were slightly inferior to landscape composition metrics in about three-fourths of our models. Therefore texture measures are best considered in conjunction with landscape metrics (if available) and are best used when they show explanatory ability that is complementarity to landscape metrics.
File: Culbert_RSE_Texture_0.pdf
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Institutional settings play a key role in shaping land cover and land use. Our goal was to understand the effects of institutional changes on agricultural land abandonment in different countries of Eastern Europe and the former Soviet Union after the collapse of socialism. We studied 273 800 km2 (eight Landsat footprints) within one agro-ecological zone stretching across Poland, Belarus, Latvia, Lithuania and European Russia. Multi-seasonal Landsat TM/ETMC satellite images centered on 1990 (the end of socialism) and 2000 (one decade after the end of socialism) were used to classify agricultural land abandonment using support vector machines. The results revealed marked differences in the abandonment rates between countries. The highest rates of land abandonment were observed in Latvia (42% of all agricultural land in 1990 was abandoned by 2000), followed by Russia (31%), Lithuania (28%), Poland (14%) and Belarus (13%). Cross-border comparisons revealed striking differences; for example, in the Belarus-Russia cross-border area there was a great difference between the rates of abandonment of the two countries (10% versus 47% of abandonment). Our results highlight the importance of institutions and policies for land-use trajectories and demonstrate that radically different combinations of institutional change of strong institutions during the transition can reduce the rate of agricultural land abandonment (e.g., in Belarus and in Poland). Inversely, our results demonstrate higher abandonment rates for countries where the institutions that regulate land use changed and where the institutions took more time to establish (e.g., Latvia, Lithuania and Russia). Better knowledge regarding the effects of such broad-scale change is essential for understanding land-use change and for designing effective land-use policies. This information is particularly relevant for Northern Eurasia, where rapid land-use change offers vast opportunities for carbon balance and biodiversity, and for increasing agricultural production on previously cultivated lands.
File: erl12_2_024021_0.pdf
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The growth of human populations around protected areas accelerates land conversion and isolation, negatively impacting biodiversity and ecosystem function, and can be exacerbated by immigration. It is often assumed that immigration around protected areas is driven by attraction in the form of economic bene?ts, but in many cases, people may be pushed from their areas of origin toward protected areas. Mitigating the effects of immigration around protected areas necessitates understanding the actual mechanisms causing it, which can be aided by analysis of patterns of land-cover change. Our goal was to identify the reasons for human population growth and land-cover change around the protected areas in the greater Serengeti ecosystem (henceforth ''the park''), and to relate agricultural conversion from 1984-2003 to trends in human demography. We found that conversion of natural habitats to agriculture was greatest closer to the park (up to 2.3% per year), coinciding with the highest rates of human population growth (3.5% per year). Agricultural conversion and population growth were greatest where there was less existing agriculture, and population density was lowest. Lack of unfarmed land farther from the park, coupled with greater poverty near the park, suggest that movement away from areas with high population densities and land scarcity was likely driving immigration near the park, where arable land was available. Our results are essential for conservation planning for one of Africa's hallmark ecosystems, and should encourage further examination of population growth and land-cover trends near protected areas throughout the developing world
File: EstesA_BioCons_LCLUC_Serengeti.pdf
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In central Argentina, the Chinese tree glossy privet (Ligustrum lucidum) is an aggressive invasive species replacing native forests, forming dense stands, and is thus a major conservation concern. Mapping the spread of biological invasions is a necessary first step toward understanding the factors determining invasion patterns. Urban areas may function as propagule sources for glossy privet because it has been used as a landscaping tree for over a century. The objectives of this paper were to 1) map the patterns of glossy privet expansion from 1983 to 2006 using a time series of Landsat TM/ETM + images, and 2) analyze the spatial pattern of glossy privet stands with regard to urban extent. Using six summer Landsat TM images (1983, 1987, 1992, 1997, 2001, and 2006) the expansion of glossy privet was analyzed using Support Vector Machines (SVM), a non-parametric classifier which we applied to a stack of all images simultaneously, a novel approach in its application to monitor non-native tree invasions. We then measured the area of glossy privet in a series of 200-m buffers at increasing distances around urban areas in 1983 and 2006, and compared it with the amount of privet expected in proportion to buffer area. Glossy privet in the study area has spread very rapidly during the 23 years that we studied and the SVM resulted in highly accurate classifications (Kappa Index 0.88, commission error 0.07, omission error 0.16). Between 1983 and 2006 glossy privet area increased 50 times (from 50 to 2500 ha), and 20% of all forest in the study area is now dominated by glossy privet. Most of the glossy privet dominated stands were located within 600 m of urban areas. However, the rate of glossy privet expansion accelerated substantially after 1992 and new glossy privet dominated stands tend to be located away from urban areas. This suggests that glossy privet is now self-sustaining, but expected urban growth in the area could further foster glossy privet invasion. Management and development plans should include mitigation efforts to contain this species and prevent invasion into native forests, and citizens should be informed about the risk of invasion associated with the use of glossy privet for landscaping.
File: Gavier_etal_2012_RSE.pdf
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Habitat connectivity is important for the survival of species that occupy habitat patches too small to sus- tain an isolated population. A prominent example of such a species is the European bison (Bison bonasus), occurring only in small, isolated herds, and whose survival will depend on establishing larger, well-con- nected populations. Our goal here was to assess habitat connectivity of European bison in the Carpathi- ans. We used an existing bison habitat suitability map and data on dispersal barriers to derive cost surfaces, representing the ability of bison to move across the landscape, and to delineate potential con- nections (as least-cost paths) between currently occupied and potential habitat patches. Graph theory tools were then employed to evaluate the connectivity of all potential habitat patches and their relative importance in the network. Our analysis showed that existing bison herds in Ukraine are isolated. How- ever, we identi?ed several groups of well-connected habitat patches in the Carpathians which could host a large population of European bison. Our analysis also located important dispersal corridors connecting existing herds, and several promising locations for future reintroductions (especially in the Eastern Car- pathians) that should have a high priority for conservation efforts. In general, our approach indicates the most important elements within a landscape mosaic for providing and maintaining the overall connec- tivity of different habitat networks and thus offers a robust and powerful tool for conservation planning.
File: Potential_habitat_connectivity.pdf
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Many terrestrial biomes are experiencing intensifying human land use. However, reductions in the intensity of agricultural land use are also common and can lead to agricultural land abandonment. Agricultural land abandonment has strong environmental and socio-economic consequences, but fine-scale and spatially explicit data on agricultural land abandonment are sparse, particularly in developing countries and countries with transition economies, such as the post-Soviet countries of Eastern Europe. Remote sensing can potentially fill this gap, but the satellite-based detection of fallow fields and shrub encroachment is difficult and requires the collection of multiple images during the growing season. The availability of such multi-seasonal cloud-free image dates is often limited. The goal of our study was to determine how much missing Landsat TM/ETM+ images at key times in the growing season affect the accuracy of agricultural land abandonment classification.We selected a study area in temperate Eastern Europe where post-socialist agricultural land abandonment had become widespread and analyzed six near-anniversary cloud-free Landsat images from Spring, Summer and Fall agriculturally defined seasons for a preabandonment- time I (1989) and post-abandonment-time II (1999/2000). Using a factorial experiment, we tested how the classification accuracy and spatial patterns of classified abandonment changed over all possible 49 image-date combinations when mapping both abandoned arable land and abandoned managed grassland. The conditional Kappa of our best overall classification with support vector machines (SVM) was 90% for abandoned arable land and 72% for abandoned managed grassland when all six images were used for the classification. Classifications with fewer image dates resulted in a substantial decrease of the conditional Kappa (from 93 to 54% for abandoned arable land and from to 75 to 50% for abandoned managed grassland). We also observed substantial decrease in accurate detection of land abandonment patterns when we compared our best overall classificationwith the other 48 image date combinations (the Fuzzy Kappa, ameasure of spatial similarity, ranged from 25.8 to 76.3% for abandoned arable land and from 30.4 to 79.5% for abandoned managed grassland). While the accuracy of the different abandonment classes was most sensitive to the number of image dates used for the classification, the seasons captured also mattered, and the importance of specific seasonal image dates varied between the pre- and post-abandonment dates. For abandoned arable land it was important to have at least one Spring or Summer image for pre-abandonment and as many images as possible for postabandonment, with a Spring image again being most important. For abandoned managed grassland no specific seasonal image dates yielded statistically significantly more accurate classifications. The factor that influenced the accurate detection of abandoned managed grassland was the number of multi-seasonal image dates (the more the better), rather than their exact dates.We also tested whether SVM performed better than the maximum likelihood classifier. SVMoutperformed the maximum likelihood classifier only for abandoned arable land and only in image-date-rich cases. Our results showed that limited image-date availability in the Landsat record placed substantial limits on the accuracy of agricultural abandonment classifications and accurately detected agricultural land abandonment patterns. Thus, we warn to use agricultural land abandonment maps produced with the suboptimal image dateswith caution, especiallywhen the accurate rates and the patterns of agricultural land abandonment are crucial (e.g., for LULCC models). The abundance of agricultural abandonment in many parts of the world and its strong ecological and socio-economic consequences suggest that better monitoring of abandonment is necessary, and our results illustrated the image dates that were most important to accomplishing this task.
File: prishchepov.jpg
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Changes in land use and land cover have affected and will continue to affect biological diversity worldwide. Yet, understanding the spatially extensive effects of land-cover change has been challenging because data that are consistent over space and time are lacking. We used the U.S. National Land Cover Dataset Land Cover Change Retrofit Product and North American Breeding Bird Survey data to examine land-cover change and its associations with diversity of birds with principally terrestrial life cycles (landbirds) in the conterminous United States. We used mixed-effects models and model selection to rank associations by ecoregion. Land cover in 3.22% of the area considered in our analyses changed from 1992 to 2001, and changes in species richness and abundance of birds were strongly associated with land-cover changes. Changes in species richness and abundance were primarily associated with changes in nondominant types of land cover, yet in many ecoregions different types of land cover were associated with species richness than were associated with abundance. Conversion of natural land cover to anthropogenic land cover was more strongly associated with changes in bird species richness and abundance than persistence of natural land cover in nearly all ecoregions and different covariates were most strongly associated with species richness than with abundance in 11 of 17 ecoregions. Loss of grassland and shrubland affected bird species richness and abundance in forested ecoregions. Loss of wetland was associated with bird abundance in forested ecoregions. Our findings highlight the value of understanding changes in nondominant land cover types and their association with bird diversity in the United States.
File: Rittenhouse_etal_2012_BioInvasions.pdf
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Agriculture is expanding and intensifying in many areas of the world, but abandoned agriculture is also becoming more widespread. Unfortunately, data and methods to monitor abandoned agriculture accurately over large areas are lacking. Remote sensing methods may be able to ?ll this gap though, especially with the frequent observations provided by coarser-resolution sensors and new classi?cation techniques. Past efforts to map abandoned agriculture relied mainly on Landsat data, making it hard to map large regions, and precluding the use of phenology information to identify abandoned agriculture. Our objective here was to test methods to map abandoned agriculture at broad scales with coarse-resolution satellite imagery and phenology data. We classi?ed abandoned agriculture for one Moderate Resolution Imaging Spectroradiometer (MODIS) tile in Eastern Europe (~1,236,000 km2 ) where abandoned agriculture was widespread. Input data included Normalized Difference Vegetation Index (NDVI) and re?ectance bands (NASA Global MODIS Terra and Aqua 16-Day Vegetation Indices for the years 2003 through 2008, ~250-m resolution), as well as phenology metrics calculated with TIMESAT. The data were classi?ed with Support Vector Machines (SVM). Training data were derived from several Landsat classi?cations of agricultural abandonment in the study area. A validation was conducted based on independently collected data. Our results showed that it is possible to map abandoned agriculture for large areas from MODIS data with an overall classi?cation accuracy of 65%. Abandoned agriculture was widespread in our study area (15.1% of the total area, compared to 29.6% agriculture). We found strong differences in the MODIS data quality for different years, with data from 2005 resulting in the highest classi?cation accuracy for the abandoned agriculture class (42.8% producer's accuracy). Classi?cations of MODIS NDVI data were almost as accurate as classi?cations based on a combination of both red and near-infrared re?ectance data. MODIS NDVI data only from the growingseason resulted in similar classi?cation accuracy as data for the full year. Using multiple years of MODIS data did not increase classi?cation accuracy. Six phenology metrics derived with TIMESAT from the NDVI time series (2003-2008) alone were insuf?cient to detect abandoned agriculture, but phenology metrics improved classi?cation accuracies when used in conjunction with NDVI time series by more than 8% over the use of NDVI data alone. The approach that we identi?ed here is promising and suggests that it is possible to map abandoned agriculture at broad scales, which is relevant to gain a better understanding of this important land use change process
File: alcantara_etal_2012.pdf
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This paper uses remote sensing data from 1989 to 2000 to examine the impacts of price liberalization, land tenure, and biophysical characteristics on farmland abandonment in the border region of Poland, Slovakia, and Ukraine. Using regression analysis and matching estimators, we ?nd that differences in biophysical characteristics, rather than in tenure systems, best explain the variation in abandonment rates within Poland. The difference in abandonment rates between Poland and Slovakia partially results from differences in land reform strategy, and abandonment in Ukraine takes a unique trajectory because of the incompleteness of the land reform and the lack of outside opportunities for resident
File: Alix-Garcia_etal_2012_LandEcon.pdf
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Lightning fires are a common natural disturbance in North America, and account for the largest proportion of the area burned by wildfires each year. Yet, the spatiotemporal patterns of lightning fires in the conterminous US are not well understood due to limitations of existing fire databases. Our goal here was to develop and test an algorithm that combined MODIS fire detections with lightning detections from the National Lightning Detection Network to identify lightning fires across the conterminous US from 2000 to 2008. The algorithm searches for spatiotemporal conjunctions of MODIS fire clusters and NLDN detected lightning strikes, given a spatiotemporal lag between lightning strike and fire ignition. The algorithm revealed distinctive spatial patterns of lightning fires in the conterminous US While a sensitivity analysis revealed that the algorithm is highly sensitive to the two thresholds that are used to determine conjunction, the density of fires it detected was moderately correlated with ground based fire records. When only fires larger than 0.4 km2 were considered, correlations were higher and the root-mean-square error between datasets was less than five fires per 625 km2 for the entire study period. Our algorithm is thus suitable for detecting broad scale spatial patterns of lightning fire occurrence, and especially lightning fire hotspots, but has limited detection capability of smaller fires because these cannot be consistently detected by MODIS. These results may enhance our understanding of large scale patterns of lightning fire activity, and can be used to identify the broad scale factors controlling fire occurrence.
File: BarMassada_etal_2012_IEEE.pdf
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