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.
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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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The political breakdown of the Soviet Union in 1991 provides a rare case of drastic changes in social and economic conditions, and as such a great opportunity to investigate the impacts of socioeconomic changes on the rates and patterns of forest harvest and regrowth. Our goal was to characterize forest-cover changes in the temperate zone of European Russia between 1985 and 2010 in 5-year increments using a strati?ed random sample of 12 Landsat footprints. We used Support Vector Machines and post-classi?cation comparison to monitor forest area, disturbance and reforestation. Where image availability was sub-optimal, we tested whether winter images help to improve classi?cation accuracy. Our approach yielded accurate mono-temporal maps (on average >95% overall accuracy), and change maps (on average 93.5%). The additional use of winter imagery improved classi?- cation accuracy by about 2%. Our results suggest that Russia's temperate forests underwent substantial changes during the observed period. Overall, forested areas increased by 4.5%, but the changes in forested area varied over time: a decline in forest area between 1990 and 1995 (?1%) was followed by an increase in overall forest area in recent years (+1.4%, 2005-2010), possibly caused in part by forest regrowth on abandoned farmlands. Disturbances varied greatly among administrative regions, suggesting that differences in socioeconomic conditions strongly in?uence disturbance rates. While portions of Russia's temperate forests experienced high disturbance rates, overall forest area is expanding. Our use of a strati?ed random sample of Landsat footprints, and of summer and winter images, allowed us to characterize forest dynamics across a large region over a long time period, emphasizing the value of winter imagery in the free Landsat archives, especially for study areas where data availability is limited.
File: Baumann-etal_2012_Using-the-landsat-record-to-detect-fcc-in-the-tempreate-zone-of-European-Russia_0.pdf
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Forest cover change is one of the most important land cover change processes globally, and old-growth forests continue to disappear despite many efforts to protect them. At the same time, many countries are on a trajectory of increasing forest cover, and secondary, plantation, and scrub forests are a growing proportion of global forest cover. Remote sensing is a crucial tool for understanding how forests change in response to forest protection strategies and economic development, but most forest monitoring with satellite imagery does not distinguish old-growth forest from other forest types. Our goal was to measure changes in forest types, and especially old-growth forests, in the biodiversity hotspot of northwest Yunnan in southwest China. Northwest Yunnan is one of the poorest regions in China, and since the 1990s, the Chinese government has legislated strong forest protection and fostered the growth of ecotourism-based economic development. We used Landsat TM/ETM+ and MSS images, Support Vector Machines, and a multi-temporal composite classi?cation technique to analyze change in forest types and the loss of old-growth forest in three distinct periods of forestry policy and ecotourism development from 1974 to 2009. Our analysis showed that logging rates decreased substantially from 1974 to 2009, and the proportion of forest cover increased from 62% in 1990 to 64% in 2009. However, clearing of high-diversity old-growth forest accelerated, from approximately 1100 hectares/year before the logging ban (1990 to 1999), to 1550 hectares/year after the logging ban (1999 to 2009). Paradoxically, old-growth forest clearing accelerated most rapidly where ecotourism was most prominent. Despite increasing overall forest cover, the proportion of old-growth forests declined from 26% in 1990, to 20% in 2009. The majority of forests cleared from 1974 to 1990 returned to either a nonforested land cover type (14%) or non-pine scrub forest (66%) in 2009, and our results suggest that most non-pine scrub forest was not on a successional trajectory towards high-diversity forest stands. That means that despite increasing forest cover, biodiversity likely continues to decline, a trend obscured by simple forest versus non-forest accounting. It also means that rapid development may pose inherent risks to biodiversity, since our study area arguably represents a best-case scenario for balancing development with maintenance of biodiversity, given strong forest protection policies and an emphasis on ecotourism development
File: brandt2012.pdf
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Conservation efforts should be based on habitat models that identify areas of high quality and that are built at spatial scales that are ecologically relevant. In this study, we developed habitat models for the Loggerhead Shrike (Lanius ludovicianus) in the Chihuahuan Desert of New Mexico to answer two questions: (1) are highly used habitats of high quality for shrikes in terms of individual fitness? and (2) what are the spatial scales of habitat associations relevant to this species? Our study area was Fort Bliss Army Reserve (New Mexico). Bird abundance was obtained from 10 min point counts conducted at forty-two 108 ha plots during a 3-year period. Measures of fitness were obtained by tracking a total of 73 nests over the 3 years. Habitat variables were measured at spatial scales ranging from broad to intermediate to local. We related habitat use and measures of fitness to habitat variables using Bayesian model averaging. We found a significant relationship between bird abundance and measures of fitness averaged across nesting birds in each plot (correlation up to 0.61). This suggests that measures of habitat use are indicative of habitat quality in the vicinity of Fort Bliss. Local- and intermediate-scale variables best explained shrike occurrence. Habitat variables were not related to any measures of fitness. A better understanding of the factors that limit individual bird fitness is therefore necessary to identify areas of high conservation value for this species.
File: StLouis-etAl-LandscapeEcology-2010.pdf
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The majority of landscape pattern studies are based on the patch-mosaic paradigm, in which habitat patches are the basic unit of the analysis. While many patch-based landscape indices successfully relate spatial patterns to ecological processes, it is also desirable to use finer grained analyses for understanding species presence, abundance, and movement patterns across the landscape and to describe spatial context by measuring habitat change across scales. Here, we introduce two multi-scale pixel-based approaches for spatial pattern analysis, which quantify the spatial context of each pixel in the landscape. Both approaches summarize the proportion of habitat at increasing window sizes around each pixel in a scalogram. In the first regressionbased approach, a third-order polynomial is fitted to the scalogram of each pixel, and the four polynomial coefficients are used as descriptors of spatial context of each pixel within the landscape mosaic. In the second shape-based approach, the scalogram mean and standard deviation, and the mean slope between forest cover at the smallest window size and each of the larger window sizes are calculated. The values emerging from these two approaches are assigned to each focal pixel and can be used as predictive variables, for example, in species presence and abundance studies. We tested the performance of these approaches on 18 random landscapes and nine actual landscapes with varying forest habitat cover. Results show that both methods were able to differentiate between several spatial contexts. We thus suggest that these approaches could serve as a complement or an alternative to existing methods for landscape pattern analysis and possibly add further insight into pattern-species relations.
File: BarMassada_LE_2010.pdf
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