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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The effectiveness of protected areas can diminish during times of pronounced socio-economic and insti- tutional change. Our goals were to assess the effectiveness of Romanian protected areas at stemming unsanctioned logging, and to assess post-socialist logging in their surrounding landscapes, during a time of massive socio-economic and institutional change. Our results suggest that forest cover remained fairly stable shortly before and after 1990, but forest disturbance rates increased sharply in two waves after 1995 and 2005. We found substantial disturbances inside protected areas, even within core reserve areas. Moreover, disturbances in the matrix surrounding protected areas were even lower than inside protected area boundaries. We suggest that these rates are largely the result of high logging rates, triggered by rapid ownership and institutional changes. These trends compromise the goals of Romania's protected area network, lead to an increasing loss of forest habitat, and more isolated and more fragmented protected areas. The effectiveness of Romania's protected area network in terms of its ability to safeguard biodiver- sity is therefore most likely decreasing.
File: Knorn_etal_BioCons_Romania.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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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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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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Land-use change is affecting Earth's capacity to support both wild species and a growing human population. The question is how best to manage landscapes for both species conservation and economic output. If large areas are protected to conserve species richness, then the unprotected areas must be used more intensively. Likewise, low-intensity use leaves less area protected but may allow wild species to persist in areas that are used for market purposes. This dilemma is present in policy debates on agriculture, housing, and forestry. Our goal was to develop a theoretical model to evaluate which land-use strategy maximizes economic output while maintaining species richness. Our theoretical model extends previous analytical models by allowing land-use intensity on unprotected land to influence species richness in protected areas. We devised general models in which species richness (with modified species-area curves) and economic output (a Cobb-Douglas production function) are a function of land-use intensity and the proportion of land protected. Economic output increased as land-use intensity and extent increased, and species richness responded to increased intensity either negatively or following the intermediate disturbance hypothesis. We solved the model analytically to identify the combination of land-use intensity and protected area that provided the maximum amount of economic output, given a target level of species richness. The land-use strategy that maximized economic output while maintaining species richness depended jointly on the response of species richness to land-use intensity and protection and the effect of land use outside protected areas on species richness within protected areas. Regardless of the land-use strategy, species richness tended to respond to changing land-use intensity and extent in a highly nonlinear fashion.
File: Butsic_etal_2012_ConsBio.pdf
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Land use change is a leading cause of environmental degradation in amenity rich rural areas. Numerous policies have been used to combat these negative effects, including zoning and land acquisition. The empirical effects of these policies on the environment and land markets are still debated. Using a coupled economic-ecological model in conjunction with landscape simulations we investigate the effect of zoning and land acquisition on property prices and largemouth bass (Micropterus salmoides) growth in Vilas County, WI, an amenity rich region with growing rural development. Using econometric models of land use change and property prices, we simulate four alternative land use scenarios: a baseline simulation, a zoning change simulation, a land acquisition program simulation, and a land acquisition program + zoning simulation. Each scenario is simulated over 82 separate lakes. For each scenario we calculate the length of a 20-year old largemouth bass, property prices, and number of new residences at simulation years 20, 40 and 60. The policies have small effects on largemouth bass size and property prices on most lakes, although the effects are more pronounced on some. We also test if the increased property values due to land acquisitions are greater than the cost of the land acquisition program and find that in our case, land acquisition does not pay for itself. Our methodology provides a means to untangle the complex interactions between policy, land markets, and the environment. Empirically, our results indicate zoning and land acquisition are likely most effective when targeted to particular lakes.
File: Butsic_largemouth.pdf
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Quantifying changes in forest bird diversity is an essential task for developing effective conservation actions. However, comparisons of changes in diversity between samples adjacent in time (i.e., successive change) may mask substantial shifts in diversity that occurs over time (i.e., progressive change) when short-term analyses are used to assess change (i.e., shifting baseline syndrome). Our objectives were to determine how forest bird diversity changed over time and whether those changes were associated with forest disturbance. We used North American Breeding Bird Survey data, a time series of Landsat images classified with respect to land cover change, and mixed-effects models to associate changes in forest bird community structure with forest disturbance, latitude, and longitude in the U.S. for the years 1985 to 2006. We document a significant divergence from the baseline structure for all birds of similar migratory habit and nest location, and all forest birds as a group from 1985 to 2006. Unexpectedly, decreases in progressive similarity resulted from small gains in richness (<1 species per route for the 22-year study period) and modest losses in abundance (-69.7 - -10.2 individuals per route) that varied by migratory habit and nest location. Forest disturbance increased progressive similarity for all forest birds as a group, and for Neotropical migrants, permanent residents, and cavity nesting species. We also documented highest progressive similarity in the southern and eastern U.S. Contemporary forest bird community structure is changing rapidly over a relatively short period of time (e.g., ~22 years). Forest disturbance and forest regeneration are primary factors affect contemporary forest bird community structure, longitude and latitude are secondary factors, and forest loss is a tertiary factor. Importantly, these findings suggest some regions of the U.S. may already exceed the threshold below which forest loss is an important predictor of forest bird community structure.
File: RittenhouseCDetal2010Forestbirdsshiftingbaseline_0.pdf
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