Illegal logging is a major environmental and economic problem, and exceeds in some countries the amounts of legally harvested timber. In Eastern Europe and the former Soviet Union, illegal logging increased and reforestation on abandoned farmland was widespread after the breakdown of socialism, and the region's forest cover trends remain overall largely unclear. Our goal here was to map forest cover change and to assess the extent of illegal logging and reforestation in the Ukrainian Carpathians. We used Landsat TM/ETM+ images and Support Vector Machines (SVM) to derive forest change trajectories between 1988 and 2007 for the entire Ukrainian Carpathians. We calculated logging and reforestation rates, and compared Landsatbased forest trends to official statistics and inventory maps. Our classification resulted in reliable forest/nonforest maps (overall accuracies between 97.1%-98.01%) and high clear cut detection rates (on average 89.4%). Forest cover change was widespread in the Ukrainian Carpathians between 1988 and 2007. We found forest cover increase in peripheral areas, forest loss in the interior Carpathians, and increased logging in remote areas. Overall, our results suggest that unsustainable forest use from socialist times likely persisted in the post-socialist period, resulting in a continued loss of older forests and forest fragmentation. Landsat-based forest trends differed substantially from official forest resource statistics. Illegal logging appears to have been at least as extensive as documented logging during the early 1990s and so-called sanitary clear-cuts represent a major loophole for overharvesting and logging in restricted areas. Reforestation and illegal logging are frequently not accounted for in forest resource statistics, highlighting limitations of these data. Combating illegal logging and transitioning towards sustainable forestry requires better monitoring and upto- date accounting of forest resources, in the Carpathians and elsewhere in Eastern Europe, and remote sensing can be a key technology to achieve these goals.
File: Kuemmerle_2009_RSE_0.pdf
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We tested image texture as a predictor of bird species richness in a semi-arid landscape of New Mexico. Bird species richness was summarized from 10-min point counts conducted at 12 points within 42 plots (108 ha each) from 1996 to 1998. We calculated 14 first- and second-order texture measures in eight different window sizes on a set of digital orthophotos acquired in 1996. For each of the 42 plots, we summarized mean and standard deviation of each texture value within multiple window sizes. The relationship between image texture and average bird species richness was assessed using linear regression models. Single image texture measures such as the standard deviation described up to 57% of the variability in species richness. Coupling multiple measures of texture or coupling elevation with a single texture measure described up to 63% of the variability in bird species richness. Models incorporating two measures of texture and coarse habitat type described 76% of the variability in bird species richness. These results show that image texture analysis is a very promising tool for characterizing habitat structure and predicting patterns of species richness in semi-arid ecosystems. This method has several advantages over methods that rely on classified imagery, including cost-effectiveness, incorporation of within-habitat vegetation variability, and elimination of errors associated with boundary delineation.
File: st-louis-rse-2006.pdf
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Eastern Europe has experienced drastic changes in political and economic conditions following the breakdown of the Soviet Union. Furthermore, these changes often differ among neighboring countries. This offers unique possibilities to assess the relative importance of broadscale political and socioeconomic factors on land cover and landscape pattern. Our question was how much land cover differed in the Polish, the Slovak, and the Ukrainian Carpathian Mountains and to what extent these differences can be related to dissimilarities in societal, economic, and political conditions. We used a hybrid classification technique, combining advantages from supervised and unsupervised methods, to derive a land cover map from three Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from 2000. Results showed marked differences in land cover between the three countries. Forest cover and composition was different for the three countries, for example Slovakia and Poland had about 20% more forest cover at higher elevations than Ukraine. Broadleaved forest dominated in Slovakia while high percentages of conifers were found in Poland and Ukraine. Agriculture was most abundant in Slovakia where the lowest level of agricultural fragmentation was found (22% core area compared to less than 5% in Poland and Ukraine). Post-socialist land change was greatest in Ukraine, were we found high agricultural fragmentation and widespread early-successional shrublands indicating extensive land abandonment. Concerning forests, differences can largely be explained by socialist forest management. The abundance and pattern of arable land and grassland can be explained by two factors: land tenure in socialist times and economic transition since 1990. These results suggest that broad-scale socioeconomic and political factors are of major significance for land cover patterns in Eastern Europe, and possibly elsewhere.
File: kuemmerle_etal_2006_RSE.pdf
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Insect defoliation is a key disturbance in many forested ecosystems. Defoliation monitoring is important for both forest managers and scientists. We used 3 Landsat TM images to monitor jack pine budworm (Choristoneura pinus pinus) defoliation in a 450,000 ha study area in northwestern Wisconsin during a recent outbreak (1990-1995). The images were atmospherically corrected and spectral mixture analysis was employed using spectrometer measurements as endmembers. Heavily defoliated stands echibited a 5% increase in TM4 reflectance. This increase was smaller than the pre-outbreak range of jack pine TM4 reflectance caused by hardwood mixtures (1987: 17-28%). Hardwood content was negatively correlated with budworm populations (r = -0.69) and might be useful to predict future population levels. Defoliation could be identified using spectral mixture analysis. The green needle fraction at the peak of the outbreak was negatively correlated with budworm populations (r = -0.94). Spectral mixture analysis allowed reliable jack pine budworm defoliation mapping using Landsat TM imagery and may be applicable in other forested ecosystems as well.
File: Radeloff_etal_RemSensEnv99.pdf
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MODIS active fire data offer new information about global fire patterns. However, uncertainties in detection rates can render satellite-derived fire statistics difficult to interpret.We evaluated theMODIS 1 kmdaily active fire product to quantify detection rates for both Terra andAquaMODIS sensors, examined how cloud cover and fire size affected detection rates, and estimated how detection rates varied across the United States. MODIS active fire detections were compared to 361 reference fires (?18 ha) that had been delineated using pre- and post-fire Landsat imagery. Reference fires were considered detected if at least one MODIS active fire pixel occurred within 1 km of the edge of the fire. When active fire data from both Aqua and Terra were combined, 82% of all reference fireswere found, but detection rateswere less forAqua and Terra individually (73% and 66% respectively). Fires not detected generally had more cloudy days, but not when the Aqua data were considered exclusively. MODIS detection rates decreased with fire size, and the size at which 50% of all fires were detected was 105 ha when combining Aqua and Terra (195 ha for Aqua and 334 ha for Terra alone). Across the United States, detection rates were greatest in theWest, lower in the Great Plains, and lowest in the East. The MODIS active fire product captures large fires in the U.S. well, but may under-represent fires in areas with frequent cloud cover or rapidly burning, small, and low-intensity fires.We recommend that users of the MODIS active fire data perform individual validations to ensure that all relevant fires are included.
File: Hawbaker_RSE_08.pdf
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Aim To investigate the relationships between bird species richness derived from the North American Breeding Bird Survey and estimates of the average, minimum, and the seasonal variation in canopy light absorbance (the fraction of absorbed photosynthetically active radiation, fPAR) derived from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). Location Continental USA. Methods We describe and apply a 'dynamic habitat index' (DHI), which incorporates three components based on monthly measures of canopy light absorbance through the year. The three components are the annual sum, the minimum, and the seasonal variation in monthly fPAR, acquired at a spatial resolution of 1 km, over a 6-year period (2000-05). The capacity of these three DHI components to predict bird species richness across 84 defined ecoregions was assessed using regression models. Results Total bird species richness showed the highest correlation with the composite DHI [R2 = 0.88, P < 0.001, standard error of estimate (SE) = 8 species], followed by canopy nesters (R2 = 0.79, P < 0.001, SE = 3 species) and grassland species (R2 = 0.74, P < 0.001, SE = 1 species). Overall, the seasonal variation in fPAR, compared with the annual average fPAR, and its spatial variation across the landscape, were the components that accounted for most (R2 = 0.55-0.88) of the observed variation in bird species richness. Main conclusions The strong relationship between the DHI and observed avian biodiversity suggests that seasonal and interannual variation in remotely sensed fPAR can provide an effective tool for predicting patterns of avian species richness at regional and broader scales, across the conterminous USA.
File: Coops_2009_JBioGeog_0.pdf
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Land use change is a principal force and inherent element of global environmental change, threatening biodiversity, natural ecosystems, and their services. However, our ability to anticipate future land use change is severely limited by a lack of understanding of how major socio-economic disturbances (e.g., wars, revolutions, policy changes, and economic crises) affect land use. Here we explored to what extent socio-economic disturbances can shift land use systems onto a different trajectory, and whether this can result in less intensive land use. Our results show that the collapse of the Soviet Union in 1991 caused a major reorganization in land use systems. The effects of this socio-economic disturbance were at least as drastic as those of the nuclear disaster in the Chernobyl region in 1986. While the magnitudes of land abandonment were similar in Ukraine and Belarus in the case of the nuclear disaster (28% and 36% of previously farmed land, respectively), the rates of land abandonment after the collapse of the Soviet Union in Ukraine were twice as high as those in Belarus. This highlights that national policies and institutions play an important role in mediating effects of socio-economic disturbances. The socio-economic disturbance that we studied caused major hardship for local populations, yet also presents opportunities for conservation, as natural ecosystems are recovering on large areas of former farmland. Our results illustrate the potential of socio-economic disturbances to revert land use intensi?cation and the important role institutions and policies play in determining land use systems' resilience against such socio-economic disturbances.
File: Hostert-et-al_Chernobyl_ERL_2011.pdf
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Land use is a critical factor in the global carbon cycle, but land-use effects on carbon fluxes are poorly understood in many regions. One such region is Eastern Europe and the former Soviet Union, where land-use intensity decreased substantially after the collapse of socialism, and farmland abandonment and forest expansion have been widespread. Our goal was to examine how land-use trends affected net carbon ?uxes in western Ukraine (57 000 km2) and to assess the region's future carbon sequestration potential. Using satellite-based forest disturbance and farmland abandon- ment rates from 1988 to 2007, historic forest resource statistics, and a carbon bookkeeping model, we reconstructed carbon ?uxes from land use in the 20th century and assessed potential future carbon ?uxes until 2100 for a range of forest expansion and logging scenarios. Our results suggested that the low-point in forest cover occurred in the 1920s. Forest expansion between 1930 and 1970 turned the region from a carbon source to a sink, despite intensive logging during socialism. The collapse of the Soviet Union created a vast, but currently largely untapped carbon sequestration potential (up to = 150 Tg C in our study region). Future forest expansion will likely maintain or even increase the region's current sink strength of 1.48 Tg C yr-1. This may offer substantial opportunities for offsetting industrial carbon emissions and for rural development in regions with otherwise diminishing income opportunities. Through- out Eastern Europe and the former Soviet Union, millions of hectares of farmland were abandoned after the collapse of socialism; thus similar reforestation opportunities may exist in other parts of this region.
File: Kuemmerle-etal_2011_Farmland-abandonment-carbon-sequestration-Ukraine_0.pdf
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Conservation of biodiversity requires information at many spatial scales in order to detect and preserve habitat for many species, often simultaneously. Vegetation structure information is particularly important for avian habitat models and has largely been unavailable for large areas at the desired resolution. Airborne LiDAR, with its combination of relatively broad coverage and ?ne resolution provides existing new opportunities to map vegetation structure and hence avian habitat. Our goal was to model the richness of forest songbirds using forest structure information obtained from LiDAR data. In deciduous forests of southern Wisconsin, USA, we used discrete-return airborne LiDAR to derive forest structure metrics related to the height and density of vegetation returns, as well as composite variables that captured major forest structural elements. We conducted point counts to determine total forest songbird richness and the richness of foraging, nesting, and forest edge-related habitat guilds. A suite of 35 LiDAR variables were used to model bird species richness using best-subsets regression and we used hierarchical partitioning analysis to quantify the explanatory power of each variable in the multivariate models. Songbird species richness was correlated most strongly with LiDAR variables related to canopy and midstory height and midstory density (R 2 =0.204, pb0.001). Richness of species that nest in the midstory was best explained by canopy height variables (R 2 =0.197, pb0.001). Species that forage on the ground responded to mean canopy height and the height of the lower canopy (R 2 =0.149, pb0.005) while aerial foragers had higher richness where the canopy was tall and dense and the midstory more sparse (R 2 =0.216, pb0.001). Richness of edge-preferring species was greater where there were fewer vegetation returns but higher density in the understory (R 2 =0.153, pb0.005). Forest interior specialists responded positively to a tall canopy, developed midstory, and a higher proportion of vegetation returns (R 2 =0.195, pb0.001). LiDAR forest structure metrics explained between 15 and 20% of the variability in richness within deciduous forest songbird communities. This variability was associated with vertical structure alone and shows how LiDAR can provide a source of complementary predictive data that can be incorporated in models of wildlife habitat associations across broad geographical extents.
File: Lesaketal.pdf
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After the collapse of the Soviet Union, the forestry sector in Russia underwent substantial changes: the state forestry sector was decentralized, the timber industry was privatized, and timber use rights were allocated through short- and long-term leases. To date, there has been no quantitative assessment of the drivers of timber harvesting in European Russia following these changes. In this paper we estimate an econometric model of timber harvesting using remote sensing estimations of forest disturbance from 1990-2000 to 2000-2005 as our dependent variable. We aggregate forest disturbance to administrative districts - equivalent to counties in the United States - and test the impact of several biophysical and economic factors on timber harvesting. Additionally, we examine the impact that regions - equivalent to states in the United States and the main level of decentralized governance in Russia - have on timber harvesting by estimating the influence of regional-level effects on forest disturbance in our econometric model. Russian regions diverged considerably in political and economic conditions after the collapse of the Soviet Union, and the question is if these variations impacted timber harvesting after controlling for district-level biophysical and economic drivers. We find that the most important drivers of timber harvesting at the district level are road density, the percent of evergreen forest, and the total area of forest. The influence of these variables on timber harvesting changed over time and there was more harvesting closer to urban areas in 2000-2005. Even though district-level variables explain more than 70 percent of the variation in forest disturbance in our econometric model, we find that regional-level effects remain statistically significant. While we cannot identify the exact mechanism through which regional-level effects impact timber harvesting, our results suggest that sub-national differences can have a large and statistically significant impact on land-use outcomes and should be considered in policy design and evaluation.
File: Wendland_etal2011_driversoftimberharvesting_GEC.pdf
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