Remote Sensing

Agricultural areas are declining in many areas of the world, often because socio-economic and political changes make agriculture less profitable. The transition from centralized to market-oriented economies in Eastern Europe and the former Soviet Union after 1989 represented major economic and political changes, yet the resulting rates and spatial pattern of post-socialist farmland abandonment remain largely unknown. Remote sensing offers unique opportunities to map farmland abandonment, but automated assessments are challenging because phenology and crop types often vary substantially. We developed a change detection method based on support vector machines (SVM) to map farmland abandonment in the border triangle of Poland, Slovakia, and Ukraine in the Carpathians from Landsat TM/ETM+ images from 1986, 1988, and 2000. Our SVM-based approach yielded an accurate change map (overall accuracy = 90.9%; kappa = 0.82), underpinning the potential of SVM to map complex land-use change processes such as farmland abandonment. Farmland abandonment was widespread in the study area (16.1% of the farmland used in socialist times), likely due to decreasing profitability of agriculture after 1989. We also found substantial differences in abandonment among the countries (13.9% in Poland, 20.7% in Slovakia, and 13.3% in Ukraine), and between previously collectivized farmland and farmland that remained private during socialism in Poland. These differences are likely due to differences in socialist land ownership patterns, post-socialist land reform strategies, and rural population density.

Forests provide important ecosystem services, and protected areas around the world are intended to reduce human disturbance on forests. The question is how forest cover is changing in different parts of the world, why some areas are more frequently disturbed, and if protected areas are effective in limiting anthropogenic forest disturbance. The Carpathians are Eastern Europe's largest contiguous forest ecosystem and are a hotspot of biodiversity. Eastern Europe has undergone dramatic changes in political and socioeconomic structures since 1990, when socialistic state economies transitioned toward market economies. However, the effects of the political and economic transition on Carpathian forests remain largely unknown. Our goals were to compare post-socialist forest disturbance and to assess the effectiveness of protected areas in the border triangle of Poland, Slovakia, and Ukraine, to better understand the role of broadscale political and socioeconomic factors. Forest disturbances were assessed using the forest disturbance index derived from Landsat MSS/TM/ETM images from 1978 to 2000. Our results showed increased harvesting in all three countries (up to 1.8 times) in 1988-1994, right after the system change. Forest disturbance rates differed markedly among countries (disturbance rates in Ukraine were 4.5 times higher than in Poland, and those in Slovakia were 4.3 times higher than in Poland), and in Ukraine, harvests tended to occur at higher elevations. Forest fragmentation increased in all three countries but experienced a stronger increase in Slovakia and Ukraine (;5% decrease in core forest) than in Poland. Protected areas were most effective in Poland and in Slovakia, where harvesting rates dropped markedly (by nearly an order of magnitude in Slovakia) after protected areas were designated. In Ukraine, harvesting rates inside and outside protected areas did not differ appreciably, and harvests were widespread immediately before the designation of protected areas. In summary, the socioeconomic changes in Eastern Europe that occurred since 1990 had strong effects on forest disturbance. Differences in disturbance rates among countries appear to be most closely related to broadscale socioeconomic conditions, forest management practices, forest policies, and the strength of institutions. We suggest that such factors may be equally important in other regions of the world.

LiDAR data are increasingly available from both airborne and spaceborne missions to map elevation and vegetation structure. Additionally, global coverage may soon become available with NASA's planned DESDynI sensor. However, substantial challenges remain to using the growing body of LiDAR data. First, the large volumes of data generated by LiDAR sensors require efficient processing methods. Second, efficient sampling methods are needed to collect the field data used to relate LiDAR data with vegetation structure. In this paper, we used low-density LiDAR data, summarized within pixels of a regular grid, to estimate forest structure and biomass across a 53,600 ha study area in northeastern Wisconsin. Additionally, we compared the predictive ability of models constructed from a random sample to a sample stratified using mean and standard deviation of LiDAR heights. Our models explained between 65 to 88% of the variability in DBH, basal area, tree height, and biomass. Prediction errors from models constructed using a random sample were up to 68% larger than those from the models built with a stratified sample. The stratified sample included a greater range of variability than the random sample. Thus, applying the random sample model to the entire population violated a tenet of regression analysis; namely, that models should not be used to extrapolate beyond the range of data from which they were constructed. Our results highlight that LiDAR data integrated with field data sampling designs can provide broad-scale assessments of vegetation structure and biomass, i.e., information crucial for carbon and biodiversity science.

The spectral reflectance of ground objects in mountainous areas is largely contaminated by second-order effects which are due to topographic slope and aspect. Such topographic effects present severe problems for the consistent analysis of optical remote sensing images, in particular for satellite-based forest cover mapping. We have integrated a topographic correction module into a modified 5S atmospheric correction model, where targets are assumed to have Lambertian reflectance characteristics. The method was successfully applied to four Landsat Thematic Mapper images with large seasonal differences in solar elevation. Classification methods of increasing complexity (euclidean minimum distance, maximum likelihood, and a backpropagation neural network) have then been used to produce forest stand maps from images which were either corrected for atmospheric effects only or for radiometric distortions due to both atmosphere and topography. It is demonstrated that the topographic corrections provide important improvements when direct and diffuse radiation components are properly accounted for. Differences between the actual bidirectional reflectance properties of forest stands and their approximation by Lambertian reflectance characteristics seem to be less important.

New methods are needed to derive detailed spatial environmental data for large areas, with the increasing interest in landscape ecology and ecosystem management at large scales. We describe a method that integrates several data sources for assessing forest composition across large, heterogeneous landscapes. Multitemporal Landsat Thematic Mapper (TM) satellite data can yield forest classi?cations with spatially detailed information down to the dominant canopy species level in temperate deciduous and mixed forests. We strati?ed a large region (10^6 ha) by ecoregions (10^3-10^4 ha). Within each ecoregion, plot-level, ?eld inventory data were aggregated to provide information on secondary and sub-canopy tree species occurrence, and tree age class distributions. We derived a probabilistic algorithm to assign information from a point coverage (forest inventory sampling points) and a polygon coverage (ecoregion boundaries) to a raster map (satellite land cover classi?cation). The method was applied to a region in northern Wisconsin, USA. The satellite map captures the occurrence and the patch structure of canopy dominants. The inventory data provide important secondary information on age class and associated species not available with current canopy remote sensing. In this way we derived new maps of tree species distribution and stand age re?ecting differences at the ecoregion scale. These maps can be used in assessing forest patterns across regional landscapes, and as input data in models to examine forest landscape change over time. As an example, we discuss the distribution of eastern white pine (Pinus strobus) as an associated species and its potential for restoration in our study region. Our method partially ?lls a current information gap at the landscape scale. However, its applicability is also limited to this scale.

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.

Satellite imagery is the major data source for regional to global land cover maps. However, land cover mapping of large areas with medium-resolution imagery is costly and often constrained by the lack of good training and validation data. Our goal was to overcome these limitations, and to test chain classifications, i.e., the classification of Landsat images based on the information in the overlapping areas of neighboring scenes. The basic idea was to classify one Landsat scene first where good ground truth data is available, and then to classify the neighboring Landsat scene using the land cover classification of the first scene in the overlap area as training data. We tested chain classification for a forest/non-forest classification in the Carpathian Mountains on one horizontal chain of six Landsat scenes, and two vertical chains of two Landsat scenes each. We collected extensive training data from Quickbird imagery for classifying radiometrically uncorrected data with Support Vector Machines (SVMs). The SVMs classified 8 scenes with overall accuracies between 92.1% and 98.9% (average of 96.3%). Accuracy loss when automatically classifying neighboring scenes with chain classification was 1.9% on average. Even a chain of six images resulted only in an accuracy loss of 5.1% for the last image compared to a reference classification from independent training data for the last image. Chain classification thus performed well, but we note that chain classification can only be applied when land cover classes are well represented in the overlap area of neighboring Landsat scenes. As long as this constraint is met though, chain classification is a powerful approach for large area land cover classifications, especially in areas of varying training data availability.

Forest use can increase substantially during periods of societal change, but it is unclear how harvesting rates differ among different landownership types in such times. Our goal here is to quantify the rates and spatial patterns of forest disturbance in private forests, state forests, and a National Park in the Polish Carpathians before and after the collapse of socialism. We analysed a series of classified Landsat TM images (1988-2000) and a landownership map. Our results showed that disturbance peaked in all ownership types in the immediate transition time. However, disturbance rates in private forests were about five times higher than on public lands. The spatial pattern of disturbances was similar across ownership types, but private forests were more fragmented than state and National Park forests. Our study indicates that institutional strength may determine forest use under different ownership types and highlights the multi-scale, nested control of the drivers of land use change.