Remote Sensing

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.

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.

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.

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.