Spatial pattern analysis for quantification of landscape structure of Tadoba-Andhari Tiger Reserve,Central India

Landscape structure is often regarded as an important factor that governs the distribution and abundance of species.Therefore it is critical to understand the landscapes and their dynamics.Patterns of landscape elements strongly influence the ecological characteristics.This study was designed to document and map the current status of the tropical dry deciduous forest of the Tadoba-Andhari Tiger Reserve(TATR),Central India,(using IRS P6 LISS IV data)and to describe its landscape structure at three levels of organization viz.landscape,class,and patch.The study area was classified into 10 land cover classes that include 6vegetation classes.The landscape structure was analyzed using FRAGSTATS using 12 set of indices.The TATR landscapes have a total of2,307 patches with a mean patch size of 25.67 ha and patch density of 1.7patches per km2.Amongst all land cover classes,mixed bamboo forest is dominant—it occupied maximum area(77.99%)—while riparian forest is least represented(0.32%).Mixed forest has maximum number of patches among all vegetation classes.Results have shown that despite being dominant in the area,mixed bamboo forest has low patch density(0.25/100 ha).Dominance of mixed bamboo forest is attributed to large patch sizes and not to the number of patches.This study has focussed on the approach of integrating satellite forest classification and forest inventory data for studying forest landscape patterns.     

EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements

The overall objective of this study was to combine national forest inventory data and remotely sensed data to produce pan-European maps on growing stock and above-ground woody biomass for the two species groups “broadleaves” and “conifers”. An automatic up-scaling approach making use of satellite remote sensing data and field measurement data was applied for EU-wide mapping of growing stock and above-ground biomass in forests. The approach is based on sampling and allows the direct combination of data with different measurement units such as forest inventory plot data and satellite remote sensing data. For the classification, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used. Comprehensive field measurement data from national forest inventories for 98,979 locations from 16 countries were used for which tree species and growing stock estimates were available. The classification results were evaluated by comparison with regional estimates derived independently from the classification from national forest inventories. The validation at the regional level shows a high correlation between the classification results and the field based estimates with correlation coefficient r = 0.96 for coniferous, r = 0.94 for broadleaved and r = 0.97 for total growing stock per hectare. The mean absolute error of the estimations is 25 m³/ha for coniferous, 20 m³/ha for broadleaved and 25 m³/ha for total growing stock per hectare. Biomass conversion and expansion factors were applied to convert the growing stock classification results to carbon stock in above-ground biomass. As results of the classification, coniferous and broadleaved growing stock as well as carbon stock of the above-ground biomass is mapped on a wall-to-wall basis with a spatial resolution of 500 m × 500 m per grid cell. The mapped area is 5 million km², of which 2 million km² are forests, and covers the whole European Union, the EFTA countries, the Balkans, Belarus, the Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey.     

An assessment of Hawaiian dry forest condition with fine resolution remote sensing

Remote sensing offers the potential to spatially map forest cover quickly and reliably for inventory purposes. We developed a new image analysis approach using an integrated methodology of “object-based” image classification techniques and field-based measurements to quantify forest cover in a degraded dry forest ecosystem on the leeward side of the Island of Hawaii. This new approach explicitly recognized the transitional areas between tree crowns and tree shades (tree shadows) as a unique class and fully utilized them for the quantification of canopy cover. Object-oriented classification of Ikonos-2 satellite images allowed delineation of tree shades and crowns and the transitional areas between them from objects with similar reflectance and size that were surrounding the trees. These included patches of fountain (Pennisetum setaceum) and kikuyu (Pennisetum clandestinum) grass, lava outcrops and lava–grass mixtures. Crown-shade transitions were clearly differentiated in spite of their wide range of spectral values and reflectance similarities with areas of lava–grass mixture. Segments representing tree shades and dark lava outcrops were also classified into their respective classes even if they were contiguous. The image estimates of canopy cover using the tree shade plus transition classes were linearly related with field estimates of canopy cover (R² = 0.86 and slope = 0.976). Based on this relationship, dry forest cover throughout the 2627-ha area was estimated at 7.7 ± 1.9%. An immediate application of this new approach is to select and delineate areas with higher canopy cover in order to concentrate ecological restoration and conservation efforts.     

Estimating forest carbon fluxes for large regions based on process-based modelling, NFI data and Landsat satellite images

The aim of this study was to develop and evaluate a new approach for estimating forest carbon fluxes for large regions based on climate-sensitive process-based model, national forest inventory (NFI) data and satellite images. The approach was tested for Central Finland and Lapland with NFI field data and daily weather data from 2004 to 2008.The approach combines (1) a light use efficiency (LUE) model, (2) a process-based summary model for estimating gross primary production (GPP) and net primary production (NPP), and (3) the Yasso07 soil carbon model, which together allow the estimation of net ecosystem exchange (NEE). Landsat TM 5 satellite images were utilized to generalize the carbon fluxes obtained for field sample plots for all forested areas using the k-NN imputation method. The accuracy of the imputations was examined by leave-one-out cross validation and by comparing the imputed and simulated values with Eddy covariance (EC) measurements.RMSE of the k-NN imputations was slightly better in Central Finland than in Lapland, the bias staying at a similar level. Based on the EC comparisons, the approach seemed to work rather well with GPP estimates in both areas, but in the north the NEE estimates were remarkably biased. The main advantages of the approach include its applicability to basic NFI data and a high output resolution (30 m).The method proved to be a promising way to produce carbon flux estimates based on large-scale forest inventory data and could therefore be easily applied to the whole of Northern Europe. However, there are still drawbacks to the approach, such as lacking parameters for peat lands. One of the future goals is to integrate the approach with an interactive mapping framework, which could thereafter be utilized, for example, in climate change research.     

Compilation of a European forest map from Portugal to the Ural mountains based on earth observation data and forest statistics

This article focuses on the approach of combining the information from both remote sensing and forest inventory statistics in order to produce a European forest proportion map covering the area from Portugal to the Ural mountains. For this purpose, a calibration method was developed, tested and applied to the pan-European area. The resulting forest map was analysed on a pixel-by-pixel basis and given to inventory and remote sensing experts for consultation. When comparing both the result of the calibrated forest map with that of the original AVHRR mosaic of the area it was found that the satellite-derived estimates of forest area closely matched the ground inventory statistics indicating the high accuracy obtained from the AVHRR mosaic alone. Most visible discrepancies were found in northern Europe where the inventory data showed less forest than the image data. In southern Europe, the inventory data displayed more forest than the AVHRR image. This project was carried out for the European Commission, Joint Research Centre in 1999/2000 (contract no. 17223-2000-12 F1SC ISP FI) mainly by the European Forest Institute and VTT Information Technology.     

An economic model of international wood supply,forest stock and forest area change

Abstract

Wood supply, the link between roundwood removals and forest resources, is an important component of forest sector models. This paper develops a model of international wood supply within the structure of the spatial equilibrium Global Forest Products Model. The wood supply model determines, for each country, the annual forest harvest, the annual change of forest stock and the annual change of forest area. The results suggest that global forest area would decline by 477 million ha between 1999 and 2030, with the largest decline in Asia and Africa. However, global forest stock would increase by 25 billion?m³, with the largest increase in Europe, and North and Central America. Higher global harvests and lower prices were predicted than those predicted in the past with exogenous timber supply assumptions.     

Forest stand age classification using time series of photogrammetrically derived digital surface models

In this research, we developed and tested a remote sensing-based approach for stand age estimation. The approach is based on changes in the forest canopy height measured from a time series of photo-based digital surface models that were normalized to canopy height models using an airborne laser scanning derived digital terrain model (DTM). Representing the Karelian countryside, Finland, CHMs from 1944, 1959, 1965, 1977, 1983, 1991, 2003, and 2012 were generated and allow for characterization of forest structure over a 68-year period. To validate our method, we measured stand age from 90 plots (1256?m²) in 2014, whereby producer's accuracy ranged from 25.0% to 100.0% and user's accuracy from 16.7% to 100.0%. The wide range of accuracy found is largely attributable to the quality and characteristics of archival images and intrastand variation in stand age. The lowest classification accuracies were obtained for the images representing the earliest dates. For forest managers and agencies that have access to long-term photo archives and a detailed DTM, the estimation of stand age can be performed, improving the quality and completeness of forest inventory databases.     

Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia

Abstract

This research reports the major evaluation results from an operational stand-based forest inventory using airborne laser scanner data carried out in Norway. This is the first operational inventory in which data from two separate districts are combined. Laser data from two forest areas of 65 and 110 km² were used to predict six biophysical stand variables used in forest planning. The predictions were based on regression equations estimated from 250 m² field training plots distributed systematically throughout the two forest areas. Test plots with a size of 0.1 ha were used for validation. The testing revealed standard deviations between ground-truth values and predicted values of 0.58–0.85 m (3.4–5.6%) for mean and dominant heights, 2.62–2.87 m² ha^?1 (9.3–14.3%) for basal area, and 18.7–25.1 m³ ha^?1 (10.8–12.8%) for stand volume. No serious bias was detected. For 10 of the 12 estimated regression models there were no significant effects of district.     

Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project

This research reports the major results from an evaluation of the first Nordic operational stand-based forest inventory using airborne laser scanner data. Laser data from a forest area of 250 km² were used to predict six biophysical stand variables used in forest planning. The predictions were based on regression equations estimated from 250 m² field training plots distributed systematically throughout the forest area. Test plots with an approximate size of 0.1–0.4 ha were used for validation. The testing revealed standard deviations between ground-truth values and predicted values of 0.36–1.37 m (1.9–7.6%) for mean height, 0.70–1.55 m (3.0–7.6%) for dominant height, 2.38–4.88 m² ha^?1 (7.8–14.2%) for basal area and 13.9–45.9 m³ ha^?1 (6.5–13.4%) for stand volume. No serious bias was detected.     

INFOCARB: A regional scale forest carbon inventory (Provincia Autonoma di Trento,Southern Italian Alps)

The aim of this inventory (acronym: INFOCARB) was to measure the organic carbon stored in the forest ecosystems of the Trento region (Provincia Autonoma di Trento, Northern Italy) in both above- and belowground pools, according to the Kyoto protocol and IPCC requirements. A total of 150 forest sampling points were selected on the entire regional area (6206 km²) with a statistical sampling approach, based on the timber volume as a proxy variable for a stratified sampling. Each sampling point was located with a GPS receiver and a 600 m² circular plot was delimited around each point. Inside the plots, the biomass of trees, shrubs and herbaceous vegetation was measured, while litter was collected in systematically placed subplots. Topsoil (down to 30 cm depth) was sampled with the excavation method on three systematically located pits, to determine the organic carbon content, the bulk density and the volume occupied by stones and roots.     

Environmental key factors and their thresholds for the avifauna of temperate montane forests

Mixed montane forests cover large tracts of the low mountain ranges that dominate Central Europe and also contain much of the area that is important to forest related nature conservation. However, beyond general patterns little is known about ecologically effective driving factors in this habitat. This results in a lack of precise values that can be used to formulate guidelines for nature conservation oriented management strategies. To improve this situation, we used birds as indicators for forest habitat qualities.     

Spatial prediction of forest stand variables

This study aims at the development of a model to predict forest stand variables in management units (stands) from sample plot inventory data. For this purpose we apply a non-parametric most similar neighbour (MSN) approach. The study area is the municipal forest of Waldkirch, 13 km north-east of Freiburg, Germany, which comprises 328 forest stands and 834 sample plots. Low-resolution laser scanning data, classification variables as well rough estimations from the forest management planning serve as auxiliary variables. In order to avoid common problems of k-NN-approaches caused by asymmetry at the boundaries of the regression spaces and distorted distributions, forest stands are tessellated into subunits with an area approximately equivalent to an inventory sample plot. For each subunit only the one nearest neighbour is consulted. Predictions for target variables in stands are obtained by averaging the predictions for all subunits. After formulating a random parameter model with variance components, we calibrate the prior predictions by means of sample plot data within the forest stands via BLUPs (best linear unbiased predictors). Based on bootstrap simulations, prediction errors for most management units finally prove to be smaller than the design-based sampling error of the mean. The calibration approach shows superiority compared with pure non-parametric MSN predictions.     

Retrieval of forest attributes in complex successional forests of Central Indonesia: Modeling and estimation of bitemporal data

Quantification of forest parameters in different successional stages is required because of its importance as a source of global emissions and ecosystem changes. This study focuses on a successional tropical forest under logging practices in East Kalimantan province, Indonesia. We modeled the forest attributes using both a parametric multiple linear regression analysis and neural networks approach, with Landsat ETM data acquired in 2000 (ETM00). We compiled sample plot data using forest inventory data collected from 1997 to 1998. A total of 226 plots were used to train the models and 112 plots were used for the validation. The remote sensing data (spectral values, vegetation indices, texture, etc.) coupled with digital elevation model (DEM) were experimented with and selectively used to model basal area, stem volume and above ground biomass (AGB). We investigated the possibility to estimate the forest attributes from bitemporal ETM data by calibrating radiometric properties of the ETM image from 2003 (ETM03) using the multivariate alteration detection method. The Pearson correlations showed that the mean texture index is strongly correlated with the forest attributes. We show that neural networks resulted in a higher coefficient of determination (r²) and lower RMSE than multiple regressions for predicting the forest attributes. The estimated forest properties increased with the forest succession advancement (i.e. from the open forest to advanced secondary forest classes). The modeled basal area, stem volume and AGB varied from 10.7–15.1 m² ha⁻¹, 123.2–181.9 m³ ha⁻¹, and 132.7–185.3 Mg ha⁻¹, respectively. The RMSE_r values of model fitting ranged from 11.2% to 13.3%, and the test dataset estimated slightly higher RMSE_r which varied from 12% to 14.1%. The ETM03 forest attributes revealed favorable estimates, showing considerably higher estimates than the ETM00. The estimation of forest properties using neural networks makes Landsat data a valuable source of information for forest management, mainly with the recent free access to its historical dataset.     

Forest Type,Diversity and Biomass Estimation in Tropical Forests of Western Ghat of Maharashtra Using Geospatial Techniques

Tropical forests are the world’s largest terrestrial storehouses of carbon and are recognized as rich, diverse and highly productive ecosystems. The present study was conducted to characterize the land use, diversity and biomass of tropical forest in Western Ghat of Maharashtra State in India through satellite remote sensing and GIS. The study has been designed and implemented to promote analysis on Western Ghat biodiversity resources including trees, shrubs and herbs based on inventorying, monitoring and mapping. Field measured biomass is integrated with spectral responses of various bands and indices of the Landsat TM satellite image for estimation of above-ground biomass in a 36,046 km² area of relic forest in the Central Western Ghat. The above-ground biomass from field-based inventory varied from 30.2 to 151.1 ton/ha in moist deciduous forest, 9.2–99.1 ton/ha in dry deciduous forest, 42.1–158.6 ton/ha in semi-evergreen forest, and 160.9–271 ton/ha in evergreen forest. The total above-ground biomass of the study area was estimated to be 95.2 M tons. A regression equation between field above-ground biomass and a Normalized Difference Vegetation Index was used for spectral modeling to estimate and prepare the above-ground biomass map in the region. A total 120 plant species in 81 genera and 31 families were identified in the study area. This study emphasizes the importance of relic forests for their biodiversity, carbon sequestration and total biomass.     

How much carbon can the Siberian boreal taiga store: a case study of partitioning among the above-ground and soil pools

In the context of global carbon cycle management, accurate knowledge of carbon content in forests is a relevant issue in contemporary forest ecology. We measured the above-ground and soil carbon pools in the darkconiferous boreal taiga. We compared measured carbon pools to those calculated from the forest inventory records containing volume stock and species composition data. The inventory data heavily underestimated the pools in the study area(Stolby State Nature Reserve, central Krasnoyarsk Territory, Russian Federation). The carbon pool estimated from the forest inventory data varied from 25(t ha-1)(low-density stands) to 73(t ha-1)(highly stocked stands). Our estimates ranged from 59(t ha-1)(lowdensity stands) to 147(t ha-1)(highly stocked stands). Our values included living trees, standing deadwood, living cover, brushwood and litter. We found that the proportion of biomass carbon(living trees): soil carbon varied from99:1 to 8:2 for fully stocked and low-density forest stands,respectively. This contradicts the common understanding that the biomass in the boreal forests represents only16–20 % of the total carbon pool, with the balance being the soil carbon pool.     

Recent spruce decline with biotic pathogen infestation as a result of interacting climate,deposition and soil variables

Decline or health deterioration of Norway spruce (Picea abies (L.) Karst.) dominated forest stands has recently been observed mainly in sub-mountainous parts of Central Europe. Forest inventory of 208 randomly distributed circular plots including field observations of spruce tree health and rot symptoms by honey fungus (Armillaria ostoyae) was used for assessing intensity of spruce forest health decline in a managed forest area of 12.7 th. ha located in Beskids Mts., NE Czech Republic. First, principal component analysis was used to separate inventory variables related to environmental stress (reduced apical increment, dry tree top and stem resin exudation due to A. ostoyae infestation) into PC1, and health deterioration symptoms associated with mechanical damage (peeling, crown breaks) into PC2. The first two principal components explained 59% of the total variability in health decline symptoms. Spatial variability of both principal components was explained using spatial lag regression model identified from a set of environmental variables including sulfur and nitrogen deposition, elevation, solar radiation, age of the forest stands and geological properties (geochemical reactivity index). Environmental stress (PC1) was associated with low elevations (sub-optimal for spruce), high level of nitrogen and sulfur deposition (their interaction), low geochemical reactivity and also stand age. On the other hand, mechanical damage (PC2) significantly increased with elevation and stand age. As the forest decline in Beskids Mts. is related to A. ostoyae spreading from local infestation hot spots, both principal components had a significant spatial autocorrelation, partly distorting the signal of environmental conditions. The results indicate that the disturbed forest soils by long-term acid deposition and subsequent nutrient degradation and more pronounced drought stress at low elevations are the most important drivers of the recent spruce health decline in Beskids Mts.     

Untersuchungen zur forstlichen Bedeutung der holzzerstörenden Robameisen unter Verwendung der Tracer-Methode

Summary Carpenter ants (GenusCamponotus) are well known as wood destroying insects in north- and southeuropean forests. As was demonstrated in a north-bavarian forest (340 mtrs. about sealevel) carpenter ants can cause important damage in Central Europe too. Whereas single stems with carpenter ants are spread over whole the forest area we found a high percentage of infestated stems in sunny and warm margin zones. The ants prefer in this region Norway Spruce, but settle also in Pines and occasionally in oaks, beeches and birchs. The forest damage was caused byC. herculeanus L. which is living more arboricol thanC. ligniperda Latr.Basing on the high tendency to regurgitations in the feeding behaviour of ants, the tracermethod (J¹³¹ in honey-water-solution) was used to determine exactly the nest area ofCamponotus-colonies. These experiments demonstrated the nesting-area of a single colony covering several stems and beeing much more wider than can be supposed by external visible damage.     

Characterizing forest species composition using multiple remote sensing data sources and inventory approaches

Abstract

The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.     

Calibrating and testing a gap model for simulating forest management in the Oregon Coast Range

The complex mix of economic and ecological objectives facing today's forest managers necessitates the development of growth models with a capacity for simulating a wide range of forest conditions while producing outputs useful for economic analyses. We calibrated the gap model ZELIG to simulate stand-level forest development in the Oregon Coast Range as part of a landscape-scale assessment of different forest management strategies. Our goal was to incorporate the predictive ability of an empirical model with the flexibility of a forest succession model. We emphasized the development of commercial-aged stands of Douglas-fir, the dominant tree species in the study area and primary source of timber. In addition, we judged that the ecological approach of ZELIG would be robust to the variety of other forest conditions and practices encountered in the Coast Range, including mixed-species stands, small-scale gap formation, innovative silvicultural methods, and reserve areas where forests grow unmanaged for long periods of time. We parameterized the model to distinguish forest development among two ecoregions, three forest types and two site productivity classes using three data sources: chronosequences of forest inventory data, long-term research data, and simulations from an empirical growth-and-yield model. The calibrated model was tested with independent, long-term measurements from 11 Douglas-fir plots (6 unthinned, 5 thinned), 3 spruce-hemlock plots, and 1 red alder plot. ZELIG closely approximated developmental trajectories of basal area and large trees in the Douglas-fir plots. Differences between simulated and observed conifer basal area for these plots ranged from −2.6 to 2.4 m²/ha; differences in the number of trees/ha ≥50 cm dbh ranged from −8.8 to 7.3 tph. Achieving these results required the use of a diameter-growth multiplier, suggesting some underlying constraints on tree growth such as the temperature response function. ZELIG also tended to overestimate regeneration of shade-tolerant trees and underestimate total tree density (i.e., higher rates of tree mortality). However, comparisons with the chronosequences of forest inventory data indicated that the simulated data are within the range of variability observed in the Coast Range. Further exploration and improvement of ZELIG is warranted in three key areas: (1) modeling rapid rates of conifer tree growth without the need for a diameter-growth multiplier; (2) understanding and remedying rates of tree mortality that were higher than those observed in the independent data; and (3) improving the tree regeneration module to account for competition with understory vegetation.