Sample Plot Level Research Articles

Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests

ABSTRACT Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of Türkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (hmean), top height (htop), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson’s correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the hmean - htop (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, hmean, htop, BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory.

Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods

Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative characteristic of the desert ecosystem and an important index to measure ecosystem productivity and monitor desertification. An accurate and efficient method of predicting the AGB of a shrub community is essential for studying the spatial patterns and ecological functions of the desert region. Even though there are several entries in the literature on the AGB prediction of desert shrub communities using remote sensing data, the applicability and accuracy of airborne LiDAR data and prediction methods have not been well studied. We first extracted the elevation, density and intensity variables based on the airborne LiDAR, and then sample plot-level AGB prediction models were constructed using the parametric regression (nonlinear regression) and nonparametric methods (Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting Machine, and Multivariate adaptive regression splines). We evaluated accuracies of all the AGB prediction models we developed based on the fit statistics. Results showed that: (1) the elevation, density and intensity variables obtained from LiDAR point cloud data effectively predicted the AGB of the desert shrub community at a sample plot level, (2) the kappa coefficient of nonlinear mixed-effects (NLME) model obtained was 0.6977 with an improvement by 13% due to the random effects included into the model, and (3) the nonparametric model, such as Support Vector Machine showed the best fit statistics (R2 = 0.8992), which is 28% higher than the NLME-model, and effectively reduced the heteroscedasticity. The AGB prediction model presented in this paper, which is based on the airborne LiDAR data and machine learning algorithm, will provide a valuable tool to the managers and researchers for evaluating desert ecosystem productivity and monitoring desertification.

Optimizing height measurement for the long-term forest experiments in Sweden

Information on tree height is useful for volume estimation and site productivity assessment and as such, remains one of the most important variables often measured in forest inventories. Measuring a sufficient number of sample trees requires considerable sampling effort and cost. In this study, we developed height functions for optimizing tree height measurement in the Swedish long-term forest experiments (LTFEs). Two large datasets from the LTFE databases: fitting data (from thinning, fertilisation and mixed species experiments) and validation data (tree species and spacing experiments) collected over several decades were used. The fitting and validation data comprise 133,788 and 68,440 observations, respectively, each covering a large range of growth and environmental conditions across Sweden. A multilevel nonlinear mixed-effects modelling approach was used to build the generalised height functions for Scots pine, Norway spruce, birch (Silver and Downy birch united), other conifers and other broadleaves, considering variations in heights and other stand characteristics at sample plot-level and revision-level. The response calibration of the functions was first carried out with all measured heights of the validation data, and second, using heights of one to six sample trees obtained from different tree selection strategies (diameter extremes, largest diameters, and smallest diameters). The mixed-effects height functions explained most of the height variations in the fitting dataset (pseudo R2: 0.938 – 0.970; RMSE: 0.957 – 1.363 m) without any residual trends. The validation showed that the functions accounted for 95 – 98 % of the height variation in the validation dataset, with RMSE ranging between 0.770 and 1.040 m, confirming the functions’ high accuracy. We recommend the measurement of four sample tree heights based on diameter extremes as the ideal threshold for response calibration. These functions and the suggested sampling technique would reduce sampling effort and inventory cost of height measurements for subsequent inventories of the LTFEs.

Forest Structure Simulation of Eucalyptus Plantation Using Remote-Sensing-Based Forest Age Data and 3-PG Model

Eucalyptus plantations play an important role in the timber supply and global warming mitigation around the world. Forest age is a critical factor for evaluating and modeling forest structure (e.g., diameter at breast height (DBH), height (H), aboveground carbon stocks (ACS)) and their dynamics. Recently, the spatial distribution of forest age at different scales based on time series remote sensing data has been widely investigated. However, it is unclear whether such data can effectively support the simulation and assessment of forest structure, especially in fast-growing plantation forests. In this study, the physiological principles in predicting growth (3-PG) model was firstly optimized and calibrated using survey and UAV lidar data at the sample plot (SP) scale, and was then applied at the forest sub-compartment (FSC) scale by designing different simulation scenarios driven by different forest age data sources and adjustments. The sensitivity of the simulated forest structure parameters to forest age was assessed at the SP and FSC levels. The results show that both the survey forest age data and the remote-sensing-derived forest age data could accurately estimate the DBH, H, and ACS of eucalyptus plantations with the coefficients of determination (R2) ranging from 0.87 to 0.94, and the relative root mean square error (RRMSE) below 20% at SP level. At the FSC level, the simulation results based on remotely sensed forest age data are significantly better than FSC forest age data from surveys by forestry bureaus, with R2 of ACS 0.7, RMSE 9.12 Mg/ha, and RRMSE 28.24%. The results of the sensitivity analysis show that the DBH, H, and ACS show different degrees of variation under different adjusted forest ages at SP and FSC level. The maximum difference in ACS is 82.91% at the SP scale if the forest age decreases 12 months and 41.23% at the FSC scale if the forest age increases 12 months. This study provides an important reference for future studies using forest age data obtained by remote sensing to drive the forest carbon model in a large spatial scale.

Stem volume by height classes of immature, mature and overmature stands of the main forest-forming species of Ukraine

Generally, it is impossible to measure diameters and heights of all trees in a forest stand. Therefore, models of relationships between heights (h) and diameters (d) of trees are commonly used in practice for stem volume estimation. This study aimed at developing models of tree height-diameter (h-d) relationships as well as corresponding models of the tree stem volume for immature, mature and overmature stands of the main forest-forming species of Ukraine. This paper is a aggregation of long-term studies of the stem volume, which are based on the results of measuring about 10 thousand sample trees. Modelling of the tree height-diameter relationships was performed using relative height values. The methodology used in this study allowed generalising the measurements of sample trees collected in stands of various forest site types, productivity levels, and age categories. The average height of trees with a diameter of 24 cm was taken as the reference during modelling relative heights, while the diameter of 40 cm was chosen as the reference for overmature Scots pine stands. As a result, the parameters of a unified mathematical model of relative heights for immature, mature, and overmature stands of the main forest-forming tree species of Ukraine were established. Based on these models, height-diameter relationships in forest stands of different height classes were predicted. The authors demonstrated that the developed mathematical models substantially simplify the methodology of field work during timber surveys. The paper also presents models of the tree stem volume. These models predict the stem volume outside the bark based on diameters and heights of trees or using the developed models of h-d relationships. In this study, a unified system of mathematical models of stem volume by height classes were created for immature, mature, and overmature stands of the main forest-forming species of Ukraine. The results of the study are introduced to the National Forest Inventory of Ukraine for growing stock volume calculation at sample plot level using measurements of individual trees. The developed models can be used both by operation forestry (estimation of the timber volume during harvesting), and forest management (forecasting the future structure of forests and estimating the growing stock volume), as well as in the forest ecology

Above and belowground woody-biomass and carbon stock estimations at Kunzila watershed, Northwest Ethiopia

Woody-biomass is an important indicator for good vegetation cover, and a healthy environment that provides ecosystems services such as supplying, provisioning, regulating, esthetic, and cultural values. Moreover, woody-biomass stores a very good amount of carbon dioxide as about fifty percent of living organisms are built from carbon. The objective of this study is to estimate the above and belowground woody biomass and carbon stock of Kunzila watershed, Northwest Ethiopia as a piece of baseline information so as to gage the changes after the intervention. The watershed covers about 11,200 ha of land with undulating topographic landscapes. More than 230 sample plots of 30 m by 30 m were surveyed and every woody plant (shrub/bush and tree) was measured. Allometric equations were applied to calculate biomass and carbon stock potential at the plot level. The Random forest algorithm was applied to upscale plot values to watershed level. The results indicate that Kunzila watershed offers maximum biomass of 1,756(±249) Mt year−1/watershed and corresponding carbon dioxide equivalent (CO2e) of 3,022(±429) Mt C year−1 /watershed with the current vegetation status; while the average woody biomass and CO2e were estimated about 244(±249) Mt ha−1 year−1 and 425(±429) Mt C ha−1 year−1, respectively. These results indicate that there are high variabilities among different ecosystem types and also the watershed generally offers below its potential. However, there is a high potential to achieve up to triples of biomass and CO2e by implementing recommended intervention plans developed out of the current study. The current approach using machine learning algorithm to upscale from sample plot-level to watershed scale is a very good one and can be applied to other watersheds at scale.

Nonlinear mixed-effects height to crown base model based on both airborne LiDAR and field datasets for Picea crassifolia Kom trees in northwest China

Height to crown base (hcb) is an important tree-level characteristic used for determination of crown size and for prediction of the initiation and propagation potential of crown fires. Crown size variables are often used as input variables for forest growth and yield models. In this study, a nonlinear mixed-effects hcb model was developed using measurements from 510 Picea crassifolia Kom trees in 16 sample plots in the Xishui forest farm of SunanYuguzu Autonomous County, Gansu Qilian Mountain National Nature Reserve, China. The modeling data were based on the airborne light detection and ranging (LiDAR), which serves as a reliable database for large forest areas. In general, field-based measurements are considered relatively more accurate compared with remote sensing data, but the LiDAR database can be applied for large forest area at a relatively low inventory cost when appropriate hcb prediction models are available. Given that measurements from the same sample plots were significantly correlated, we incorporated the random-effect component in the model to account for hcb variations at the sample plot-level. Among several potential predictors evaluated, we observed that diameter at breast height, LiDAR-derived total tree height, LiDAR-derived crown width, and LiDAR-derived plot dominant tree height were highly significant in the nonlinear mixed-effects hcb model. Four strategies for selection of sample trees per sample plot (largest trees, medium-sized trees, smallest trees, and randomly selected trees) and seven sample sizes (two to eight selected trees per sample plot) were evaluated for calibration of our nonlinear mixed-effects hcb model. The empirical best linear unbiased prediction method was applied to estimate the random effects for calibration and sample plot-specific hcb prediction. Calibration results revealed increased accuracy with increased number of selected trees per sample plot. However, selecting many sample trees per sample plot to calibrate the nonlinear mixed-effects hcb model may increase inventory costs with little gain in accuracy. Therefore, we recommended, in our study, that selection of five medium-sized trees per sample plot provides a compromise between measurement cost, model use efficiency, and prediction accuracy.

Forest growing stock volume estimation using optical remote sensing over snow-covered ground: a case study for Sentinel-2 data and the Russian Southern Taiga region

ABSTRACT This paper describes an approach to forest growing stock volume (GSV) estimation based on remotely sensed optical data in red and near-infrared (NIR) bands collected during the period of persistent snow cover. The approach was applied to Sentinel-2 reflectance measurements over forest with snow-covered understory in the north-eastern part of Russian Kostroma region. An in-house dataset with a forest stand-level GSV data was used to approximate GSV-reflectance relationship based on a power function for spruce-dominated, pine-dominated and birch-dominated forests. Highest coefficient of determination (R 2) = 0.84 was obtained for spruce-dominated forest and red band. A cross-validation was performed to estimate the accuracy of a stand-level GSV estimation based on the obtained GSV-reflectance relationship model and Sentinel-2 data. Best results were achieved for pine-dominated forest and NIR band: R 2 = 0.66; root-mean-square error (RMSE) = 58 m3/ha. This GSV estimation approach was validated with an independent dataset of field survey-based GSV measurements at the sample plot level. Validation showed R 2 values comparable to cross-validation results but higher RMSE. Overall Sentinel-2 data tested was found to be informative for GSV estimation; however performance of the described approach varied significantly depending on forest type, spectral band, GSV values range and spatial aggregation level.

Retrieval of Forest Structural Parameters from Terrestrial Laser Scanning: A Romanian Case Study

Research Highlights: The present study case investigates the differences occurring when tree’s biophysical parameters are extracted through single and multiple scans. Scan sessions covered mountainous and hill regions of the Carpathian forests. Background and Objectives: We focused on analyzing stems, as a function of diameter at breast height (DBH) and the total height (H), at sample plot level for natural forests, with the purpose of assessing the potential for transitioning available methodology to field work in Romania. Materials and Methods: We performed single and multiple scans using a FARO Focus 3D X130 phase shift terrestrial laser scanner at 122 kpts and 0.3:0.15 mm noise compression ratio, resulting in an average point density of 6pts at 10m. The point cloud we obtained underpinned the DBH and heights analysis. In order to reach values similar to those measured in the field, we used both the original and the segmented point clouds, postprocessed in subsamples of different radii. Results: Pearson’s correlation coefficient above 0.8 for diameters showed high correlation with the field measurements. Diameter averages displayed differences within tolerances (0.02 m) for 10 out of 12 plots. Height analysis led to poorer results. For both acquisition methods, the values of the correlation coefficient peaked at 0.6. The initial hypothesis that trees positioned at a distance equivalent to their height can be measured more precise, was not valid; no increase in correlation strength was visible for either heights or diameters as the distance from scanner varied (r = 0.52). Conclusions: With regard to tree biophysical parameters extraction, the acquisition method has no major influence upon visible trees. We emphasize the term “visible”, as an increase in the number of acquisitions led to an increased number of detected trees (16% in old stands and 29% in young stands).

Estimation of Tree Biomass at Individual tree, Sample plot and Hybrid Level using Drone Images

Estimation of Tree Biomass at Individual tree, Sample plot and Hybrid Level using Drone Images

Aboveground forest biomass derived using multiple dates of WorldView-2 stereo-imagery: quantifying the improvement in estimation accuracy

ABSTRACTThe aim of this study was to investigate the capabilities of two date satellite-derived image-based point clouds (IPCs) to estimate forest aboveground biomass (AGB). The data sets used include panchromatic WorldView-2 stereo-imagery with 0.46 m spatial resolution representing 2014 and 2016 and a detailed digital elevation model derived from airborne laser scanning data. Altogether, 332 field sample plots with an area of 256 m2 were used for model development and validation. Predictors describing forest height, density, and variation in height were extracted from the IPC 2014 and 2016 and used in k-nearest neighbour imputation models developed with sample plot data for predicting AGB. AGB predictions for 2014 (AGB2014) were projected to 2016 using growth models (AGBProjected_2016) and combined with the AGB estimates derived from the 2016 data (AGB2016). AGB prediction model developed with 2014 data was also applied to 2016 data (AGB2016_pred2014). Based on our results, the change in the 90th percentile of height derived from the WorldView-2 IPC was able to characterize forest height growth between 2014 and 2016 with an average growth of 0.9 m. Features describing canopy cover and variation in height derived from the IPC were not as consistent. The AGB2016 had a bias of −7.5% (−10.6 Mg ha−1) and root mean square error (RMSE) of 26.0% (36.7 Mg ha−1) as the respective values for AGBProjected_2016 were 7.0% (9.9 Mg ha−1) and 21.5% (30.8 Mg ha−1). AGB2016_pred2014 had a bias of −19.6% (−27.7 Mg ha−1) and RMSE of 33.2% (46.9 Mg ha−1). By combining predictions of AGB2016 and AGBProjected_2016 at sample plot level as a weighted average, we were able to decrease the bias notably compared to estimates made on any single date. The lowest bias of −0.25% (−0.4 Mg ha−1) was obtained when equal weights of 0.5 were given to the AGBProjected_2016 and AGB2016 estimates. Respectively, RMSE of 20.9% (29.5 Mg ha−1) was obtained using equal weights. Thus, we conclude that combination of two date WorldView-2 stereo-imagery improved the reliability of AGB estimates on sample plots where forest growth was the only change between the two dates.

Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran).

Hyrcanian forests of North of Iran are of great importance in terms of various economic and environmental aspects. In this study, Spot-6 satellite images and regression models were applied to estimate above-ground biomass in these forests. This research was carried out in six compartments in three climatic (semi-arid to humid) types and two altitude classes. In the first step, ground sampling methods at the compartment level were used to estimate aboveground biomass (Mg/ha). Then, by reviewing the results of other studies, the most appropriate vegetation indices were selected. In this study, three indices of NDVI, RVI, and TVI were calculated. We investigated the relationship between the vegetation indices and aboveground biomass measured at sample-plot level. Based on the results, the relationship between aboveground biomass values and vegetation indices was a linear regression with the highest level of significance for NDVI in all compartments. Since at the compartment level the correlation coefficient between NDVI and aboveground biomass was the highest, NDVI was used for mapping aboveground biomass. According to the results of this study, biomass values were highly different in various climatic and altitudinal classes with the highest biomass value observed in humid climate and high-altitude class.

Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information

In this paper, we demonstrate the feasibility of using the Microsoft Kinect and Google Tango frame-based depth sensors for individual tree stem measurements and reconstruction for the purpose of forest inventory. Conventionally field reference data in forest inventory are collected at tree and sample plot level by means of manual measurements (e.g., a caliper), which are both labor-intensive and time-consuming. In this study, color (i.e., red, green and blue channels, RGB) and range images acquired by a Kinect and Tango systems were processed and used to extract tree diameter measurements for the individual tree stems. For this, 121 reference stem diameter measurements were made with tape and caliper. Kinect-derived tree diameters agreed with tape measurements to a 1.90 cm root-mean-square error (RMSE). The stem curve from the ground to the diameter at breast height agreed with a bias of 0.7 cm and random error of 0.8 cm with respect to the reference trunk. For Tango measurements, the obtained stem diameters matched those from tape measurement with an RMSE of 0.73 cm, having an average bias of 0.3 cm. As highly portable and inexpensive systems, both Kinect and Tango provide an easy way to collect tree stem diameter and stem curve information vital to forest inventory. These inexpensive instruments may in future compete with both terrestrial and mobile laser scanning or conventional fieldwork using calipers or tape. Accuracy is adequate for practical applications in forestry. Measurements made using Kinect and Tango type systems could also be applied in crowdsourcing context.

Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design

Combining sample plot and image data has been widely used to map forest carbon density at local, regional, national and global scales. When mapping is conducted using multiple spatial resolution images at different scales, field observations have to be collected at the corresponding resolutions to match image values in pixel sizes. Given a study area, however, to save time and cost, field observations are often collected from sample plots having a fixed size. This will lead to inconsistency of spatial resolutions between sample plots and image pixels and impede the mapping and product quality assessment. In this study, a methodological framework was proposed to conduct mapping and accuracy assessment of forest carbon density at four spatial resolutions by combining remotely sensed data and reference values of sample plots from a systematical, nested and clustering sampling design. This design led to one field observation dataset at a 30 m spatial resolution sample plot level and three other reference datasets by averaging the observations from three, five and seven sample plots within each of 250 m and 500 m sub-blocks and 1000 m blocks, respectively. The datasets matched the pixel values of a Landsat 8 image and three MODIS products. A sequential Gaussian co-simulation (SGCS) and a sequential Gaussian block co-simulation (SGBCS), an upscaling algorithm, were employed to map forest carbon density at the spatial resolutions. This methodology was tested for mapping forest carbon density in Huang-Feng-Qiao forest farm of You County in Eastern Hunan of China. The results showed that: First, all of the means of predicted forest carbon density values at four spatial resolutions fell in the confidence intervals of the reference data at a significance level of 0.05. Second, the systematical, nested and clustering sampling design provided the potential to obtain spatial information of forest carbon density at multiple spatial resolutions. Third, the relative root mean square error (RMSE) of predicted values at the plot level was much greater than those at the sub-block and block levels. Moreover, the accuracies of the up-scaled estimates were much higher than those from previous studies. In addition, at the same spatial resolution, SGCSWA (scaling up the SGCS and Landsat derived 30 m resolution map using a window average (WA)) resulted in smallest relative RMSEs of up-scaled predictions, followed by combinations of Landsat images and SGBCS. The accuracies from both methods were significantly greater than those from the combinations of MODIS images and SGCS. Overall, this study implied that the combinations of Landsat 8 images and SGCSWA or SGBCS with the systematical, nested and clustering sampling design provided the potential to formulate a methodological framework to map forest carbon density and conduct accuracy assessment at multiple spatial resolutions. However, this methodology needs to be further refined and examined in other forest landscapes.

Effects of thinning-induced changes in structural heterogeneity on growth, ingrowth, and mortality in secondary coastal Douglas-fir forests

Thinning is believed to accelerate the development of late-successional attributes, thereby enhancing stand structural heterogeneity in young, secondary forests. By making use of a large-scale experiment implemented in 40- to 60-year-old coastal Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) forests, we addressed the following objectives: (i) determine the effect of three thinning treatments on the temporal dynamics (first 11 years after thinning) of key forest structure measures, (ii) evaluate the relationships between spatially explicit structural diversity measures and spatially nonexplicit stand metrics, and (iii) test the relationships between stand structure and observed periodic stand volume growth, ingrowth, and mortality. Treatments consisted of high-density, moderate-density, and variable-density thinnings-from-below, as well as a control. Differences in stand structural heterogeneity between treatments were mostly nonsignificant. However, our results suggest that variable-density stands displayed structural enrichment as tree size and tree species diversity increased throughout the study period as a result of continuous ingrowth of species other than Douglas-fir. Simple spatially nonexplicit metrics could not be used to reliably model spatially explicit structural diversity measures. The inclusion of structural and species diversity measures only rarely improved accuracy of sample plot level growth, ingrowth, and mortality prediction models. Despite the short-term nature of this study, we conclude that variable-density thinning shows promise in increasing structural heterogeneity in young even-aged stands. The inclusion of structural diversity measures in growth and mortality models may be beneficial, but further work is needed to clarify the underlying relationships, particularly at the individual-tree level.

Predicting tree crown defoliation using color-infrared orthophoto maps

Orthophoto maps based on color-infrared aerial photography have been used by the Lithuanian forest inventory since 2001. This study aimed to investigate the opportunities for using these orthophoto maps to predict tree crown defoliation at the single tree and sample plot levels. The test area was located in the Aukstaitija National Park, eastern Lithuania, and it was photographed in the summer of 2008 using a Vexcel UltraCam D digital frame aerial camera to produce digital orthophoto maps with a 0.5 x 0.5 m ground sampling density. Some 1721 tree crowns (mainly pine, spruce and birch), located in 166 permanent sample plots, were identified and delineated on the orthophoto maps. Crown defoliation and other dendrometric characteristics were field-estimated for all of these trees in summer 2008. Judgments on the suitability of using color-infrared aerial photography based orthophotos to estimate tree crown defoliation were based on the accuracy of the defoliation prediction. Defoliation for each crown was predicted using the non-parametric k-Nearest Neighbor (k-NN) method and characteristics extracted from the digital orthophoto maps as the auxiliary variables for prediction. Prediction accuracies were validated using the “Leave One Out” technique by comparing the predicted data with data from field-assessed crown defoliations. The lowest root mean square errors for the predicted tree crown defoliation values were 7.564 for pine trees, 9.166 for spruce and 7.712 for birch and the highest coefficients of correlation between field-estimated and predicted crown defoliations were 0.576, 0.600 and 0.386, respectively. However, there was no best performing solution for using the k-NN prediction found, as the best results were achieved using different approaches. Next, predicted and field estimated tree crown defoliation values were aggregated up to the sample plot level by taking an averaging of trees in the same sample plot. The root mean square error at the sample plot level was around 3.7 %, the bias was statistically not significant and the correlation coefficients between plot-wise average values of field-estimated and predicted defoliations were around 0.8. The achieved results suggested that color-infrared orthophoto maps could be a potential data source of forest health characteristics for use in stand-wise forest inventories.

Automatic Stem Mapping by Merging Several Terrestrial Laser Scans at the Feature and Decision Levels

Detailed up-to-date ground reference data have become increasingly important in quantitative forest inventories. Field reference data are conventionally collected at the sample plot level by means of manual measurements, which are both labor-intensive and time-consuming. In addition, the number of attributes collected from the tree stem is limited. More recently, terrestrial laser scanning (TLS), using both single-scan and multi-scan techniques, has proven to be a promising solution for efficient stem mapping at the plot level. In the single-scan method, the laser scanner is placed at the center of the plot, creating only one scan, and all trees are mapped from the single-scan point cloud. Consequently, the occlusion of stems increases as the range of the scanner increases, depending on the forest's attributes. In the conventional multi-scan method, several scans are made simultaneously inside and outside of the plot to collect point clouds representing all trees within the plot, and these scans are accurately co-registered by using artificial reference targets manually placed throughout the plot. The additional difficulty of applying the multi-scan method is due to the point-cloud registration of several scans not being fully automated yet. This paper proposes a multi-single-scan (MSS) method to map the sample plot. The method does not require artificial reference targets placed on the plot or point-level registration. The MSS method is based on the fully automated processing of each scan independently and on the merging of the stem positions automatically detected from multiple scans to accurately map the sample plot. The proposed MSS method was tested on five dense forest plots. The results show that the MSS method significantly improves the stem-detection accuracy compared with the single-scan approach and achieves a mapping accuracy similar to that achieved with the multi-scan method, without the need for the point-level registration.

HERBIVORE-DRIVEN LAND DEGRADATION: CONSEQUENCES FOR PLANT DIVERSITY AND SOIL IN ARID SUBTROPICAL THICKET IN SOUTH-EASTERN AFRICA

Investigations were made of plant and soil responses to severe degradation through heavy grazing and browsing in arid, succulent, subtropical thicket. Severe degradation of thicket is of major concern in terms of threatened biodiversity, unsustainable utilization and collapse of other ecosystem services. We used a natural, field contrast, case-study approach, sampling within plots under lightly and heavily stocked conditions. Mean plant species diversity and richness did not change significantly at sample plot level although there was a 27 per cent decline in richness with degradation at the scale of the study site. On degraded plots, there was a high species turnover and high beta diversity, which created a replacement zone rather than an impoverished zone. Replacement species were confirmed as mainly, but not exclusively, weedy annual grasses and alien forbs. The few persisting perennials were small trees that survived above the browse line, and hardy shrub species. Perennial persisting and replacement species below the browse line may suggest potential candidates for restoration. Nitrogen was the only measured nutrient that showed a significant decrease with degradation. Phosphorous, potassium and magnesium increased significantly, with the first two mentioned reaching potentially excessive levels. Soil salinization occurred with an order of magnitude increase in sodium. Thus, the increased cation exchange capacity occurred together with development of a nutrient imbalance. The elevation in some nutrients and soluble salts is ascribed to wind-determined directional grazing that concentrate livestock in the degraded area. The implications of the above altered soil conditions for thicket restoration need to be further explored. Copyright © 2012 John Wiley & Sons, Ltd.

Long-term compositional dynamics of Acadian mixedwood stands under different silvicultural regimes

Pathways of forest compositional dynamics over five decades (1953–2005) were reconstructed using measurements from permanent sample plots in a long-term silviculture experiment in a central Maine mixedwood forest. The objective of this study was to elucidate the dynamics of tree species composition at the sample plot level in relation to the initial composition when the experiment was established (1953–1957) and harvest disturbance history following a wide range of even-aged and uneven-aged silvicultural treatments. Cluster analysis revealed three groupings of sample plots based on pretreatment composition and harvest disturbance history, or nine subclusters (i.e., three harvest disturbance histories nested within each pretreatment composition). From 1953 to 2005, the silvicultural treatments generated an array of compositional outcomes at the plot level. Hardwood dominance increased following a history of heavy and infrequent harvests, while northern conifer dominance was maintained where harvests were lighter and more frequent. The importance of balsam fir ( Abies balsamea (L.) Mill.) changed little across a range of harvest intensities. A ubiquitous decline in northern white-cedar ( Thuja occidentalis L.) was found among silvicultural treatments, suggesting that additional intervention may be needed to promote cedar recruitment. Plot-level compositional dynamics indicated that neighborhood-scale stand dynamics were associated with variability in harvest disturbance overlain on plot-to-plot variability in tree species composition at the time the experiment was established.

Variation in the results of Norway spruce planting and Scots pine direct seeding in privately-owned forests in southern Finland

This study describes the variation in the planting results for 3-year-old Norway spruce (Picea abies (L.) Karst.) and 4-year-old Scots pine (Pinus sylvestris L.) using direct seeding in privately-owned regeneration areas in southern Finland. The study material consists of operative forest regeneration quality management inventory areas from the years 2000–2006. The effect of both the regional and the administrative levels as well as ecological factors was modelled on the basis of the hierarchy structure forestry centre, Forest Owners’ Association (= FOA), forestry professional, regeneration area and sample plot. The major part of the variation occurred at the sample plot and regeneration area level. Particular attention was paid to observation of the clustered spatial distribution of Scots pine seedlings. The FOA and forestry professional levels explained 5% of the variation in Norway spruce planting and 11% of the variation in Scots pine direct seeding. Applied forest regeneration operations, site and soil characteristics were included in the fixed effects. In the planting of Norway spruce the most important factor explaining the regeneration result was soil preparation. Mounding produced better results than patching and disc trenching. The site and soil characteristics were other important factors in the operations. The selection of direct seeding of Scots pine on too fertile, fine textured or moist sites yielded poor results.