Forest Inventory Sample Plots Research Articles

A stand level scenario model for the Norwegian forestry – a case study on forest management under climate change

Carbon sequestration and income generation are competing objectives in modern forest management. The climate commitments of many countries depend on forests as carbon sinks which must be quantified, monitored, and projected into the future. For projections we need tools to model forest development and perform scenario analyses to assess future carbon sequestration potentials under different management regimes, the expected net present value of such regimes, and possible impacts of climate change. We propose a scenario analysis software tool (GAYA 2.0) that can assist in answering these types of questions using stand level simulations, detailed carbon flow models and an optimizer. This paper has two objectives: (1) to describe GAYA 2.0, and (2) demonstrate its potential in a case study where we analyze the forest carbon balance over a region in Norway based on national forest inventory sample plots. The tool was used to map the optimality front between the carbon benefit and net present value. We observed changes in net present value for different levels of carbon benefit as well as changes in optimal management strategies. We predicted future changes in several forest carbon pools as well as albedo and illustrated the impact of gradual increase in forest productivity (i.e., due to climate warming). Having been updated and modernized from its previous version with increased attention to forest carbon and energy fluxes, GAYA 2.0 is an effective tool that offers multiple opportunities to perform various types of scenario analyses in forest management.

Modeling direct above-ground carbon loss due to urban expansion in Zanzibar City Region, Tanzania

Abstract Expansion of urban fabric on carbon storages is estimated to cause loss of 1.38 Pg of Above-Ground Carbon (AGC) in pan-tropics between 2000 and 2030. This would be approximately 5% of all emissions caused by tropical land use changes. Despite the significance of the phenomenon, these emissions are rarely measured, monitored, or addressed in climate change mitigation plans, especially in Sub-Saharan Africa. Therefore, we demonstrated a state-of-the-art approach predicting AGC loss of Zanzibar City Region under multiple alternative urban planning scenarios between 2013 and 2030. The AGC information was modeled based on field measured forest inventory sample plots and RapidEye satellite data from 2013, while the future urban expansion model was calibrated with data of happened expansion between 2004, 2009 and 2013, and geospatial independent variables influencing the expansion patterns. This model was then projected until 2030, while alternative urban planning scenarios were integrated to the model by modifying the geospatial variables. The combination of these two models indicates that 42,000 Mg or 15% of total AGC in Zanzibar City Region can be anticipated to be lost by 2030 due to urban expansion. Majority of the loss will take place in the agroforest and fruit tree plantations surrounding the city, while natural forest face limited impacts. None of the tested alternative urban planning scenarios significantly impact the loss of AGC compared to the business-as-usual scenario. Therefore, alternative policies and plans are seriously needed to address the issue in Zanzibar. These could include promoting urban densification, directing urban expansion to low carbon areas, improving soil carbon management, and preparing an urban forestry and greenery strategy. All in all, the study indicates that data and methods are available for monitoring and predicting the phenomenon in Sub-Saharan Africa. Research based on a comparable methodology should be produced from all the main cities in the region that are surrounded by significant carbon storages and facing high urban expansion rates to support climate change mitigation.

Modeling direct above-ground carbon loss due to urban expansion in Zanzibar City Region, Tanzania

Expansion of urban fabric on carbon storages is estimated to cause loss of 1.38 Pg of Above-Ground Carbon (AGC) in pan-tropics between 2000 and 2030. This would be approximately 5% of all emissions caused by tropical land use changes. Despite the significance of the phenomenon, these emissions are rarely measured, monitored, or addressed in climate change mitigation plans, especially in Sub-Saharan Africa. Therefore, we demonstrated a state-of-the-art approach predicting AGC loss of Zanzibar City Region under multiple alternative urban planning scenarios between 2013 and 2030. The AGC information was modeled based on field measured forest inventory sample plots and RapidEye satellite data from 2013, while the future urban expansion model was calibrated with data of happened expansion between 2004, 2009 and 2013, and geospatial independent variables influencing the expansion patterns. This model was then projected until 2030, while alternative urban planning scenarios were integrated to the model by modifying the geospatial variables. The combination of these two models indicates that 42,000 Mg or 15% of total AGC in Zanzibar City Region can be anticipated to be lost by 2030 due to urban expansion. Majority of the loss will take place in the agroforest and fruit tree plantations surrounding the city, while natural forest face limited impacts. None of the tested alternative urban planning scenarios significantly impact the loss of AGC compared to the business-as-usual scenario. Therefore, alternative policies and plans are seriously needed to address the issue in Zanzibar. These could include promoting urban densification, directing urban expansion to low carbon areas, improving soil carbon management, and preparing an urban forestry and greenery strategy. All in all, the study indicates that data and methods are available for monitoring and predicting the phenomenon in Sub-Saharan Africa. Research based on a comparable methodology should be produced from all the main cities in the region that are surrounded by significant carbon storages and facing high urban expansion rates to support climate change mitigation.

Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology

The estimation of single tree and complete stand information is one of the central tasks of forest inventory. In recent years, automatic algorithms have been successfully developed for the detection and measurement of trees with laser scanning technology. Nevertheless, most of the forest inventories are nowadays carried out with manual tree measurements using traditional instruments. This is due to the high investment costs for modern laser scanner equipment and, in particular, the time-consuming and incomplete nature of data acquisition with stationary terrestrial laser scanners. Traditionally, forest inventory data are collected through manual surveys with calipers or tapes. Practically, this is both labor and time-consuming. In 2020, Apple implemented a Light Detection and Ranging (LiDAR) sensor in the new Apple iPad Pro (4th Gen) and iPhone Pro 12. Since then, access to LiDAR-generated 3D point clouds has become possible with consumer-level devices. In this study, an Apple iPad Pro was tested to produce 3D point clouds, and its performance was compared with a personal laser scanning (PLS) approach to estimate individual tree parameters in different forest types and structures. Reference data were obtained by traditional measurements on 21 circular forest inventory sample plots with a 7 m radius. The tree mapping with the iPad showed a detection rate of 97.3% compared to 99.5% with the PLS scans for trees with a lower diameter at a breast height (dbh) threshold of 10 cm. The root mean square error (RMSE) of the best dbh measurement out of five different dbh modeling approaches was 3.13 cm with the iPad and 1.59 cm with PLS. The data acquisition time with the iPad was approximately 7.51 min per sample plot; this is twice as long as that with PLS but 2.5 times shorter than that with traditional forest inventory equipment. In conclusion, the proposed forest inventory with the iPad is generally feasible and achieves accurate and precise stem counts and dbh measurements with efficient labor effort compared to traditional approaches. Along with future technological developments, it is expected that other consumer-level handheld devices with integrated laser scanners will also be developed beyond the iPad, which will serve as an accurate and cost-efficient alternative solution to the approved but relatively expensive TLS and PLS systems. Such a development would be mandatory to broadly establish digital technology and fully automated routines in forest inventory practice. Finally, high-level progress is generally expected for the broader scientific community in forest ecosystem monitoring, as the collection of highly precise 3D point cloud data is no longer hindered by financial burdens.

Comparison of 3D Point Clouds Obtained by Terrestrial Laser Scanning and Personal Laser Scanning on Forest Inventory Sample Plots

In forest inventory, trees are usually measured using handheld instruments; among the most relevant are calipers, inclinometers, ultrasonic devices, and laser range finders. Traditional forest inventory has been redesigned since modern laser scanner technology became available. Laser scanners generate massive data in the form of 3D point clouds. We have developed a novel methodology to provide estimates of the tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to test new software routines for the automatic measurement of forest trees using laser scanner data. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 20 forest inventory sample plots in Austria. The data were collected by two different laser scanning systems: (1) A mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK) and (2) a static terrestrial laser scanner (TLS) (Focus3D X330, Faro Technologies Inc., Lake Mary, FL, USA). The data also contain digital terrain models (DTMs), field measurements as reference data (ground-truth), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.

A Flexible Height–Diameter Model for Tree Height Imputation on Forest Inventory Sample Plots Using Repeated Measures from the Past

In this study, height–diameter relations were modeled using two different mixed model types for imputation of missing heights from longitudinal data. Model Type A had a hierarchical structure of sample plot-specific and measurement occasion-specific random effects. In Model Type B, a possible temporal variance was modeled by a sample plot-specific linear time trend. Furthermore, various calibration strategies of random effects were performed on past and current data, and a combination of both. The performance of the mixed models was compared on independent data using bias and root mean square error (RMSE). The results showed that Model Type A achieved the highest precision (lowest RMSE), if sample plot-specific random effects were predicted from old data and measurement occasion-specific ones were predicted from new data. In comparison, however, Model Type B had a higher RMSE, and lower bias. Model performance was almost unaffected from the usage of past or current data for the prediction of random effects. Results revealed that a certain calibration strategy should be simultaneously applied to all random effects from the same hierarchy level. Otherwise, predictions would become imprecise and a serious bias may result. In comparison with traditional uniform height curves, the novel mixed model approach performed slightly better.

Terrestrial Laser Scanning for Forest Inventories—Tree Diameter Distribution and Scanner Location Impact on Occlusion

The rapid development of portable terrestrial laser scanning (TLS) devices in recent years has led to increased attention to their applicability for forest inventories, especially where direct measurements are very expensive or nearly impossible. However, in terms of precision and reproducibility, there are still some pending questions. In this study, we investigate the influence of stand parameters on the TLS-related visibility in forest plots. We derived 2740 stand parameters from Swiss national forest inventory sample plots. Based on these parameters, we defined virtual scenes of the forest plots with the software “Blender”. Using Blender’s ray-tracing features, we assessed the 3D coverage in a cubic space and 2D visibility properties for each of the virtual plots with different scanner placement schemes. We provide a formula to calculate the maximum number of possible hits for any object size at any distance from a scanner with any resolution. Additionally, we show that the Weibull scale parameter describing a stand, in addition to the number of trees and the mean diameter of the dominant 100 trees per hectare, has a significant and relevant influence on the visibility of the sample plot. Furthermore, we show the effectiveness and the efficiency of 40 scanner location patterns. These experiments demonstrate that intuitively distributing scanner locations evenly within the sample plot, with similar distances between locations and from the edge of the sample plot, provides the best overall visibility of the stand.

Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

Image-based point clouds obtained using aerial photogrammetry share many characteristics with point clouds obtained by airborne laser scanning (ALS). Two approaches have been used to predict forest parameters from ALS: the area-based approach (ABA) and the individual tree crown (ITC) approach. In this article, we apply the semi-ITC approach, a variety of the ITC approach, on an image-based point cloud to predict forest parameters and compare the performance to the ABA. Norwegian National Forest Inventory sample plots on a site in southeastern Norway were used as the reference data. Tree crown objects were delineated using a watershed segmentation algorithm, and explanatory variables were calculated for each tree crown segment. A multivariate kNN model for timber volume, stem density, basal area and quadratic mean diameter with the semi-ITC approach produced RMSEs of 30%, 46%, 25%, 26%, respectively. The corresponding measures for the ABA were 30%, 51%, 26%, 35%, respectively. Univariate kNN models resulted in timber volume RMSEs of 25% for the semi-ITC approach and 22% for the ABA. A non-linear logistic regression model with the ABA produced an RMSE of 23%. Both approaches predicted timber volume with comparable precision and accuracy at the plot level. The multivariate kNN model was slightly more precise with the semi-ITC approach, while biases were larger

Large-scale forest inventories of the United States and China reveal positive effects of biodiversity on productivity

Abstract Background With the loss of species worldwide due to anthropogenic factors, especially in forested ecosystems, it has become more urgent than ever to understand the biodiversity-ecosystem functioning relationship (BEFR). BEFR research in forested ecosystems is very limited and thus studies that incorporate greater geographic coverage and structural complexity are needed. Methods We compiled ground-measured data from approx. one half million forest inventory sample plots across the contiguous United States, Alaska, and northeastern China to map tree species richness, forest stocking, and productivity at a continental scale. Based on these data, we investigated the relationship between forest productivity and tree species diversity, using a multiple regression analysis and a non-parametric approach to account for spatial autocorrelation. Results In general, forests in the eastern United States consisted of more tree species than any other regions in the country. The highest forest stocking values over the entire study area were concentrated in the western United States and Central Appalachia. Overall, 96.4 % of sample plots (477,281) showed a significant positive effect of species richness on site productivity, and only 3.6 % (17,349) had an insignificant or negative effect. Conclusions The large number of ground-measured plots, as well as the magnitude of geographic scale, rendered overwhelming evidence in support of a positive BEFR. This empirical evidence provides insights to forest management and biological conservation across different types of forested ecosystems. Forest timber productivity may be impaired by the loss of species in forests, and biological conservation, due to its potential benefits on maintaining species richness and productivity, can have profound impacts on the functioning and services of forested ecosystems.

NFI plots as complementary reference data in forest inventory based on airborne laser scanning and aerial photography in Finland

In Finland, there are currently two, parallel sample-plot-based forest inventory systems, which differ in their methodologies, sampling designs, and objectives. One is the National Forest Inventory (NFI), aimed at unbiased inventory results at national and regional level. The other is the Forest Centre’s management-oriented forest inventory based on interpretation of airborne laser scanning and aerial images, with the aim of locally accurate stand-level forest estimates. The National Forest Inventory utilises relascope sample plots with systematic cluster sampling. This inventory method is optimised for accuracy of regional volume estimates. In contrast, the management-oriented forest inventory utilises circular sample plots with an allocation system covering certain pre-defined forest classes in the inventory area. This method is optimised to produce reference data for interpretation of the remote-sensing materials in use. In this study, we tested the feasibility of the National Forest Inventory sample plots in provision of additional reference data for the management-oriented inventory. Various combinations of NFI plots and management inventory plots were tested in the interpretation of the laser and aerial-image data. Adding NFI plots in the reference data generally improved the accuracy of the volume estimates by tree species but not the estimates of total volume or stand mean height and diameter. The difference between the plot types in the NFI and management inventories causes difficulties in combination of the two datasets.

Comparing simulations of three conceptually different forest models with National Forest Inventory data

Although they were originally introduced for different purposes, forest models are often used today for scenario development, which includes forest production as one aspect of forest development. However, studies using an independent data set to compare different simulators are rarely found. In this study a subset of National Forest Inventory data for the whole of Austria was compared to simulations of the biogeochemistry model Biome-BGC, the hybrid gap model PICUS and a climate sensitive version of the growth and yield model PrognAus. The models were used to simulate the development of approximately 700 forest inventory sample plots over a period of 15 years. The study focussed on the models' sensitivity to varying environmental conditions; thus, the comparison was based on the mean current annual volume increment per hectare. All models showed a significant average deviation from the inventory (over- or under-estimation). The estimated year-to-year variation was best reproduced by PICUS. However, the 15 year growth trend was also shown by Biome-BGC and PrognAus. Potential model users interested in relating mean current annual volume increment to climate data will need to weigh accuracy against applicability when choosing among these models.

Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information

The aim was to investigate the relations between diameter at breast height and maximum crown diameter, tree height and other possible independent variables available in stand databases. Altogether 76 models for estimating stem diameter at breast height and 60 models for tree age were formulated using height and maximum crown diameter as independent variables. These types of models can be utilized in modern remote sensing applications where tree crown dimensions and tree height are measured automatically. Data from Finnish national forest inventory sample plots located throughout the country were used to develop the models, and a separate test site was used to evaluate them. The RMSEs of the diameter models for the entire country varied between 7.3% and 14.9% from the mean diameter depending on the combination of independent variables and species. The RMSEs of the age models for entire country ranged from 9.2% to 12.8% from the mean age. The regional models were formulated from a data set in which the country was divided into four geographical areas. These regional models reduced local error and gave better results than the general models. The standard deviation of the dbh estimate for the separate test site was almost 5 cm when maximum crown width alone was the independent variable. The deviation was smallest for birch. When tree height was the only independent variable, the standard deviation was about 3 cm, and when both height and maximum crown width were included it was under 3 cm. In the latter case, the deviation was equally small (11%) for birch and Norway spruce and greatest (13%) for Scots pine.

Relationships between ground-based forest data and a multiple-interpretation-based certainty map

The locations of 482 ground-based forest inventory sample plots were overlaid on a map indicating the certainty of the forest type as identified at each location by aerial photo-interpreters. Plots were then stratified into areas of high and low certainty and the means and coefficients of variation for wood volume calculated for each group. Results indicated that the measured wood volumes from sample plots located on areas of high map certainty were higher and less variable than wood volume estimates from sample plots located on areas of low map certainty. It is suggested that sample plots located on areas of high certainty can be used from one forest inventory to another to provide long-term cost-savings.

Seasonal dynamics of C-band backscatter of boreal forests with applications to biomass and soil moisture estimation

The seasonal changes of the C-band backscattering properties of boreal forests are investigated by applying 1) a semiempirical forest backscattering model and 2) multitemporal ERS-1 SAR data from two test areas in Finland. The semiempirical modeling of forest canopy volume backscattering and extinction properties is based on high-resolution data from the authors' ranging scatterometer HUTSCAT. The response of ERS-1 SAR to forest stem volume (biomass) and other forest characteristics is investigated by employing the National Forest Inventory sample plots, stand-wise forest inventory data and LANDSAT- and SPOT-based digital land use maps. The results show that the correlation between the backscattering coefficient and forest stem volume (biomass) varies from positive to negative depending on canopy and soil moisture. Additionally, the seasonal snow cover and soil freezing/thawing effects cause drastic changes in the radar response. A novel method for the estimation of forest stem volume (biomass) is introduced. This technique is based on the use of: 1) multitemporal ERS-1 SAR data; 2) reference sample plot information; and 3) the semiempirical backscattering model. It is shown that the multitemporal ERS-1 SAR images can be successfully used for estimating the forest stem volume. The effects of soil moisture variations to ERS-1 SAR results have been analyzed using hydrological soil moisture model and in situ data. The results indicate that the semiempirical model can he used for predicting the soil and canopy moisture variations in ERS-1 images.

A cooperative, integrated project to classify forest sites in Newfoundland

Most existing systems of forest site classification attempt to combine vegetation, soil, terrain, geology, climatic and hydrologic factors. The current paper describes an ongoing project to assess the relationship of these factors to forest site capability in Newfoundland. Through the description and classification of forest inventory plots, this project is providing productivity data for species, descriptions of vegetational succession, growth and yield projections, as well as an indication of soil type variation within Forest Management Districts.The cooperative, multi-agency approach employed in this project has benefited all parties concerned, and has resulted in a mix of expertise and focus of resources that would not be possible within one agency. A large and valuable bank of vegetation, soil and site data is being acquired. Elements of the project include: 1) establishment of permanent forest inventory sample plots, in Management Districts throughout the province; 2) training of forest inventory crews so that they recognize forest vegetation, accurately prepare soil descriptions and undertake soil sampling; 3) laboratory analyses of soil samples for chemical and physical parameters, and integration of these results into the forest site classification; and, 4) formal reporting of the forest site classification, including improved descriptions of Damman Forest Site Types (FSTs) and soils.

Derivation of a predictive model for production of tree species composition maps at small scales using discriminant function analysis

Functions for the prediction of tree species composition category were estimated by means of discriminant function analysis. Five tree species composition categories were defined, and the functions were calibrated using the National Forest Inventory sample plots from two counties in eastern Norway. The independent variables were site index, altitude, and slope, which can all be derived from spatial databases. Tree species composition category was predicted for the calibration data and independent test data. The prediction was based on a deterministic and a probabilistic approach. About 30–60% of the observations were correctly classified. The deterministic approach gave large systematic errors for the individual categories. The probabilistic approach gave no such errors. Tree species composition category can be predicted within a GIS environment for production of small scale thematic maps. Although the classification accuracy was rather low, the functions might be used for map production in the absence of better alternatives.