A study of the relationship between heights and diameters at breast height in tree stands of Scots pine in the Training and Experimental Forestry of the Leningrad region

Взаимосвязи между высотами деревьев и диаметрами на высоте груди широко применяются при определении запасов и сортиментной структуры древостоев. Для их описания применяются многочисленные математические модели. Сравнительный анализ точности шести моделей на примере древостоев сосны обыкновенной Учебно-опытного лесничества Ленинградской области показал близкую точность каждой из них. При этом для разных пробных площадей наибольшую точность показывали разные модели. Это обуславливает необходимость дальнейших исследований по данной тематике с целью выявления наиболее применимых для различных древесных пород, возрастов и условий местопроизрастаний математических моделей. В практической деятельности часто используются таблицы, составленные с учетом соотношений высот и диаметров на высоте груди в древостоях, основными из которых являются таблицы объемов стволов по разрядам высот и сортиментные таблицы. В рамках исследования было установлено, что фактические зависимости высот деревьев от диаметров на высоте груди отличаются от приведенных в таблицах, что обусловлено индивидуальными особенностями структуры и условий местопроизрастания древостоев. По этой причине разряды высот, определенные для отдельных ступеней толщины, зачастую отличаются от найденных по средним для древостоя высоте и диаметру на высоте груди. Это приводит к расхождениям в найденных с учетом данных двух подходов запасах древостоев, а также выхода и стоимости сортиментов в них. Несмотря на незначительность различий, их наличие свидетельствует о необходимости дальнейших исследований с целью повышения точности определения таксационных показателей древостоев. The relationships between heights and diameters at breast height of trees are widely used in determining of growing stock and assortment structure of stands. Numerous mathematical models are used to describe them. A comparative analysis of the accuracy of six models on the example of tree stands of Scots pine in the Training and Experimental Forestry of the Leningrad region showed the close accuracy of each of them. For different sample plots, however, the highest accuracy was showed by different models. This necessitates further research on this topic in order to identify the most applicable mathematical models for different tree species, ages and habitat conditions. In practice are often used the tables, compiled taking into account the ratios of heights and diameters at breast height in tree stands, the main of which are tables of volumes of trees by height ranks and assortment tables. Within the framework of the study, it was found that the actual relationships between tree heights and diameters at breast height differ from those given in the tables, which is due to the individual features of the stand structure and habitat conditions. For this reason, the height ranks, determined for individual diameter classes often differ from those found for the average tree stand height and diameter at breast height. This leads to discrepancies in the growing stocks of tree stands, found taking into account these two approaches, as well as the yield and cost of assortments in them. Although the differences are insignificant, they highlight the need for further research in order to improve the accuracy of determining the inventory parameters of tree stands

Estimating Forest Stand Height in Savannakhet, Lao PDR Using InSAR and Backscatter Methods with L-Band SAR Data

Forest stand height (FSH), or average canopy height, serves as an important indicator for forest monitoring. The information provided about above-ground biomass for greenhouse gas emissions reporting and estimating carbon storage is relevant for reporting for Reducing Emissions from Deforestation and Forest Degradation (REDD+). A novel forest height estimation method utilizing a fusion of backscatter and Interferometric Synthetic Aperture Radar (InSAR) data from JAXA’s Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) is applied to a use case in Savannakhet, Lao. Compared with LiDAR, the estimated height from the fusion method had an RMSE of 4.90 m and an R2 of 0.26. These results are comparable to previous studies using SAR estimation techniques. Despite limitations of data quality and quantity, the Savannakhet, Lao use case demonstrates the applicability of these techniques utilizing L-band SAR data for estimating FSH in tropical forests and can be used as a springboard for use of L-band data from the future NASA-ISRO SAR (NISAR) mission.

Respuesta del ñame espino a diferentes densidades de siembra y altura de espalderas

The tubers of spiny yam are one of the main food sources for producers in the Caribbean region of Colombia. However, the productivity of the crop is low due to the scarce use of sustainable management practices that contribute to its improvement. In this sense, a study was conducted at the Turipaná Research Center of Agrosavia, El Carmen de Bolívar, with the objective of evaluating the response in yield of purple stalk hawthorn yam cv. purple stalk to different planting densities and trellis heights. Six treatments were evaluated in a randomized complete block experimental design with a split plot arrangement, the main plot corresponded to two densities (14,285 plants ha-1 and 20,000 plants ha-1) and the subplots to three trellis heights (1.6 m; 2.0 m and 2.4 m). Planting density was the only factor that significantly influenced yield; the use of 20,000 plants ha-1 increased yield by 28.68% compared to the lowest density. The use of supports with heights of 2.4 m in combination with either of the two planting densities induced the production of tubers unsuitable for commercialization. The findings of this research suggest that increasing plant density could be a safe measure to increase yields and economic efficiency in the cultivation of hawthorn yam. Future evaluations are needed on stand height and number of plants to tie per stand.

Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry

Plantation forests play a critical role in forest products and ecosystems. Unmanned aerial vehicle (UAV) remote sensing has become a promising technology in forest related applications. The stand heights will reflect the growth and competition of individual trees in plantation. UAV laser scanning (ULS) and UAV stereo photogrammetry (USP) can both be used to estimate stand heights using different algorithms. Thus, this study aimed to deeply explore the variations of four kinds of stand heights including mean height, Lorey’s height, dominated height, and median height of coniferous plantations using different models based on ULS and USP data. In addition, the impacts of thinned point density of 30 pts to 10 pts, 5 pts, 1 pts, and 0.8 pts/m2 were also analyzed. Forest stand heights were estimated from ULS and USP data metrics by linear regression and the prediction accuracy was assessed by 10-fold cross validation. The results showed that the prediction accuracy of the stand heights using metrics from USP was basically as good as that of ULS. Lorey’s height had the highest prediction accuracy, followed by dominated height, mean height, and median height. The correlation between height percentiles metrics from ULS and USP increased with the increased height. Different stand heights had their corresponding best height percentiles as variables based on stand height characteristics. Furthermore, canopy height model (CHM)-based metrics performed slightly better than normalized point cloud (NPC)-based metrics. The USP was not able to extract exact terrain information in a continuous coniferous plantation for forest canopy cover (CC) over 0.49. The combination of USP and terrain from ULS can be used to estimate forest stand heights with high accuracy. In addition, the estimation accuracy of each forest stand height was slightly affected by point density, which can also be ignored.

Tree species, crown cover, and age as determinants of the vertical distribution of airborne LiDAR returns

Key message We assessed even-aged stand vertical distributions of LiDAR returns and found that tree species, age, and crown cover each have a distinct pattern that together explains up to 47% of the variation. Abstract Light detection and ranging (LiDAR) provides information on the vertical structure of forest stands enabling detailed and extensive ecosystem study. The vertical structure is often summarized by scalar features and data-reduction techniques that limit the interpretation of results. Instead, we quantified the influence of three variables, species, crown cover, and age, on the vertical distribution of airborne LiDAR returns from forest stands. We studied 5428 regular, even-aged stands in Quebec (Canada) with five dominant species: balsam fir [Abies balsamea (L.) Mill.], paper birch (Betula papyrifera Marsh), black spruce [Picea mariana (Mill.) BSP], white spruce (Picea glauca Moench) and aspen (Populus tremuloides Michx.). We modeled the vertical distribution against the three variables using a functional general linear model and a novel nonparametric graphical test of significance. Results indicate that LiDAR returns from aspen stands had the most uniform vertical distribution. Balsam fir and white birch distributions were similar and centered at around 50% of the stand height, and black spruce and white spruce distributions were skewed to below 30% of stand height ($$p$$ p <0.001). Increased crown cover concentrated the distributions around 50% of stand height. Increasing age gradually shifted the distributions higher in the stand for stands younger than 70-years, before plateauing and slowly declining at 90–120 years. Results suggest that the vertical distributions of LiDAR returns depend on the three variables studied.

Derivating forest structure from Sentinel-1 time series to assist forest fire risk assessments

&lt;p&gt;The assessment of forest fire risk has recently gained interest in countries of Central Europe and the alpine region since the occurrence of forest fires is expected to increase with a changing climate. Information on forest fuel structure, which is related to forest structure, is a key component in such assessments. Forest structure information can be derived from airborne laser scanning (ALS) data, whose value for the derivation of respective metrics at a high accuracy level has been demonstrated in numerous studies over the last years.&lt;/p&gt;&lt;p&gt;Yet, the temporal resolution of ALS data is low as flight missions are typically carried out in time intervals of five to ten years in Central Europe. ALS-derived forest structure descriptors for fire risk assessments, therefore, are often outdated. Open access earth observation data offer the potential to fill these information gaps. Data provided by synthetic aperture radar (SAR) sensors, in particular, are of interest in this context since this technology has a known sensitivity to the vegetation structure and acquires data independent of weather or daylight conditions.&lt;/p&gt;&lt;p&gt;In our study, we investigate the potential to derive forest structure descriptors from time series of Sentinel-1 (S-1) SAR data for a deciduous forest site in the Eastern part of Austria. We focus on forest stand height and fractional cover, which is a measure for forest density, as both of these components impact forest fire propagation and ignition. The two structure metrics are estimated using a random forest (RF) model, which takes a total of 36 predictors as input, which we compute from the S-1 time series. The model is trained using ALS-derived structure metrics acquired during the same year as the S-1 data.&lt;/p&gt;&lt;p&gt;We estimated stand height with a root mean square error (RMSE) of 4.76 m and a bias of 0.09 m at 100 m resolution, while the RMSE for the fractional cover estimation is 0.08 with a bias of zero at the same resolution. The spatial comparison of the structure predictions with the ALS reference further shows that the general structure is well reproduced. Yet, fine scale variations cannot be completely reproduced by the S1-derived structure products, and the height of tall stands and very dense canopy parts are underestimated. Due to the high correlation of the predicted values to the reference (Pearson&amp;#8217;s R of 0.88 and 0.94 for the stand height and the fractional cover, respectively), we consider S-1 time series in combination with ALS data with low temporal resolution and machine learning techniques to be a reliable data source and workflow for regularly (e.g. &lt; yearly) updating ALS structure information in an operational way.&lt;/p&gt;

Potential of Sentinel-1 C-Band Time Series to Derive Structural Parameters of Temperate Deciduous Forests

With the increasing occurrence of forest fires in the mid-latitudes and the alpine region, fire risk assessments become important in these regions. Fuel assessments involve the collection of information on forest structure as, e.g., the stand height or the stand density. The potential of airborne laser scanning (ALS) to provide accurate forest structure information has been demonstrated in several studies. Yet, flight acquisitions at the state level are carried out in intervals of typically five to ten years in Central Europe, which often makes the information outdated. The Sentinel-1 (S-1) synthetic aperture radar mission provides freely accessible earth observation (EO) data with short revisit times of 6 days. Forest structure information derived from this data source could, therefore, be used to update the respective ALS descriptors. In our study, we investigated the potential of S-1 time series to derive stand height and fractional cover, which is a measure of the stand density, over a temperate deciduous forest in Austria. A random forest (RF) model was used for this task, which was trained using ALS-derived forest structure parameters from 2018. The comparison of the estimated mean stand height from S-1 time series with the ALS derived stand height shows a root mean square error (RMSE) of 4.76 m and a bias of 0.09 m on a 100 m cell size, while fractional cover can be retrieved with an RMSE of 0.08 and a bias of 0.0. However, the predictions reveal a tendency to underestimate stand height and fractional cover for high-growing stands and dense areas, respectively. The stratified selection of the training set, which we investigated in order to achieve a more homogeneous distribution of the metrics for training, mitigates the underestimation tendency to some degree, yet, cannot fully eliminate it. We subsequently applied the trained model to S-1 time series of 2017 and 2019, respectively. The computed difference between the predictions suggests that large decreases in the forest height structure in this two-year interval become apparent from our RF-model, while inter-annual forest growth cannot be measured. The spatial patterns of the predicted forest height, however, are similar for both years (Pearson’s R = 0.89). Therefore, we consider that S-1 time series in combination with machine learning techniques can be applied for the derivation of forest structure information in an operational way.

Estimating stand attributes of complex forest types in subtropical mountain environments by combining the shadow fraction method with analyses of high-dynamic-range photographs

Shadow fractions can be overestimated because of topographic shadows, which can occupy a significant area on aerial photographs of mountainous terrain. In this study, we first used high-dynamic-range (HDR) image analysis techniques to extract the original canopy shadow from the topographic shadows on aerial photographs. Subsequently, we applied the shadow fraction method to estimate selected forest attributes (stand height, basal area, and stem volume). In this paper, we discuss the effects of tree shadow fraction normalization, auxiliary spectral information, and forest type on forest attribute estimation. HDR image analysis successfully extracted canopy shadow information from topographic shadows. The tree shadow fraction normalization method had no obvious effect. The shadow fraction enhanced spectral information to estimate stand attributes. Using shadow fractions resulted in better estimates of stand height for mixed-hardwood forest ([Formula: see text] = 0.45), basal area for mixed-hardwood forest ([Formula: see text] = 0.50), and stem volume for conifer–hardwood forest ([Formula: see text] = 0.43). This difference in estimated results is related to the shade patterns produced by stand structures in the different forest types.

Deadwood Characteristics in Mature and Old-Growth Birch Stands and Their Implications for Carbon Storage

As one of the most abundant tree species in the hemiboreal zone, birch is important from both commercial and biodiversity perspectives. While old-growth deciduous stands are important for biodiversity conservation with an emphasis on deadwood availability, the role that deadwood in these stands plays in carbon sequestration remains unclear. We studied mature (71–110 years old) and old-growth (121–150 years old) birch stands on fertile mineral soils. The marginal mean deadwood volume was 43.5 ± 6.4 m3 ha−1 in all mature stands, 51.3 ± 7.1 m3 ha−1 in recently unmanaged mature stands, and 54.4 ± 4.4 m3 ha−1 in old-growth stands; the marginal mean deadwood carbon pool for each stand type was 5.4 ± 0.8 t·ha−1, 6.3 ± 0.9 t·ha−1, and 7.9 ± 0.6 t·ha−1, respectively. Deadwood volume was not related to stand productivity in terms of stand basal area, stand height, or stand age. The difference between mature and old-growth stands remained non-significant (p < 0.05). A high volume of deadwood was almost continuously present throughout the landscape in assessed unmanaged sites; moreover, 88% of sample plots in old-growth stands and 63% of sample plots in mature stands had a deadwood volume higher than 20 m3·ha−1. Old-growth stands had a slightly greater volume of large deadwood than unmanaged mature stands; in both, almost half of the deadwood was more than 30 cm in diameter and approximately one-fifth had a diameter greater than 40 cm. Both groups of stands had similar proportions of coniferous and deciduous deadwood and lying and standing deadwood. Old-growth stands had a higher volume of recently and weakly decayed wood, indicating increased dieback during recent years.

Reach scale analysis of riparian vegetation interactions with fluvial morphology using UAV based laser scanning and multispectral imaging

&lt;p&gt;River corridors are greatly influenced by vegetation, whether it be through direct interactions with flow, influencing the stability of banks, or contributing to floodplain roughness. With vegetation present across many of the world&amp;#8217;s river corridors in one form or another, it is a vital component of the active river corridor that receives relatively less attention than the flow and morphological components. This is partly because the routine monitoring of the very complex and temporally dynamic structure of vegetation is challenging.&amp;#160; Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS) have been used to monitor fluvial vegetation across scales. However, whilst UAVs and Structure from Motion (SfM) techniques have recently bridged the gap between fine scale local surveys and coarse larger surveys for fluvial morphology, they are not well suited to characterising complex vegetation.&lt;/p&gt;&lt;p&gt;A UAV based laser scanning and imagery system has been developed which enables the collection of high resolution (&gt; 300 points m2) point cloud data (first and last return) to analyse vegetation structure alongside simultaneous multispectral imagery data, including the red edge band. Such data can be collected on scales from metres to kilometres depending on the needs of the user, and is capable of picking out vegetation structure using metrics such as stand height, vertical distribution, canopy health, plant density etc. Moreover, the collection of this data through time will allow the evaluation of how these factors change across seasons, subsequently filling a void in data collection between spatially limited TLS and temporally limited ALS. Here we show some examples of how the data can be used to establish interactions between vegetation, flow and fluvial morphology from a series of flights over a 1 km reach of the River Teme, UK. These examples highlight how the data enables us to begin to establish a more detailed conceptual understanding of temporally evolving fluvial-vegetation interactions along river corridors.&lt;/p&gt;