Importance Of Small Diameter Woody Plants And Treelet Species In Tropical Wet Evergreen Forest Of Southern Western Ghats, India

Tropical forests have long been accepted for their productivity and ecosystem services on account of their high diversity and stand structural attributes. In spite of their significance, tropical forests, and especially those of Asia, remain understudied. Until recently, most forest inventories in Asia have concentrated on trees 10 cm in diameter. Floristic composition, plant species diversity, above-ground biomass, basal area, and diversity were investigated across different life forms and two-diameter classes in a large-scale 10-ha plot, in the undisturbed tropical seasonal rain forest of Southern Western Ghats, Kerala, India. The regeneration pattern of the study area was examined by evaluating fisher's alpha and IVI (Important Value Index) across three layers of vegetation (seedling, sapling, and tree). Within the plot, we recorded 25,390 woody plant species ≥1 cm dbh from 45 families, 91 genera, and 106 species. Plant density was 2539 woody individuals per hectare, with a basal area of 47.72 m2/ha and above-ground biomass of 421.77 Mg/ha. By basal area, density, and frequency, the Rubiaceae, Sapotaceae, and Malvaceae families were the most important. Small-diameter trees (1 cm ≤ dbh ≤10 cm) were found to be 78 percent of the total tree population, 20.2 percent of the basal area, and 1.4 percent of the aboveground biomass. They also possessed 6 percent more diversity at the family level, 10% more diversity at the genus level, and 12% more diversity at the species level than woody individuals under 10 cm dbh. Woody individuals of treelets life form and small-diameter classes were much more diverse and dense than the other groups, indicating that results based only on larger canopy trees and larger diameter class maybe not be an appropriate representation of the diversity status of a particular tropical forest type. The lower density of individuals in the initial girth class indicates the vulnerability of the forest system to anthropogenic, natural disturbance and a changing climate. Reduce the minimum diameter limit down to 1 cm, in contrast to 10 cm limit used in most of the evergreen forest inventories, revealed a high density and diversity in the lower stories.

Rapid Static Positioning Using a Four System GNSS Receivers in the Forest Environment

Global Navigation Satellite Systems (GNSS) are crucial elements used in forest inventories. Forest metrics modeling efficacy depends on the accuracy of determining sample plot locations by GNSS. As of 2021, the GNSS consists of 120 active satellites, ostensibly improving position acquisition in forest conditions. The main idea of this article was to evaluate GIS-class and geodetic class GNSS receivers on 33 control points located in the forest. The main assumptions were operating on four GNSS systems (GPS, GLONASS, Galileo, and BeiDou), keeping a continuous online connection to the network of reference stations, maintaining occupation time-limited to 60 epochs, and repeating all the measurements three times. Rapid static positioning was tested, as it compares the true performance of the four GNSS systems receivers. Statistical differences between the receivers were confirmed. The GIS-class receiver achieved an accuracy of 1.38 m and a precision of 1.29 m, while the geodetic class receiver reached 0.74 m and 0.91 m respectively. Even though the research was conducted under the same data capture conditions, the large variability of positioning results were found to be caused by cycle slips and the multipath effect.

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

ForestScanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad

Terrestrial laser scanning (TLS) is becoming increasingly popular as an alternative means to conventional forest inventory methods. By gauging the distances to multiple points on the surrounding object surfaces, TLS acquires 3D point clouds from which tree sizes and spatial distributions can be rapidly estimated. However, the high cost and specialized skills required for TLS have put it out of reach for many potential users. We here introduce ForestScanner, a free, mobile application that allows TLS-based forest inventories by means of iPhone or iPad with a built-in LiDAR sensor. ForestScanner does not require any manual analysis of 3D point clouds. As the user scans trees with an iPhone/iPad, ForestScanner estimates the stem diameters and spatial coordinates based on real-time instance segmentation and circle fitting. The users can visualize, check, and share the results of scanning in situ. By using ForestScanner, we measured the stem diameters and spatial coordinates of 672 trees within a 1 ha plot in 1 h 39 min with an iPhone and in 1 h 38 min with an iPad (diameter ≥5 cm; detection rate = 100%). ForestScanner reduced the person-hours required for measuring diameters to 25.7%, mapping trees to 9.3%, and doing both to 6.8% of the person-hours taken using a dimeter tape and a conventional surveying method. The diameters measured by ForestScanner and diameter tape were in good agreement; R2=0.963 for iPhone and R2=0.961 for iPad. ForestScanner and the conventional surveying system showed almost identical results for tree mapping (assessed by the spatial distances among trees within 0.04 ha subplots); Mantel R2=0.999 for both iPhone and iPad. Our results indicate that ForestScanner enables cost-, labor-, and time-efficient forest inventories. The application can increase the accessibility to TLS for people beyond specialists and enhance resource assessments and biodiversity monitoring in forests worldwide.

Smart Harvest Operations and Timber Processing for Improved Forest Management

Climate-smart forestry can be regarded as the evolution of traditional silviculture. As such, it must rely on smart harvesting equipment and techniques for a reliable and effective application. The introduction of sensors and digital information technologies in forest inventories, operation planning, and work execution enables the achievement of the desired results and provides a range of additional opportunities and data. The latter may help to better understand the results of management options on forest health, timber quality, and many other applications. The introduction of intelligent forest machines may multiply the beneficial effect of digital data gathered for forest monitoring and management, resulting in forest harvesting operations being more sustainable in terms of costs and environment. The interaction can be pushed even further by including the timber processing industry, which assesses physical and chemical characteristics of wood with sensors to optimize the transformation process. With the support of an item-level traceability system, the same data could provide a formidable contribution to CSF. The “memory” of wood could support scientists to understand the response of trees to climate-induced stresses and to design accordingly an adaptive silviculture, contributing to forest resilience in the face of future changes due to human-induced climate alteration.