Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired fromUnmannedAerialVehicles(UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model E ciency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant di erence was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantationsinfo:eu-repo/semantics/publishedVersio

Linking Optical SPOT and Unmanned Aerial Vehicle data for a rapid biomass estimation in a Forest-savanna Transitional Zone of Ghana

The direct estimation of biomass using remote sensing technologies, such as LiDAR, RaDAR and Stereo Data is limited in utility, since it does not allow for historical analysis of biomass dynamics far back in time due to their recency in development. This study links Unmanned Aerial Vehicle (UAV)-measured tree height and optical SPOT image reflectance in a mathematical model for a quick and less expensive indirect biomass estimation, and the possibility of historical analysis using the earliest captured optical data. SPOT 6/7 images were used to map land-use/cover patterns. A Phantom 4 drone images were used for height and crown width estimation. A stepwise regression analysis was conducted to establish a relationship between SPOT 6/7 channels and the UAV-generated tree heights. The linear model was used to convert the reflectance values of SPOT images into tree heights, and in turn used for crown width estimation. The estimated tree height and crown width images were used to estimate biomass using an allometric equation. There was no statistically significant difference between UAV and manual tree height measurements. UAV-estimated tree height predicted 88.0% of crown width. Regressing the tree height on the SPOT bands yielded an R2 of 66.0%. It is recommended that further studies be conducted to improve on the accuracy of estimation. It is hoped this would facilitate a quick biomass estimation and long term historical dynamics

The application of UASs in forest management and monitoring : challenges and opportunities for use in the Miombo woodland

DATA AVAILABILITY STATEMENT : The data are available on request from the corresponding authorThe Miombo woodland is the most extensive tropical woodland in south-central Africa. However, field sample plot data on forest cover changes, species distribution and carbon stocks in the Miombo ecoregion are inadequate for effective forest management. Owing to logistical challenges that come with field-based inventory methods, remote sensing plays an important role in supplementing field methods to fill in data gaps. Traditional satellite and manned aircraft remote sensing platforms have their own advantages and limitations. The advent of unmanned aerial systems (UASs) has made it possible to acquire forest data at unprecedented spatial and temporal scales. UASs are adaptable to various forest applications in terms of providing flexibility in data acquisition with different sensors (RGB, multispectral, hyperspectral, thermal and light detection and ranging (lidar)) at a convenient time. To highlight possible applications in the Miombo woodlands, we first provide an overview of the Miombo woodlands and recent progress in remote sensing with small UASs. An overview of some potential forest applications was undertaken to identify key prospects and challenges for UAS applications in the Miombo region, which will provide expertise and guidance upon which future applications in the Miombo woodlands should be based. While much of the potential of using UASs for forest data acquisition in the Miombo woodlands remains to be realized, it is likely that the next few years will see such systems being used to provide data for an ever-increasing range of forest applications.The United States Agency for International Development through Partnerships for Enhanced Engagement in Research (PEER) program, Oliver R Tambo African Research Chair Initiative (ORTARChI) project, an initiative of Canada’s International Development Research Centre (IDRC), South Africa’s National Research Foundation (NRF) and the Department of Science and Innovation (DSI), in partnership with the Oliver & Adelaide Tambo Foundation (OATF) and National Science and Technology Council, Zambia.https://www.mdpi.com/journal/forestsam2023Forestry and Agricultural Biotechnology Institute (FABI)Plant Production and Soil Scienc

Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation

Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry

This is an Accepted Manuscript of an article published by Taylor & Francis in Geocarto International on 3 dec 2018, available online: http://www.tandfonline.com/10.1080/10106049.2018.1552322Methods of estimating the total amount of above-ground biomass (AGB) in crop fields are generally based on labourious, random, and destructive in situ sampling. This study proposes a methodology for estimating herbaceous crop biomass using conventional optical cameras and structure from motion (SfM) photogrammetry. The proposed method is based on the determination of volumes according to the difference between a digital terrain model (DTM) and digital surface model (DSM) of vegetative cover. A density factor was calibrated based on a subset of destructive random samples to relate the volume and biomass and efficiently quantify the total AGB. In all cases, RMSE Z values less than 0.23 m were obtained for the DTMDSM coupling. Biomass field data confirmed the goodness of fit of the yieldbiomass estimation (R2=0,88 and 1,12 kg/ha) mainly in plots with uniform vegetation coverage. Furthermore, the method was demonstrated to be scalable to multiple platform types and sensorsThis work was supported by the life project “Operation CO2: Integrated Agroforestry Practices and Nature Conservation Against Climate Change - LIFE+ 11 ENV/ES/535” and by Xunta de Galicia under the grant “Financial aid for the consolidation and structure of competitive units of investigation in the universities of the University Galician System (2016-18)” Ref. ED431B 2016/030 and Ref. ED341D R2016/023.S

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations