Analyses of the feasibility of participatory REDD+ MRV approaches to Lidar assisted carbon inventories in Nepal

Forests are estimated to sequester and emit respectively 15% and 20% of the CO2 emissions. REDD+ aims at establishing a financial framework to compensate developing countries for reducing Green House Gasses emissions due to decreased deforestation and land degradation. An accurate Monitoring, Reporting and Verification (MRV) of the forest carbon pools is needed. The adoption of State-Of-The-Art remote sensing technologies, such as Lidar in combination with participatory approaches can potentially produce an accurate assessment of the forest resources, ensuring the sustainability of the process. The study aims at defining the feasibility of Lidar assisted Above Ground Biomass (AGB) assessment with a participatory approach. The study compares AGB regression models built with wall-to-wall, low density (0.8 points m-2) laser scanning data and two field datasets collected by professionals and Community Forest User Groups (CFUGs) teams. The models were built using ArboLiDAR©, a tool-box developed in ESRI environment by Arbonaut Oy, that uses a Sparse Bayesian approach to define a set of weights for each independent variable based on the variance of the field measured AGB and the Lidar metrics. Finally the models were validated with Leave-One-Out Cross Validation (LOOCV). The adjusted R2, relative RMSE and BIAS as well as the analyses of the residuals were used to compare the models. In addition the study also analyzed the reliability of the models across different forest structures. The professional model described a greater part of the variability of the AGB (adj.R2=0.75) compared the CFUG model (adj. R2=0.55), moreover the first was slightly more accurate (professional: rel. RMSE= 45.6 %; CFUG: rel. RMSE= 47.2 %). Although both of the models proved to have the mean of the error term not equal to zero and did not follow a normal distribution, the CUFG model showed heteroschedastic residuals. The accuracy improved when applying the models to forests characterized by a more uniform height distribution (rel. RMSE= 32.1 – 45.2 %), whereas it drastically decreased for sparse forests (rel. RMSE= 91.4 -130.5 %). The study concludes that with the limitation of having different sampling designs and measuring techniques the CFUGs models were slightly worst than the professional ones. However, it is likely that with a more accurate retrieval of the GPS plot center and increase of plot size the results can be as good as the ones obtained with professionally collected data

Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests

Abstract In the EU 2020 biodiversity strategy, maintaining and enhancing forest biodiversity is essential. Forest managers and technicians should include biodiversity monitoring as support for sustainible forest management and conservation issues, through the adoption of forest biodiversity indices. The present study investigates the potential of a new type of Structure from Motion (SfM) photogrammetry derived variables for modelling forest structure indicies, which do not require the availability of a digital terrain model (DTM) such as those obtainable from Airborne Laser Scanning (ALS) surveys. The DTM-independent variables were calculated using raw 3D UAV photogrammetric data for modeling eight forest structure indices which are commonly used for forest biodiversity monitoring, namely: basal area (G); quadratic mean diameter (DBHmean); the standard deviation of Diameter at Breast Height (DBHσ); DBH Gini coefficient (Gini); the standard deviation of tree heights (Hσ); dominant tree height (Hdom); Lorey's height (Hl); and growing stock volume (V). The study included two mixed temperate forests areas with a different type of management, with one area, left unmanaged for the past 50 years while the other being actively managed. A total of 30 field sample plots were measured in the unmanaged forest, and 50 field plots were measured in the actively managed forest. The accuracy of UAV DTM-independent predictions was compared with a benchmark approach based on traditional explanatory variables calculated from ALS data. Finally, DTM-independent variables were used to produce wall-to-wall maps of the forest structure indices in the two test areas and to estimate the mean value and its uncertainty according to a model-assisted regression estimators. DTM-independent variables led to similar predictive accuracy in terms of root mean square error compared to ALS in both study areas for the eight structure indices (DTM-independent average RMSE% = 20.5 and ALS average RMSE% = 19.8). Moreover, we found that the model-assisted estimation, with both DTM-independet and ALS, obtained lower standar errors (SE) compared to the one obtained by model-based estimation using only field plots. Relative efficiency coefficient (RE) revealed that ALS-based estimates were, on average, more efficient (average RE ALS = 3.7) than DTM-independent, (average RE DTM-independent = 3.3). However, the RE for the DTM-independent models was consistently larger than the one from the ALS models for the DBH-related variables (i.e. G, DBHmean, and DBHσ) and for V. This highlights the potential of DTM-independent variables, which not only can be used virtually on any forests (i.e., no need of a DTM), but also can produce as precise estimates as those from ALS data for key forest structural variables and substantially improve the efficiency of forest inventories

Multitemporal Sentinel-1 and Sentinel-2 Images for Characterization and Discrimination of Young Forest Stands Under Regeneration in Norway

There is a need for mapping of forest areas with young stands under regeneration in Norway, as a basis for conducting tending, or precommercial thinning (PCT), whenever necessary. The main objective of this article is to show the potential of multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) data for characterization and detection of forest stands under regeneration. We identify the most powerful radar and optical features for discrimination of forest stands under regeneration versus other forest stands. A number of optical and radar features derived from multitemporal S-1 and S-2 data were used for the class separability and cross-correlation analysis. The analysis was performed on forest resource maps consisting of the forest development classes and age in two study sites from south-eastern Norway. Important features were used to train the classical random forest (RF) classification algorithm. A comparative study of performance of the algorithm was used in three cases: I) using only S-1 features, II) using only S-2 optical bands, and III) using combination of S-1 and S-2 features. RF classification results pointed to increased class discrimination when using S-1 and S-2 data in relation to S-1 or S-2 data only. The study shows that forest stands under regeneration in the height interval for PCT can be detected with a detection rate of 91% and F-1 score of 73.2% in case III as most accurate, while tree density and broadleaf fraction could be estimated with coefficient of determination (R 2 ) of about 0.70 and 0.80, respectively

Reindeer carcasses provide foraging habitat for insectivorous birds of the alpine tundra

On August of 2016, almost an entire herd (n = 323) of wild tundra reindeer (Rangifer tarandus) was killed by lightning on Hardangervidda in southern Norway. While conducting fieldwork for another study in 2017, we opportunistically registered the occurrence and behaviour of birds on carcasses from this mass die-off. Several passerine species other than corvids were observed actively foraging on arthropods, such as blowfly (Calliphoridae sp.) adults and larvae, which are typically associated with carcass decomposition. We quantified observations of those birds, and described their foraging behaviour at the carcass site. In decreasing order of abundance, five passerine species were observed taking arthropods at the site: Meadow Pipit (Anthus pratensis), Northern Wheatear (Oenanthe oenanthe), Common Reed Bunting (Emberiza schoeniclus), Bluethroat (Luscinia svecica,), and Lapland Bunting (Calcarius lapponicus). Systematic surveys of passerines utilizing carcass sites would further our understanding of how such resources may affect behaviour and life history of various bird species

Country-wide retrieval of forest structure from optical and SAR satellite imagery with deep ensembles

Monitoring and managing Earth’s forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-m resolution, using freely available satellite imagery as input. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 syntheticaperture radar images into maps of five different forest structure variables: 95th height percentile, mean height, density, Gini coefficient, and fractional cover. We train and test our model on reference data from 41 airborne laser scanning missions across Norway and demonstrate that it is able to generalize to unseen test regions, achieving normalized mean absolute errors between 11% and 15%, depending on the variable. Our work is also the first to propose a variant of so-called Bayesian deep learning to densely predict multiple forest structure variables with well-calibrated uncertainty estimates from satellite imagery. The uncertainty information increases the trustworthiness of the model and its suitability for downstream tasks that require reliable confidence estimates as a basis for decision making. We present an extensive set of experiments to validate the accuracy of the predicted maps as well as the quality of the predicted uncertainties. To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.publishedVersio

Towards accurate instance segmentation in large-scale LiDAR point clouds

Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy

FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees

The FOR-instance dataset (available at https://doi.org/10.5281/zenodo.8287792) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite the growing need for detailed tree data, automating segmentation and tracking scientific progress remains difficult. Existing methodologies often overfit small datasets and lack comparability, limiting their applicability. Amid the progress triggered by the emergence of deep learning methodologies, standardized benchmarking assumes paramount importance in these research domains. This data paper introduces a benchmarking dataset for dense airborne laser scanning data, aimed at advancing instance and semantic segmentation techniques and promoting progress in 3D forest scene segmentation. The FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections from diverse global locations, representing various forest types. The laser scanning data were manually annotated into individual trees (instances) and different semantic classes (e.g. stem, woody branches, live branches, terrain, low vegetation). The dataset is divided into development and test subsets, enabling method advancement and evaluation, with specific guidelines for utilization. It supports instance and semantic segmentation, offering adaptability to deep learning frameworks and diverse segmentation strategies, while the inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable. In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data

UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce

Numerous species of pathogenic wood decay fungi, including members of the genera Heterobasidion and Armillaria, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (Picea abies L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13).publishedVersio