Airborne laser scanning of natural forests in New Zealand reveals the influences of wind on forest carbon

Abstract Background Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results Power-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind. </jats:sec

Wood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data

5openInternationalInternational coauthor/editorWood decay caused by pathogenic fungi in Norway spruce forests causes severe economic losses in the forestry sector, and currently no efficient methods exist to detect infected trees. The detection of wood decay could potentially lead to improvements in forest management and could help in reducing economic losses. In this study, airborne hyperspectral data were used to detect the presence of wood decay in the trees in two forest areas located in Etnedal (dataset I) and Gran (dataset II) municipalities, in southern Norway. The hyperspectral data used consisted of images acquired by two sensors operating in the VNIR and SWIR parts of the spectrum. Corresponding ground reference data were collected in Etnedal using a cut-to-length harvester while in Gran, field measurements were collected manually. Airborne laser scanning (ALS) data were used to detect the individual tree crowns (ITCs) in both sites. Different approaches to deal with pixels inside each ITC were considered: in particular, pixels were either aggregated to a unique value per ITC (i.e., mean, weighted mean, median, centermost pixel) or analyzed in an unaggregated way. Multiple classification methods were explored to predict rot presence: logistic regression, feed forward neural networks, and convolutional neural networks. The results showed that wood decay could be detected, even if with accuracy varying among the two datasets. The best results on the Etnedal dataset were obtained using a convolution neural network with the first five components of a principal component analysis as input (OA = 65.5%), while on the Gran dataset, the best result was obtained using LASSO with logistic regression and data aggregated using the weighted mean (OA = 61.4%). In general, the differences among aggregated and unaggregated data were smallopenDalponte, Michele; Kallio, Alvar J. I.; Ørka, Hans Ole; Næsset, Erik; Gobakken, TerjeDalponte, M.; Kallio, A.J.I.; Ørka, H.O.; Næsset, E.; Gobakken, T

Estimation of the occurrence, severity, and volume of heartwood rot using airborne laser scanning and optical satellite data

Rot in commercial timber reduces the value of the wood substantially and estimating the occurrence, severity, and volume of heartwood rot would be a useful tool in decision-making to minimize economic losses. Remotely sensed data has recently been used for mapping rot on a single-tree level, and although the results have been relatively poor, some potential has been shown. This study applied area-based approaches to predict rot occurrence, rot severity, and rot volume , at an area level. Ground reference data were collected from harvester operations in 2019–2021. Predictor variables were calculated from multi-temporal remotely sensed data together with environmental variables. Response variables from the harvester data and predictor variables from remotely sensed data were aggregated to grid cells and to forest stands. Random Forest models were built for the different combinations of response variables and predictor subsets, and validated with both random- and spatial cross-validation. The results showed that it was not possible to estimate rot occurrence and rot severity with the applied modeling procedure (pR2: 0.00–0.16), without spatially close training data. The better performance of rot volume models (pR2: 0.12–0.37) was mainly due to the correlation between timber volume and rot volum

Individual Tree Species Classification from Airborne Multisensor Imagery Using Robust PCA

Remote sensing of individual tree species has many applications in resource management, biodiversity assessment, and conservation. Airborne remote sensing using light detection and ranging (LiDAR) and hyperspectral sensors has been used extensively to extract biophysical traits of vegetation and to detect species. However, its application for individual tree mapping remains limited due to the technical challenges of precise coalignment of images acquired from different sensors and accurately delineating individual tree crowns (ITCs). In this study, we developed a generic workflow to map tree species at ITC level from hyperspectral imagery and LiDAR data using a combination of well established and recently developed techniques. The workflow uses a nonparametric image registration approach to coalign images, a multiclass normalized graph cut method for ITC delineation, robust principal component analysis for feature extraction, and support vector machine for species classification. This workflow allows us to automatically map tree species at both pixel- and ITC-level. Experimental tests of the technique were conducted using ground data collected from a fully mapped temperate woodland in the UK. The overall accuracy of pixel-level classification was 91%, while that of ITC-level classification was 61%. The test results demonstrate the effectiveness of the approach, and in particular the use of robust principal component analysis to prune the hyperspectral dataset and reveal subtle difference among species.Department for Environment, Food and Rural AffairsThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/JSTARS.2016.256940

Dynamic spatial task generation for collaborative location-based collecting systems coverage objectives

Collaborative location-based collecting systems (CLCS) are a particular case of collaborative systems where a community of users collaboratively collect geo-referenced data. Each CLCS sets its territory coverage objectives, commonly defined as to guarantee that all the affected territory is surveyed with a particular coverage criterium. This paper presents a three-step pipeline to recommend the subareas that require observations dynamically. The first step generates a disjoint and adjacent set of areas -a mesh- covering the sampling territory. The second step sets a priority and coverage objective for each area. Finally, the third step considers the project’s objectives and the area coverage situation to recommend the areas that need surveys. The output of this last step is an input for a user-task distribution process where the user’s profile is taken into account. Moreover, an example of meshing strategy and task generation is proposed.Facultad de Informátic

Análisis Geológico y Metalogenético del Sector Norte de la Cordillera del Viento, Provincia del Neuquén

Fil: Zappettini, Eduardo O. Servicio Geológico Minero Argentino; Argentina.Fil: Cozzi, Guillermo. Servicio Geológico Minero Argentino; Argentina.Fil: Dalponte, Marcelo. Servicio Geológico Minero Argentino; Argentina.Fil: Godeas, Marta C. Servicio Geológico Minero Argentino; Argentina.Fil: Korzeniewski, Lidia I. Servicio Geológico Minero Argentino; Argentina.Fil: Peroni, Javier. Servicio Geológico Minero Argentino; Argentina.Fil: Segal, Susana. Servicio Geológico Minero Argentino; Argentina.Fil: Castro Godoy, Silvia. Servicio Geológico Minero Argentino; Argentina.El distrito Varvarco, ubicado en el sector norte de la Cordillera del Viento, provincia del Neuquén, comprende mineralizaciones polimetálicas auríferas (mina Santos y manifestaciones Gregorio (Radales 1), Radales 2, Lomas del Trapiche, La Pirita, El Indio y Arroyo Norte), manifestaciones cupro-argentíferas (vetas Silverio 1 y 2), y de hierro (El Fierrillo) y áreas de alteración hidrotermal (Auque Cap, Auque Breccia y Guaraco Norte). Se describen las mineralizaciones en un contexto metalogenético regional, se analiza la mineralogía, las inclusiones fluidas y las condiciones físico-químicas de formación de las asociaciones de alteración hidrotermal. Se destaca la presencia de alteración argílica avanzada caracterizada por la asociación pirofilita-diásporo-corindón. El relevamiento magnetométrico ha permitido analizar la continuidad y geometría de los cuerpos mineralizados. El conjunto de mineralizaciones se vincula con rocas plutónicas y subvolcánicas asignadas al Cretácico superior-Paleógeno (Granodiorita Varvarco, Granito Radales y diques subvolcánicos asociados) del que se brinda información geocronológica, geoquímica y petrológica que confirma su asignación a un magmatismo de arco de margen continental activo, sin señal adakítica. Se presenta un modelo metalogenético local y su vinculación con el marco regional. Los diversos tipos de asociaciones minerales identificadas y las características de las inclusiones fluidas analizadas en las vetas analizadas, sugieren que el conjunto podría vincularse con un sistema tipo pórfiro, si bien la alteración argílica avanzada de baja sulfuración y la presencia de mineralización de hierro (magnetita-hematita) permite plantear la hipótesis de un modelo de exploración del tipo IOCG, más afín con la escasez de azufre del sistema

Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning

Borneo contains some of the world's most biodiverse and carbon-dense tropical forest, but this 750 000 km(2) island has lost 62% of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognizing the ecosystem services they provide, including their ability to store and sequester carbon. Airborne laser scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into statewide assessments of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and composition-ally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rainforests of Sabah on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbon stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalized and effective approach for mapping forest carbon stocks in Borneo and underpins ongoing efforts to safeguard and facilitate the restoration of its unique tropical forests.Peer reviewe