Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry

Three-dimensional (3D) image mapping of real-world scenarios has a great potential to provide the user with a more accurate scene understanding. This will enable, among others, unsupervised automatic sampling of meaningful material classes from the target area for adaptive semi-supervised deep learning techniques. This path is already being taken by the recent and fast-developing research in computational fields, however, some issues related to computationally expensive processes in the integration of multi-source sensing data remain. Recent studies focused on Earth observation and characterization are enhanced by the proliferation of Unmanned Aerial Vehicles (UAV) and sensors able to capture massive datasets with a high spatial resolution. In this scope, many approaches have been presented for 3D modeling, remote sensing, image processing and mapping, and multi-source data fusion. This survey aims to present a summary of previous work according to the most relevant contributions for the reconstruction and analysis of 3D models of real scenarios using multispectral, thermal and hyperspectral imagery. Surveyed applications are focused on agriculture and forestry since these fields concentrate most applications and are widely studied. Many challenges are currently being overcome by recent methods based on the reconstruction of multi-sensorial 3D scenarios. In parallel, the processing of large image datasets has recently been accelerated by General-Purpose Graphics Processing Unit (GPGPU) approaches that are also summarized in this work. Finally, as a conclusion, some open issues and future research directions are presented.European Commission 1381202-GEU PYC20-RE-005-UJA IEG-2021Junta de Andalucia 1381202-GEU PYC20-RE-005-UJA IEG-2021Instituto de Estudios GiennesesEuropean CommissionSpanish Government UIDB/04033/2020DATI-Digital Agriculture TechnologiesPortuguese Foundation for Science and Technology 1381202-GEU FPU19/0010

A keystone species, European aspen (Populus tremula L.), in boreal forests : Ecological role, knowledge needs and mapping using remote sensing

European aspen (Populus tremula L.) is a keystone species in boreal forests that are dominated by coniferous tree species. Both living and dead aspen trees contribute significantly to the species diversity of forest landscapes. Thus, spatial and temporal continuity of aspen is a prerequisite for the long-term persistence of viable populations of numerous aspen-associated species. In this review, we collate existing knowledge on the ecological role of European aspen, assess the knowledge needs for aspen occurrence patterns and dynamics in boreal forests and discuss the potential of different remote sensing techniques in mapping aspen at various spatiotemporal scales. The role of aspen as a key ecological feature has received significant attention, and studies have recognised the negative effects of modern forest management methods and heavy browsing on aspen occurrence and regeneration. However, the spatial knowledge of occurrence, abundance and temporal dynamics of aspen is scarce and incomprehensive. The remote sensing studies reviewed here highlight particularly the potential of three-dimensional data derived from airborne laser scanning or photogrammetric point clouds and airborne imaging spectroscopy in mapping European aspen, quaking aspen (Populus tremuloides Michx.) and other Populus species. In addition to tree species discrimination, these methods can provide information on biophysical, biochemical properties and even genetic diversity of aspen trees. Major obstacles in aspen detection using remote sensing are the low proportion and scattered occurrence of European aspen in boreal forests and the overlap of spectral and/or structural properties of European aspen and quaking aspen with some other tree species. Furthermore, the suitability of remote sensing data for aspen mapping and monitoring depends on the geographical coverage of data, the availability of multitemporal data and the costs of data acquisition. Our review highlights that integration of ecological knowledge with spatiotemporal information acquired by remote sensing is key to understanding the current and future distribution patterns of aspen-related biodiversity.peerReviewe

Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests.Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras:Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.peerReviewe

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

Drone-based spectral and 3D remote sensing applications for forestry and agriculture

Practising sustainable agriculture and forestry requires information on the state of forests and crops to support management. In precision agriculture, crops are observed in order to treat them precisely in the right place and at the right time, saving both production costs and the environment. Similarly, in forests, information on the composition and state of forest health are crucial to enable their sustainable management. In particular, climate-change-driven insect pests have increased, but economic and ecological losses can be reduced by the right actions if up-to-date and precise information on the health of forests is available. In recent years, drones with cameras have evolved into a flexible way to collect remote sensing data locally. Spectral cameras provide accurate information about the reflection properties of objects, and photogrammetric methods also provide a cost-effective way to collect three-dimensional (3D) data from an object. The objective of this work was to develop and assess drone-based 3D and spectral remote sensing techniques to classify the health status of individual trees and to estimate crop biomass, various biochemical parameters such as nitrogen content, and grass-feeding quality. The work developed a processing chain in which spectral and 3D features were extracted from remote sensing data. Then, combining the features with observations and reference measurements collected from plants, machine learning models were developed for tree health classification and estimation of crop-related parameters. The effects of different factors related to data collection and processing on classification and estimation accuracies were studied in order to generate knowledge on optimal sensors and methods. In general, radiometric corrections, spectral resolution, and the combined use of spectral and 3D features improved classification and estimation accuracies. However, the optimal sensors as well as the data collection and processing methods depend on the different applications and their accuracy requirements. This work was the first to demonstrate the ability of drone hyperspectral data to map the health status of a forest by classifying individual trees infested by bark beetles. The results of the work also showed that drone-based mapping offers a great tool to estimate agricultural crop parameters which can be applied to the optimization of various precision agriculture tasks.Kestävän maa- ja metsätalouden harjoittaminen vaatii tietoa metsien ja viljelykasvien tilasta päätöksenteon tueksi. Täsmämaataloudessa viljelykasveja havainnoidaan, jotta viljelytoimenpiteet voidaan kohdistaa oikeaan paikkaan ja oikea-aikaisesti säästäen sekä tuotantokustannuksia että ympäristöä. Metsissä tieto metsien terveydentilasta on tärkeää, jotta voidaan hillitä metsätuhojen leviämistä. Erityisesti hyönteistuhot ovat lisääntyneet voimakkaasti ilmastonmuutoksen vauhdittamana, mutta taloudellisia ja ekologisia tappiota voidaan vähentää oikeilla toimenpiteillä, jos on olemassa ajantasaisesta tietoa metsien terveydentilasta. Dronet ja niihin asennettavat kamerat ovat kehittyneet viime vuosina joustavaksi tavaksi kerätä kaukokartoitusaineistoa paikallisesti. Spektrikameroilla saadaan tarkkaa tietoa kohteen heijastusominaisuuksista, ja fotogrammetriset menetelmät mahdollistavat myös kustannustehokkaan tavan kerätä kohteesta kolmiulotteista (3D) tietoa. Tämän työn tavoitteena oli kehittää näihin aineistoihin nojautuen kaukokartoitusmenetelmiä yksittäisten puiden terveydentilan luokitteluun sekä viljelykasvien biomassan, erilaisten biokemiallisten parametrien, kuten typpipitoisuuden sekä nurmen ruokintalaadun, kuten D-arvon estimointiin. Työssä kehitettiin prosessointiketju, jossa kaukokartoitusaineistoista irrotettiin spektri- ja 3D-piirteitä, yhdistettiin ne kasveista kerättyihin havaintoihin ja mittauksiin sekä muodostettiin koneoppimismalleja puiden luokittelua ja viljelykasveihin liittyvien parametrien estimointia varten. Työssä verrattiin useiden aineistonkeräykseen ja -prosessointiin liittyvien tekijöiden vaikutuksia luokittelu- ja estimointitulosten tarkkuuteen optimaalisten menetelmien löytämiseksi. Esimerkiksi spektri- ja 3D-piirteiden hyödyntäminen yhdessä sekä radiometriset korjaukset paransivat yleisesti luokittelu- ja estimointitarkkuuksia. Optimaaliset sensorit sekä aineistonkeräys- ja käsittelytavat riippuvat kuitenkin eri sovelluksista ja niiden tarkkuusvaatimuksista. Työssä osoitettiin ensimmäistä kertaa dronesta kerätyn hyperspektrisen aineiston kyvykkyys metsän terveydentilan havainnoinnissa luokittelemalla kuuset kolmeen luokkaan kirjanpainajan aiheuttaman tuhon perusteella. Työn tulokset myös osoittivat drone-pohjaisen kartoituksen kyvyn estimoida erilaisia viljelykasvien parametreja, joita voidaan edelleen soveltaa suunniteltaessa esimerkiksi lisälannoitusta tai säilörehun optimaalista korjuuaikaa

Tree Genera Classification by Ensemble Classification of Small-Footprint Airborne LiDAR

Tree genera information is useful in environmental applications such as forest management, forestry, urban planning, and the maintenance of utility transmission line infrastructure. The ability of small foot print airborne LiDAR (Light Detection and Ranging) to acquire 3D information provides a promising way of studying vertical forest structures. This provides an extra dimension of information compared to the traditional 2D remote sensing data. However, the techniques for processing this type of data are relatively recent and have becoming an innovative research direction. The existing perspective for processing LiDAR data for tree species classification involve calculating the statistics attributes of the vertical point profile for individual trees. This method however does not explicitly utilize the geometric information of the tree form such as shapes of the tree crown and geometric features that are derivable inside of the tree crown. Therefore, the aim of this dissertation research is to derive geometric features from individual tree crowns and use these features for genera classification. The second goal of this research is to improve classification results by combining the newly developed features with the conventional vertical point profile features through ensemble classification system. Final goal of this research is to design a classification system to cope with the situation where the number of classes in the validation data exceeds the number of classes in the training data. 24 geometric features were initially derived and six of them are selected for the classification of pine, poplar and maple. Average classification accuracy of 88.3% is achieved by using this method. When the geometric features are combined with vertical profile features by ensemble classification system, the average classification accuracy increased to 91.2%. While the individual performance of geometric classifier and vertical classifier is 88.0% and 88.8% respectively for the classification of pine, poplar and maple. Lastly, when samples that do not belong to pine, poplar and maple are added to the validation data, the classification accuracy dropped to 72.8% by using randomly selected samples for training. However, through diversified sampling technique, the classification accuracy increased to 93.8%

Sensing Mountains

Sensing mountains by close-range and remote techniques is a challenging task. The 4th edition of the international Innsbruck Summer School of Alpine Research 2022 – Close-range Sensing Techniques in Alpine Terrain brings together early career and experienced scientists from technical-, geo- and environmental-related research fields. The interdisciplinary setting of the summer school creates a creative space for exchanging and learning new concepts and solutions for mapping, monitoring and quantifying mountain environments under ongoing conditions of change