Objects-based Image Analysis for Mapping Natura 2000 Habitats to Improve Forest Management

“…RapidEye, QuickBird) have proven highly useful for mapping Natura 2000 habitat types, such as grasslands (Hernando et al. ; Schmidt et al. ; Buck et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluating an unmanned aerial vehicle‐based approach for assessing habitat extent and condition in fine‐scale early successional mountain mosaics

Gonçalves

Henriques

Alves³

et al. 2015

Applied Vegetation Science

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Nomenclature Castroviejo (1986Castroviejo ( -2013 for published volumes, Franco (1984) and Franco & Afonso (1998) for other groups, except Agrostis (Romero Garc ıa et al. 1988) Abbreviations DSM = digital surface model; EU = European Union; GCP = ground control points; GPS = differential global positioning system; UAV = unmanned aerial vehicle; RF = random forest Abstract Question: Can very high-resolution colour orthophotography and digital surface models (DSMs) from an unmanned aerial vehicle (UAV) be effectively used for assessment of habitat extent and condition in fine-scale disturbancedependent mosaics? Location: Serra de Arga mountain range, a Natura 2000 protected site in the NW region of Portugal where drastic changes in pastoral activities have occurred over recent decades.Methods: An UAV platform was used to collect very high-resolution (6 cm) images and to produce a DSM (10 cm). From these data, several features were extracted related to colour, band ratios, as well as texture features calculated from colour imagery and surface elevation. Based on a systematic sampling design, field data were collected for both training and validation of a supervised classifier. Extracted features and ground truth training data were combined to calibrate a pixel-based Random forest classifier, with the purpose of devising a habitat map for the entire study area. Map validation was performed to assess classification accuracy, and feature importance metrics were calculated.Results: Validation results revealed good mean overall accuracy (0.89), with some performance decrease in situations of high interspersion of habitat types. The priority habitat type 6230* (Nardus grasslands), defining the vegetation matrix of the test site, obtained 0.96 and 0.91, considering, respectively, producer and user accuracy. In turn, priority habitat type 4020* (Atlantic wet heathlands) recorded 0.68 and 0.77. The obtained habitat map allowed measurement of the extent, description of the spatial arrangement and provided an indication of the conservation condition of target habitat types. Test results regarding the discrimination ability of different features highlighted the importance of surface elevation textures derived from the DSM, followed by band ratios textures and other more complex texture features calculated from colour imagery.Conclusions: Overall, the developed methodology showed promising results for assessing the extent and condition of habitats of high conservation priority in fine-scale, dynamic vegetation mosaics. Future advances in the use of UAV platforms may play an important role in monitoring protected sites and fulfil legal reporting obligations of EU member states, while reducing the costs associated with intensive in-field assessments.

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“…Forest biomass is a substantial source of renewable energy, and it is becoming increasingly important for environmental and economic reasons (Straub & Koch 2011). Studies have addressed various aspects of the use of forest residue biomass for energy production and the associated carbon emissions and sequestration (Hernando et al 2012, Anderson & Mitchell 2016. Recent studies have shown that European Union countries underuse their current potential forest biomass production, and those resources are unevenly harvested across Europe (Verkerk et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Some of those characteristics are the stock of detailed tree biomass components and the spatial location of these resources at finer scales (Montero et al 2005, Mauro et al 2016. In this sense, the contributions of remote sensing estimates are becoming a valuable tool (Hernando et al 2012, Anderson & Mitchell 2016 to produce accurate and cost-competitive estimates at the landscape level (Zolkos et al 2013). However, there are some limitations for certain remote sensing techniques, such as satellite-borne optical sensors or synthetic aperture radar, that have shown signal saturation in forest environments with very high biomass density (Cohen & Spies 1992).…”

Section: Introductionmentioning

confidence: 99%

Estimation of forest biomass components using airborne LiDAR and multispectral sensors

Hernando¹,

Puerto²,

Mola‐Yudego³

et al. 2019

iForest

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In order to consider forest biomass as a real alternative for energy production, it is critical to obtain accurate estimates of its availability using non-destructive sampling methods. In this study, we estimate the biomass available in a Scots pine-dominated forest (Pinus sylvestris L.) located in Spain. The biomass estimates were obtained using LiDAR data combined with a multispectral camera and allometric equations. The method used to fuse the data was based on back projection, which assures a perfect match between both datasets. The results present estimates for each of the seven different biomass components: above ground, below ground, log, needles, and large, medium and small branches. The accuracy of the models varied between R 2 values of 0.46 and 0.67 with RMSE% ranging from 15.72% to 35.43% with all component estimates below 20%, except for the model estimating biomass of big branches. The models in this study are suitable for the estimation of biomass and demonstrate that computation is possible at a fine scale for the different biomass components. These remote sensing methods are sufficiently accurate to develop biomass resource cartography for multiple energy uses.

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Objects-based Image Analysis for Mapping Natura 2000 Habitats to Improve Forest Management

Cited by 16 publications

References 17 publications

Key structural forest connectors can be identified by combining landscape spatial pattern and network analyses

Key structural forest connectors can be identified by combining landscape spatial pattern and network analyses

Evaluating an unmanned aerial vehicle‐based approach for assessing habitat extent and condition in fine‐scale early successional mountain mosaics

Estimation of forest biomass components using airborne LiDAR and multispectral sensors

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