A questionnaire-based review of the operational use of remotely sensed data by national forest inventories

Barrett, Frank J.; McRoberts, Ronald E.; Tomppo, Erkki; Cienciala, Emil; Waser‬, Lars T.

doi:10.1016/j.rse.2015.08.029

Cited by 92 publications

(71 citation statements)

References 41 publications

(42 reference statements)

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“…Forest inventories provide a precise statistical estimation of forest composition for large areas, but spatially explicit information beyond inventory domains is missing [5]. Thus, maps of broadleaved and coniferous trees are an important source of information for assessing woodland resources and an additional valuable output of National Forest Inventories (NFI) [6].…”

Section: Introductionmentioning

confidence: 99%

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

2017

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Abstract:Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km 2 that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of −3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector.

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Section: Introductionmentioning

confidence: 99%

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

2017

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“…In the second phase (the terrestrial survey), all plots that are classified as "forest", and the remaining "non-forest" plots which are closer than 5 m to an existing forest border, are visited. The NFI field teams follow a strict protocol [38] to make the final decision to Forests 2015, 6,[4510][4511][4512][4513][4514][4515][4516][4517][4518][4519][4520][4521][4522][4523][4524][4525][4526][4527][4528] classify these plots as either "forest" or "non-forest" (each visited plot consists of two concentric circles with a radius of 8 m and 12.6 m, respectively).…”

Section: Reference Datamentioning

confidence: 99%

“…However, both criteria are handled differently by many NFIs. Minimum width is more frequently used to Forests 2015, 6,[4510][4511][4512][4513][4514][4515][4516][4517][4518][4519][4520][4521][4522][4523][4524][4525][4526][4527][4528] separate areas of narrow tree elements-which do not fulfill the minimum requirements of the forest definition-from clearly defined forest areas [4].…”

Section: Land Cover Criteria Of the Swiss Nfimentioning

confidence: 99%

“…Considering plots with a distance of greater than or equal to 70 m from the forest border leads to an OA of greater than 0.9. Above this threshold, the continuous increase of Forests 2015, 6,[4510][4511][4512][4513][4514][4515][4516][4517][4518][4519][4520][4521][4522][4523][4524][4525][4526][4527][4528] OA is much less marked. When assessing the altitude classes independently differences between the classes are apparent.…”

Section: Comparison With Nfi Sample Plot Datamentioning

confidence: 99%

“…New perspectives for wall-to-wall forest mapping are emerging through digital aerial stereo-images, which are likely-in contrast to ALS-updated more regularly by national or regional mapping agencies in several countries [6]. The possibility of extending the spatially and temporarily limited NFI plot information to a continuous landscape-level representation-such as wall-to-wall forest cover maps-has now become more feasible.…”

Section: Introductionmentioning

confidence: 99%

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Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition

Waser‬

Fischer

Wang

et al. 2015

Forests

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Abstract:Forest mapping is an important source of information for assessing woodland resources and a key issue for any National Forest Inventory (NFI). In the present study, a detailed wall-to-wall forest cover map was generated for all of Switzerland, which meets the requirement of the Swiss NFI forest definition. The workflow is highly automated and based on digital surface models from image-based point clouds of airborne digital sensor data. It fully takes into account the four key criteria of minimum tree height, crown coverage, width, and land use. The forest cover map was validated using almost 10,000 terrestrial and stereo-interpreted NFI plots, which verified 97% agreement overall. This validation implies different categories such as five production regions, altitude, tree type, and distance to the forest border. Overall accuracy was lower at forest borders but increased with increasing distance from the forest border. Commission errors remained stable at around 10%, but increased to 17.6% at the upper tree line. Omission errors were low at 1%-10%, but also increased with altitude and mainly occurred at the upper tree line (19.7%). The main reasons for this are the lower image quality and the NFI height definition for forest which apparently excludes shrub forest from the mask. The presented forest mapping approach is superior to existing products due to its national coverage, high level of detail, regular updating, and implementation of the land use criteria.

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