Quantification of accuracy in field‐based land cover maps: A new method to separate different components

Haga, Hannah Emma Emilie Strømsrud; Nilsen, Anne-Barbi; Ullerud, Heidrun Asgeirsdatter; Bryn, Anders

doi:10.1111/avsc.12578

Cited by 6 publications

(12 citation statements)

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“…Wetland is often hard to map with remote sensing due to high heterogeneity and low spectral variation between wetland communities (Adam et al., 2010; Amani et al., 2017). In general, wetlands are also challenging to map consistently by field‐surveys (Haga et al., 2021; Ullerud et al., 2018), so low accuracy should be expected. A possible solution for increasing the classification accuracies could be to delineate the class into wetland sub‐classes found in the treeline ecotone such as open fen and bog (Halvorsen et al., 2020), or add other relevant features to the classification.…”

Section: Discussionmentioning

confidence: 99%

“…Imagery acquired from sensors mounted on satellites, aircrafts and unmanned aerial vehicles (UAVs) provide opportunities for land cover mapping over various spatial extents and with various spatial resolutions. Although land covers can be classified through field‐based mapping, remote sensing can provide the basis for a more consistent and cost‐efficient mapping method (Borre et al., 2011; Haga et al., 2021). Land cover mapping through remote sensing has most frequently been carried out using satellite and aerial images because of their capability to cover vast areas (Cihlar, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral‐ and three‐dimensional information from UAV‐based aerial imagery

Mienna

Klanderud

Ørka

et al. 2022

Remote Sens Ecol Conserv

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The alpine treeline ecotone is expected to move upwards in elevation with global warming. Thus, mapping treeline ecotones is crucial in monitoring potential changes. Previous remote sensing studies have focused on the usage of satellites and aircrafts for mapping the treeline ecotone. However, treeline ecotones can be highly heterogenous, and thus the use of imagery with higher spatial resolution should be investigated. We evaluate the potential of using unmanned aerial vehicles (UAVs) for the collection of ultra-high spatial resolution imagery for mapping treeline ecotone land covers. We acquired imagery and field reference data from 32 treeline ecotone sites along a 1100 km latitudinal gradient in Norway (60-69°N). Before classification, we performed a superpixel segmentation of the UAV-derived orthomosaics and assigned land cover classes to segments: rock, water, snow, shadow, wetland, tree-covered area and five classes within the ridge-snowbed gradient. We calculated features providing spectral, textural, three-dimensional vegetation structure, topographical and shape information for the classification. To evaluate the influence of acquisition time during the growing season and geographical variations, we performed four sets of classifications: global, seasonal-based, geographical regional-based and seasonalregional-based. We found no differences in overall accuracy (OA) between the different classifications, and the global model with observations irrespective of data acquisition timing and geographical region had an OA of 73%. When accounting for similarities between closely related classes along the ridgesnowbed gradient, the accuracy increased to 92.6%. We found spectral features related to visible, red-edge and near-infrared bands to be the most important to predict treeline ecotone land cover classes. Our results show that the use of UAVs is efficient in mapping treeline ecotones, and that data can be acquired irrespective of timing within a growing season and geographical region to get accurate land cover maps. This can overcome constraints of a short field-season or low-resolution remote sensing data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral‐ and three‐dimensional information from UAV‐based aerial imagery

Mienna

Klanderud

Ørka

et al. 2022

Remote Sens Ecol Conserv

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“…Furthermore, ALS is commonly used in forest inventories and thus is readily available over vast areas. The difference between observers in vegetation and habitat mapping is a known problem [10,11]. Thus, the pre-information maps seem to be a promising tool to improve delineation and minimize differences between observers.…”

Section: Discussionmentioning

confidence: 99%

“…However, operationally delineating WKH is still based on extensive field inventories and benefits from remote sensing sources only to a limited degree, mainly from orthophotos. The subjectivity and, thus, differences between surveyors in assessing habitats is also a known problem that should be minimized [10,11]. Since remotely sensed data are already available for the forest management inventory, they are also available for WKH inventory; thus, the cost of incorporating these data into inventory protocols is minimal.…”

Section: Introductionmentioning

confidence: 99%

Delineation of Geomorphological Woodland Key Habitats Using Airborne Laser Scanning

et al. 2022

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Forest ecosystems provide a range of services and function as habitats for many species. The concept of woodland key habitats (WKH) is important for biodiversity management in forest planning standards and certification schemes. The main idea of the WKH is to preserve biodiversity hotspots in the forest landscape. Current methods used in delineating WKH rely on costly field inventories. Furthermore, it is well known that the surveyor introduces an error because of the subjective assessment. Remote sensing may reduce this error in a cost-efficient way. The current study develops automated methods using airborne laser scanning (ALS) data to delineate geomorphological WKH, i.e., rock walls and stream gorges. The methods were evaluated based on a complete field inventory of WKH in a 1600 ha area in south-eastern Norway. The delineated WKH showed high detection rates, minor omission errors, but high commissions errors. Combining the delineation into a map of potential WKH suitable to guide field surveyors resulted in detecting all field reference WKH, i.e., a detection rate of 100% and a commission error of 25%. It is concluded that a higher degree of automatization might be possible to improve results and increase the efficiency of WKH inventories.

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“…An inconsistency matrix was finally obtained by subtraction of each element from one. All thematic inconsistency values were converted from proportions to percentages.Mapping-unit pairs, regardless of belonging to the same or different major types or different major-type groups, were characterised by the ecological distance (ED) separating them (seeEriksen et al, 2018;Haga et al, 2021). The data files and R scripts for generating the ED matrix, providing the ED between all combinations of map-ping units are available on GitHub (https://github.com/geco-nhm/ NiN_ecolo gical_distance).…”

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confidence: 99%

What explains inconsistencies in field‐based ecosystem mapping?

Naas

Halvorsen

Horvath

et al. 2023

Applied Vegetation Science

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Questions Field‐based ecosystem mapping is prone to observer bias, typically resulting in a mismatch between maps made by different mappers, that is, inconsistency. Experimental studies testing the influence of site, mapping scale, and differences in experience level on inconsistency in field‐based ecosystem mapping are lacking. Here, we study how inconsistencies in field‐based ecosystem maps depend on these factors. Location Iškoras and Guollemuorsuolu, northeastern Norway, and Landsvik and Lygra, western Norway. Methods In a balanced experiment, four sites were field‐mapped wall‐to‐wall to scales 1:5000 and 1:20,000 by 12 mappers, representing three experience levels. Thematic inconsistency was calculated by overlay analysis of map pairs from the same site, mapped to the same scale. We tested for significant differences between sites, scales, and experience‐level groups. Principal components analysis was used in an analysis of additional map inconsistencies and their relationships with site, scale and differences in experience level and time consumption were analysed with redundancy analysis. Results On average, thematic inconsistency was 51%. The most important predictor for thematic inconsistency, and for all map inconsistencies, was site. Scale and its interaction with site predicted map inconsistencies, but only the latter were important for thematic inconsistency. The only experience‐level group that differed significantly from the mean thematic inconsistency was that of the most experienced mappers, with nine percentage points. Experience had no significant effect on map inconsistency as a whole. Conclusion Thematic inconsistency was high for all but the dominant thematic units, with potentially adverse consequences for mapping ecosystems that are fragmented or have low coverage. Interactions between site and mapping system properties are considered the main reasons why no relationships between scale and thematic inconsistency were observed. More controlled experiments are needed to quantify the effect of other factors on inconsistency in field‐based mapping.

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Quantification of accuracy in field‐based land cover maps: A new method to separate different components

Cited by 6 publications

References 50 publications

Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral‐ and three‐dimensional information from UAV‐based aerial imagery

Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral‐ and three‐dimensional information from UAV‐based aerial imagery

Delineation of Geomorphological Woodland Key Habitats Using Airborne Laser Scanning

What explains inconsistencies in field‐based ecosystem mapping?

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