Monitoring Protocols: Options, Approaches, Implementation, Benefits

Karl, Jason W.; Pyke, David A.

doi:10.1007/978-3-319-46709-2_16

Cited by 9 publications

(4 citation statements)

References 142 publications

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“…For this study, MSC was defined as a key forest indicator primarily because of its relevance, for being informative over large regions (Karl et al 2017), but also because of the large number of countries with available information to estimate it. The proposed shrub indicator, MSC, can be estimated for species from NFI data for all except three of the countries that assess shrub cover.…”

Section: Discussionmentioning

confidence: 99%

Mean species cover: a harmonized indicator of shrub cover for forest inventories

et al. 2018

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Because shrub cover is related to many forest ecosystem functions, it is one of the most relevant variables for describing these communities. Nevertheless, a harmonized indicator of shrub cover for large-scale reporting is lacking. The aims of the study were threefold: to define a shrub indicator that can be used by European countries for harmonized shrub cover estimation using data from their respective national forest inventories (NFIs); to quantify the effects of using different NFI field cover scales; and to establish bridges to facilitate harmonized estimation. Data for shrub species cover from the Third Spanish NFI together with scales for cover assessment from 16 European NFIs were used. The indicator, mean species cover (MSC), was defined for each species and each European forest category. Estimates of MSC calculated using species covers recorded for field plots, with 1% interval widths (MSCobs), were compared with the MSC values that would be obtained for the same data with the different European cover scales (MSCpred). Residuals calculated as differences between MSCobs and MSCpred were analyzed, and a linear mixed model was used as bridging function to adjust predictions and thus further harmonize estimates. Scales with only two or three intervals produced the greatest residuals, while all the other analyzed scales had residuals less than 5%. Most scales, except those most similar to Braun-Blanquet, displayed a tendency to be unreliable for larger covers. The proposed mean species cover indicator provides comparable estimates for shrub communities at large scales. The linear models improved the harmonization of MSC for the scales having two and three intervals. Keywords Vegetation • National forest inventories • Braun-Blanquet cover scale • European forest categories • Bridge function • Linear mixed model Abbreviations EFC European forest categories SNFI-3 Third Spanish national forest inventory NFIs National forest inventories MSC Mean species cover ME Mean error MAE Mean absolute error RMSE Root mean square error

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Section: Discussionmentioning

confidence: 99%

Mean species cover: a harmonized indicator of shrub cover for forest inventories

et al. 2018

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“…Although the HAF method most closely matches the maximum height measurements from previous sage‐grouse habitat studies (see Connelly et al ), there is value in coordinating methods with other monitoring efforts (Karl et al ). The AIM monitoring methods are widely applied across BLM lands and also used by the NRCS National Resources Inventory, National Park Service, and other land management organizations.…”

Section: Discussionmentioning

confidence: 72%

Comparison of 2 vegetation height methods for assessing greater sage‐grouse seasonal habitat

et al. 2018

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The 2015 Sage-Grouse Habitat Assessment Framework (HAF) was developed to evaluate habitat quality for sage-grouse (Centrocercus spp.), with the greater sage-grouse (C. urophasianus) as the primary focus of HAF evaluations and basis of the indicators in the HAF. Site-scale assessments of sagegrouse habitat can be completed using either data collection methods described in the HAF or core methods adopted by the U.S. Bureau of Land Management's Assessment, Inventory, and Monitoring (AIM) program. However, there is a discrepancy in how vegetation height is measured between HAF and AIM methods, which has led to confusion as to which protocol should be used and if the AIM height method is compatible with the HAF for habitat assessments. Our objective was to use simulations and data from multiple study areas to determine how often differences between the 2 methods would result in a different determination of quality for the vegetation-height habitat indicator. We confirmed that the AIM method generally yields lower estimates of height than the HAF method because it estimates mean vegetation height whereas the HAF method estimates mean maximum height (d ¼ 0.031). However, differences between methods at the plot level most often were not substantial enough to lead to a different conclusion about the HAF vegetation-height indicator for habitat quality. There is value in implementing the AIM method because it is widely used for other monitoring purposes, and slight modifications to the AIM technique (i.e., increasing measurement frequency, adding measurements for both grasses and forbs) could improve usefulness for sage-grouse habitat assessments. Ó 2018 This article is a U.S. Government work and is in the public domain in the USA.

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“…When using remotely sensed data, the subject of "ground truthing" is often a consideration. It is common practice to collect ground-based transect data to compare with remote sensing-based vegetation cover estimates, and numerous studies have found approximate equivalence between plant canopy cover measured on the ground and that measured in image data [44,[52][53][54]. In contrast, some studies showed the image-assessed canopy to be substantially different from the ground-assessed canopy [55,56].…”

Section: Comparison Of Map Validation Methodsmentioning

confidence: 99%

Rangeland Brush Estimation Tool (RaBET): An Operational Remote Sensing-Based Application for Quantifying Woody Cover on Western Rangelands

Holifield Collins,

Skirvin,

Kautz

et al. 2023

Remote Sensing

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Much of the western United States is covered by rangelands used for grazing and wildlife. Woody plant cover is increasing in areas historically covered by grasslands and can cause numerous problems, including losses in wildlife habitat, forage for grazing, and overall losses in soil health. Land managers and conservationists are working to control these increases in woody plants, but need tools to help determine target areas to focus efforts and resources where they are most needed. In this work, we present RaBET (Rangeland Brush Estimation Tool), which uses transparent, well-understood methodologies with remotely sensed data to map woody canopy cover across large areas of rangelands. We demonstrate that our process produced more accurate results than two currently available tools based on advanced machine learning techniques. We compare two methods of map validation: traditional field methods of plant canopy measurements; and aircraft-based photography, which decreases the amount of time and resources needed. RaBET is a remote sensing-based application for obtaining repeatable, accurate measures of woody cover to aid land managers and conservationists in the control of woody plants on rangelands.

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Monitoring Protocols: Options, Approaches, Implementation, Benefits

Cited by 9 publications

References 142 publications

Mean species cover: a harmonized indicator of shrub cover for forest inventories

Mean species cover: a harmonized indicator of shrub cover for forest inventories

Comparison of 2 vegetation height methods for assessing greater sage‐grouse seasonal habitat

Rangeland Brush Estimation Tool (RaBET): An Operational Remote Sensing-Based Application for Quantifying Woody Cover on Western Rangelands

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