Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index

Korhonen, Lauri; Korpela, Ilkka; Heiskanen, Janne; Maltamo, Matti

doi:10.1016/j.rse.2010.12.011

Cited by 339 publications

(311 citation statements)

References 64 publications

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“…Using LiDAR with similar footprint and point density, as in this study, tree canopy cover can be estimated more accurately than understory cover [25,31].…”

Section: Discussionmentioning

confidence: 95%

“…Estimates of fractional vegetation cover, defined as the projection of the tree crowns onto the ground divided by ground surface area, can be calculated from point ratios in different strata [14]. Most applications of airborne LiDAR to derive fractional cover were focused on tree canopy cover, which can be estimated with high accuracy [25][26][27]. Some attention has also been paid on the estimation of understory cover in forests [28][29][30].…”

Section: Lidardata Acquisitionmentioning

confidence: 99%

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LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer

Ewald

Dupke

Heurich

et al. 2014

Forests

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Abstract:The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the cold period in winter forces individuals to optimize their trade off in searching for food and shelter. We analyzed the winter habitat selection of roe deer (Capreolus capreolus) in a montane forest landscape combining estimates of vegetation cover in three different height strata, derived from high resolution airborne Laser-scanning (LiDAR, Light detection and ranging), and activity data from GPS telemetry. Specifically, we tested the influence of temperature, snow height, and wind speed on site selection, differentiating between active and resting animals using mixed-effects conditional logistic regression models in a case-control design. Site selection was best explained by temperature deviations from hourly means, snow height, and activity status of the animals. Roe deer tended to use forests of high canopy cover more frequently with decreasing temperature, and when snow height exceeded 0.6 m. Active animals preferred lower canopy cover, but higher understory cover. Our approach demonstrates the OPEN ACCESSForests 2014, 5 1375 potential of LiDAR measures for studying fine scale habitat selection in complex three-dimensional habitats, such as forests.

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“…Using LiDAR with similar footprint and point density, as in this study, tree canopy cover can be estimated more accurately than understory cover [25,31].…”

Section: Discussionmentioning

confidence: 95%

Section: Lidardata Acquisitionmentioning

confidence: 99%

LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer

Ewald

Dupke

Heurich

et al. 2014

Forests

View full text Add to dashboard Cite

show abstract

“…This parameter can be easily translated to LiDAR data by dividing the number of returns measured above a certain height threshold by the total number of returns. Many studies have shown a strong (R 2 > 0.7) relationship between this LiDAR-metric and ground measurements [120]. By using hemispherical images or LAI-2000 sensor data for calibration, LAI and solar radiation can also be derived from LiDAR data with a high precision over large areas [108,121].…”

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 96%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

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“…Tree cover is estimated from HRSI by visual analysis [33] or by semi-automated object-oriented methods [1,[42][43][44]. From discrete, small-footprint LiDAR returns, tree cover is calculated by dividing the number of returns above a criterion height by the total number of returns within a given sample [45,46]. In accordance with the International Geosphere-Biosphere definition of forests, Sexton et al specified the height criterion as 5 m, but this parameter may be tuned to biome-specific definitions of tree height [34,47].…”

Section: Assessing Spaceborne Maps Of Tree Cover Across the Ttementioning

confidence: 99%

Calibration and Validation of Landsat Tree Cover in the Taiga−Tundra Ecotone

et al. 2016

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Abstract:Monitoring current forest characteristics in the taiga´tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover >80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.

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Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index

Cited by 339 publications

References 64 publications

LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer

LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Calibration and Validation of Landsat Tree Cover in the Taiga−Tundra Ecotone

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