Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data

We have developed a tree-based approach to create spatially continuous forest stand mean height maps across China through integrating high-point density, high-precision close-range LiDAR data and multisource remote sensing data. The accuracy analysis of the arithmetic mean height (Ha) and the weighted mean height (Hw) demonstrates the feasibility of the proposed method. A practical framework for forestry investigation based on close-range LiDAR was proposed. The mean values of Ha and Hw are 13.3 ± 3.3 m 11.3 ± 2.9 m on pixel level, respectively. Validation based on LiDAR and field sample data shows that the RMSE values, range from 2.6 to 4.1 m for Ha and  2.9 to 4.3 m for Hw, respectively, indicating that our approach outperforms existing forest canopy height maps derived from area-based approaches. Hopefully, our methods and maps will serve as a foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability.

opencc-by-4.0Jun 2019View details →

Above-ground carbon density derived from LiDAR data over oil palm plantations in Malaysian Borneo, 2014

Description: The work was carried out in the oil palm plantations within the Stability of Altered Forest Ecosystem (SAFE) Project, located within lowland dipterocarp forest regions of East Sabah in Malaysian Borneo. Airborne LiDAR data were acquired on 5 November 2014 using a Leica LiDAR50-II flown at 1850 m altitude on a Dornier 228-201 travelling at 135 knots. The LiDAR sensor emitted pulses at 83.1 Hz with a field of view of 12.0°, and a footprint of about 40 cm diameter. The average pulse density was 7.3/m2. The Leica LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. The LiDAR data were pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools (Rapidlasso GmbH, LAStools). Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells. Project: This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/25">Influences of disturbance and environmental variation on biomass change in Malaysian Borneo</a>Funding: These data were collected as part of research funded by: <ul><li>NERC (Standard grant, JKM/MBS.1000-2/2 JLD.3 (128))</li></ul>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.Permits: These data were collected under permit from the following authorities:<ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.3 (128))</li></ul>XML metadata: GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247699">here</a>Files: This consists of 1 file: LiDAR_Aboveground_Carbon.xlsxLiDAR_Aboveground_Carbon.xlsxThis file contains dataset metadata and 1 data tables:<ol><li>LiDAR aboveground carbon in Oil palm plantations (described in worksheet LiDAR_ Aboveground_Carbon)Description: The output of a LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. The LiDAR data was pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools (Rapidlasso GmbH, LAStools). Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells. Number of fields: 35Number of data rows: 27Fields: <ul><li>Year: Year the oil palm trees were planted (Field type: Numeric)</li><li>Plot: Plot number based on the SAFE project framework. Each plot is 25 metres x 25 metres size or 0.0625 hectares (Field type: Location)</li><li>meanH: Average tree height per plot (Field type: Numeric)</li><li>TreeN_plot: Number of trees per plot (Field type: Numeric)</li><li>TreeN_ha: Number of trees per hectare obtained by upscaling the number of trees within each 25m x 25m (0.0625 ha) plot to 1 ha (Field type: Numeric)</li><li>ACD_plot: Sum of the aboveground carbon density per plot (Field type: Numeric)</li><li>ACD_ha: Sum of the aboveground carbon density per hectare obtained by upscaling the number of aboveground carbon density within each 25m x 25m (0.0625 ha) plot to 1 ha (Field type: Numeric)</li><li>CC1: Average canopy cover per hectare: the proportion of area occupied by crowns at 1 metre height (Field type: Numeric)</li><li>CC2: Average canopy cover per hectare: the proportion of area occupied by crowns at 2 metres height (Field type: Numeric)</li><li>CC3: Average canopy cover per hectare: the proportion of area occupied by crowns at 3 metres height (Field type: Numeric)</li><li>CC4: Average canopy cover per hectare: the proportion of area occupied by crowns at 4 metres height (Field type: Numeric)</li><li>CC5: Average canopy cover per hectare: the proportion of area occupied by crowns at 5 metres height (Field type: Numeric)</li><li>CC6: Average canopy cover per hectare: the proportion of area occupied by crowns at 6 metres height (Field type: Numeric)</li><li>CC7: Average canopy cover per hectare: the proportion of area occupied by crowns at 7 metres height (Field type: Numeric)</li><li>CC8: Average canopy cover per hectare: the proportion of area occupied by crowns at 8 metres height (Field type: Numeric)</li><li>CC9: Average canopy cover per hectare: the proportion of area occupied by crowns at 9 metres height (Field type: Numeric)</li><li>CC10: Average canopy cover per hectare: the proportion of area occupied by crowns at 10 metres height (Field type: Numeric)</li><li>CC11: Average canopy cover per hectare: the proportion of area occupied by crowns at 11 metres height (Field type: Numeric)</li><li>CC12: Average canopy cover per hectare: the proportion of area occupied by crowns at 12 metres height (Field type: Numeric)</li><li>CC13: Average canopy cover per hectare: the proportion of area occupied by crowns at 13 metres height (Field type: Numeric)</li><li>CC14: Average canopy cover per hectare: the proportion of area occupied by crowns at 14 metres height (Field type: Numeric)</li><li>CC15: Average canopy cover per hectare: the proportion of area occupied by crowns at 15 metres height (Field type: Numeric)</li><li>CC16: Average canopy cover per hectare: the proportion of area occupied by crowns at 16 metres height (Field type: Numeric)</li><li>CC17: Average canopy cover per hectare: the proportion of area occupied by crowns at 17 metres height (Field type: Numeric)</li><li>CC18: Average canopy cover per hectare: the proportion of area occupied by crowns at 18 metres height (Field type: Numeric)</li><li>CC19: Average canopy cover per hectare: the proportion of area occupied by crowns at 19 metres height (Field type: Numeric)</li><li>CC20: Average canopy cover per hectare: the proportion of area occupied by crowns at 20 metres height (Field type: Numeric)</li><li>CC21: Average canopy cover per hectare: the proportion of area occupied by crowns at 21 metres height (Field type: Numeric)</li><li>CC22: Average canopy cover per hectare: the proportion of area occupied by crowns at 22 metres height (Field type: Numeric)</li><li>CC23: Average canopy cover per hectare: the proportion of area occupied by crowns at 23 metres height (Field type: Numeric)</li><li>TCH: Top of canopy height: mean height of Canopy Height Model (CHM) pixels per hectare. (Field type: Numeric)</li><li>TreeN_itc: Number of segmented trees per hectare obtained by using the itcSegment function implemented in R (Field type: Numeric)</li><li>meanH_itc: Average tree height per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li><li>meanHc_itc: Corrected average tree height per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li><li>ACDc_itc: Sum of the aboveground carbon density per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li></ul></li></ol>Date range: 2014-11-05 to 2014-11-05Latitudinal extent: 4.5000 to 5.0700Longitudinal extent: 116.7500 to 117.8200

opencc-by-4.0Aug 2024View details →

Aerosol products presented in "ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications"

ALICENET output products on aerosol optical and physical properties and vertical layering presented in “Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: Alicenet – An Italian network of Automated Lidar-Ceilometers for 4D aerosol monitoring: infrastructure, data processing, and applications, AMT, https://doi.org/10.5194/egusphere-2024-730, 2024”. The aod*.txt files include the following information: - date: date in UTC - AOD_ALICENET: AOD as retrieved by ALICENET at 1064 nm - AOD_AERONET/SKYNET: AOD measured by a co-located photometer from AERONET/SKYNET (level 2) at 1020 nm - AE: Angstrom Exponent from AERONET/SKYNET (level 2) The contiunous.aerosol.layer.rome.txt file includes the following information: - date: date in CET - continuous_aerosol_layer: Continous Aerosol Layer heights as retrieved by ALICENET The mixed.aerosol.layer.rome.txt file includes the following information: - date: date in CET - mixed_aerosol_layer: Mixed Aerosol Layer heights as retrieved by ALICENET This work received partial financial support from the EC H2020 Project RI-URBANS (GA No 101036245), and benefited from work done within the Action PROBE (CA18235), supported by COST (European Cooperation in Science and Technology).

opencc-by-4.0Aug 2024View details →

MiniMPL data for 'Supercooled liquid water cloud classification using lidar backscatter peak properties'

This depository contains MiniMPL data collected in Christchurch, New Zealand from May 2021 to December 2022 for 'Supercooled liquid water cloud classification using lidar backscatter peak properties' by Whitehead et al. (2024). The dataset contains: <ul> <li> MiniMPL data processed with the Automatic Lidar and Ceilometer Framework (ALCF; Kuma et al., 2021)</li> <li>Reference cloud phase mask</li> <li>G22-Christchurch model-generated cloud phase mask</li> <li>Figures comparing the G22-Davis and G22-Christchurch masks to the reference mask</li> </ul>

opencc-by-4.0Oct 2024View details →

PollyXT and COSMO-MUSCAT data for "Investigating the link between mineral dust hematite content and intensive optical properties by means of lidar measurements and aerosol modelling"

The dataset contains 4 different files:  <ul> <li>For the single case example on the 24 August 2021 between 2:45 to 5:27 UTC in Mndelo, Cabo Verde: <ul> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth-info.txt : contains the information of the vertically retrieved optical properties from PollyXT lidar measurements. The information contained refers to the chosen retrieval times, vertical smoothing, and reference heights</li> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth.txt : vertically retrieved optical properties per height.</li> <li>-Mindelo-model_24aug.csv : COSMO-MUSCAT vertical results of dust and mineral mass concentrations per height. The columns that end with "int mass" correspond to the integrated mass per dust layer and columns that end with numbers correspond to different size bins. For reference to the size bins see Table 1 in Gómez Maqueo Anaya et al., 2024</li> </ul> </li> <li>Mutiple case studies: <ul> <li>-Mindelo-lidar-uvvisdiff_model.csv : Twenty-two case studies with the following order: first, the mean values of the lidar-derived optical properties, along with their corresponding retrieval times and heights that define the dust plume. This is followed by the POLIPHON (Mamouri and Ansmann, 2014, 2017) data. The mean values from dust and mineral mass concentrations from the model start with the model heights where the dust plumes were calculated. At the end of the dataset rows, the times from which the modeled mean values are calculated can be found.</li> </ul> </li> </ul>

opencc-by-4.0Aug 2021View details →

Data from dissertation: 'Landscape and Aboveground Biomass Dynamics of Brazilian Savanna using airborne LIDAR and MapBiomas datasets : case study of Rio Vermelho Watershed, Brazil'

This dissertation was submitted to University of Manchester as part of MSc GIS program This repository contains: 1) Contains the R language code used in the dissertation (CHM_&_LiDAR_metric.R; Landscape_metric.R; Generalized_Linear_Model.R; Random_Forest_Model.R). 2) Canopy Height Model (CHM) and 56 LiDAR metric raster files with a resolution of 1m (CHM_&_LiDAR_metric_2014.zip; CHM_&_LiDAR_metric_2018.zip), the original LiDAR data come from Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, USAID, and the US Department of State. 3) AGB raster files with a resolution of 10m (AGB_2014.tif; AGB_2018.tif; AGB_dynamic.tif), field plots used for AGB estimation come from Sabrinado Couto de Miranda from University of Goiás State (UEG), Brazil, and her team. 4) Landscape metric interpolation raster file (SHDI.tif; SHEI.tif; AREA_CV.tif; CIRCLE_MN.tif; SHAPE_MN.tif) with a resolution of 10m, land cover map Map come from Biomas team for landscape metric calculation.

opencc-zeroSep 2021View details →

Data for: Characterizing individual tree-level snags using airborne lidar-derived forest canopy gaps within closed-canopy conifer forests

1. Airborne lidar is often used to calculate forest metrics about trees but it may also provide a wealth of information about the space between trees. Forest canopy gaps are defined by the absence of vegetative structure and serve important roles for wildlife, such as facilitating animal movement. Forest canopy gaps also occur around snags, keystone structures that provide important substrates to wildlife species for breeding, roosting, and foraging. 2. We wanted to test a method for quantifying canopy gaps around individual snags and live trees, with the working hypothesis that snags would have more gaps surrounding them overall than live trees. We evaluated canopy gaps around individual snags (n=270) and live trees (n=2186) and evaluated correlations between canopy structure and snag occurrence in dense conifer stands of the Idaho Panhandle National Forest, USA. We paired airborne lidar with ground reference data collected at fixed-radius plots (n=53) to evaluate local gap structure. The R package ForestGapR was used to quantify canopy gaps throughout the canopy to determine where the differences were greatest. A canopy space profile was created for each tree by mapping gaps (a) vertically every 2 m in height (2–50 m above ground), and (b) horizontally across small (16 m2), medium (36 m2), and large (64 m2) footprint sizes. 3. Our results suggest this method is robust for quantifying canopy gaps around individual trees. The canopy space profiles were distinctly different for snags and live trees, with more canopy gaps within the area surrounding snags relative to live trees. The greatest differences occurred at mid-canopy heights (~20 m above ground) and at the smallest footprint size (16 m2). 4. These results show potential to improve understanding of gap dynamics in closed-canopy conifer forests, and we suggest snag modeling could be improved by incorporating lidar-derived canopy gap analyses alongside existing methodologies.

opencc-by-4.0Dec 2018View details →

Data Set: Hyperspectral image unmixing with LiDAR data-aided spatial regularization

Data set and matlab codes used for the experimental section of "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization" T. Uezato, M. Fauvel and N. Dobigeon, "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization," in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 7, pp. 4098-4108, July 2018. doi: 10.1109/TGRS.2018.2823419 URL: <a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475</a>

CLAMPS2 Doppler Lidar VAD Data

These files contain 24 hour periods of data collected from the CLAMPS2 Halo Streamline XR+ Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the SPLASH project.

CLAMPS2 Doppler Lidar Vertical Stare Data

These files contain 24 hour periods of data collected from the CLAMPS2 Halo Streamline XR+ Doppler lidar. While not conducting other scans, the lidar directs the beam to zenith, allowing for the measurement of vertical velocity. These data were collected during the SPLASH project.

GEDI and ALS LiDAR data for the Upper Austrian national park "Kalkalpen"

This dataset contains GEDI L1B,L2B,L2A data for the Upper Austrian national park "Kalkalpen". Also included are the obtained ALS return pulses for the Kalkalpen. The GEDI waveforms are averaged to 1m vertical height layers. Afterwards, the mean noise level stored in GEDI L1B dataset is subtracted from each waveform The ALS pulses are collocated to the GEDI waveforms (x/y-shift = +- 10m).

opencc-zeroNov 2022View details →

Data for: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR

Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics' Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1400 kg/h. When emission rate estimates were generated using wind from High-Resolution Rapid Refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger's stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), accuracy of individual emission rate estimates produced using HRRR wind ranged from -64.1% to 87.0%. Across all controlled releases 38.1% of estimates had error within +/- 20%, and 87.3% of measurements were within a factor of two (-50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h) emission estimates are biased high; however, when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications which must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models, or to audit reported emission levels from oil and gas systems.

Archaeological LiDAR data, Kostanjevica na Krasu (Slovenia). Companion data to article "Executable Map Paper (EMaP) for archaeological LiDAR"

Archaeological LiDAR data, Kostanjevica na Krasu (Slovenia) is a supplement to the article "Executable Map Paper (EMaP) for archaeological LiDAR". In the future we also intend to publish an archaeological interpretation of the same data (current working title: "An Archaeological Interpretation of Airborne LiDAR Data. Case study from Kostanjevica na Krasu (Slovenia)"). All relevant para- and metadata are available in the listed publications. The deposited data are intended for use in a GIS system and consist of: 0.5m DFM (GeoTIFF with TFW) Sky view visualisation of the same DFM (GeoTIFF) Archaeological features - points ( Shape file) Archaeological features - lines (Shape file)

opencc-zeroJan 2023View details →

Data for: Spatial monitoring of flying insects over a Swedish lake using a CW lidar system

Data for a field experiment on remote sensing of flying insects are supplied. A bistatic CW lidar system of the Scheimpflug type was employed, and echoes from flying insects over and close to a Swedish lake were recorded as read-out files from an array detector making observations along the emitted laser beam. The activated pixels of the detector could be converted into range data by triangulation. With a high detector read-out frequency, data on numerous insects could be obtained. Special emphasis was put on distinguishing between large and small insects (from the intensities of the recorded echoes) and the insect positions with regard to the shores of the lake.

opencc-by-4.0Apr 2023View details →

Using a low-cost 2D LiDAR Sensor to capture 3D Data - Raw Data

Raw Data for an upcoming publication in the MDPI Journal of Sensors, titled: "Using a low-cost 2D LiDAR Sensor to capture 3D Data"

Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018

Forest cover and density metrics (RDCC) extracted from 2018 Lidar in the Florida panhandle Leon. These 5m horizontal resolution rasters consist of 28 bands of forest cover and density metrics created using the lidR R software package from 2018 Lidar data. The 28 bands are listed in the following table:  <table> <tbody> <tr> <td> RDCC Band </td> <td> lidR short name </td> <td> Metric Description </td> </tr> <tr> <td> Band 1 </td> <td> Num_Returns </td> <td> Total number of returns in cell </td> </tr> <tr> <td> Band 2  </td> <td> Num_GrndRet </td> <td> Number of ground returns in cell </td> </tr> <tr> <td> Band 3 </td> <td> Num_1stRet </td> <td> Number of first returns in cell </td> </tr> <tr> <td> Band 4 </td> <td> Grnd_Elev </td> <td> Ground Elevations (above geoid, etc) </td> </tr> <tr> <td> Band 5 </td> <td> Mn_RH </td> <td> Mean of all Relative Heights </td> </tr> <tr> <td> Band 6 </td> <td> SD_RH </td> <td> Std. Dev of all Relative heights </td> </tr> <tr> <td> Band 7 </td> <td> RHt_95th </td> <td> Relative Height 95% </td> </tr> <tr> <td> Band 8 </td> <td> RHt_90th </td> <td> Relative Height 90% </td> </tr> <tr> <td> Band 9 </td> <td> RHt_75th </td> <td> Relative Height 75% </td> </tr> <tr> <td> Band 10 </td> <td> RHt_50th </td> <td> Relative Height 50% </td> </tr> <tr> <td> Band 11 </td> <td> RHt_25th </td> <td> Relative Height 25% </td> </tr> <tr> <td> Band 12 </td> <td> RHt_10th </td> <td> Relative Height 10% </td> </tr> <tr> <td> Band 13 </td> <td> RHt_05th </td> <td> Relative Height  5% </td> </tr> <tr> <td> Band 14 </td> <td> RD_2to10ft </td> <td> Relative Density 2 to 10 ft (Shrubs) </td> </tr> <tr> <td> Band 15 </td> <td> RD_10to20ft </td> <td> Relative Density 10 to 20 ft (High shrub/low midstory) </td> </tr> <tr> <td> Band 16 </td> <td> RD_20to49ft </td> <td> Relative Density 20 to 49 ft (High midstory) </td> </tr> <tr> <td> Band 17 </td> <td> RD_gt2ft </td> <td> Relative Density all returns gt 2 ft (for comp w/ CC) </td> </tr> <tr> <td> Band 18 </td> <td> RD_gt10ft </td> <td> Relative Density all returns gt 10 ft (for comp w/ CC) </td> </tr> <tr> <td> Band 19 </td> <td> RD_gt20ft </td> <td> Relative Density all returns gt 20 ft (for comp w/ CC) </td> </tr> <tr> <td> Band 20 </td> <td> RD_gt49ft </td> <td> Relative Density greater than 49 ft (Canopy) </td> </tr> <tr> <td> Band 21 </td> <td> CC_gt2ft </td> <td> Canopy Cover gt 2 ft (based only on first returns) </td> </tr> <tr> <td> Band 22 </td> <td> CC_gt10ft </td> <td> Canopy Cover gt 10 ft (based only on first returns) </td> </tr> <tr> <td> Band 23 </td> <td> CC_gt20ft </td> <td> Canopy Cover gt 20 ft (based only on first returns) </td> </tr> <tr> <td> Band 24 </td> <td> CC_gt49ft </td> <td> Canopy Cover gt 49 ft (based only on first returns) </td> </tr> <tr> <td> Band 25 </td> <td> MnRHgt2ft </td> <td> Mean of all relative heights gt 2 ft (includes shrubs, midstory and upper canopy) </td> </tr> <tr> <td> Band 26 </td> <td> MnRHgt10ft </td> <td> Mean of all relative heights gt 10 ft (includes all midstory and upper canopy) </td> </tr> <tr> <td> Band 27 </td> <td> MnRHgt20ft </td> <td> Mean of all relative heights gt 20 ft (includes high midstory and upper canopy) </td> </tr> <tr> <td> Band 28 </td> <td> MnRHgt49ft </td> <td> Mean of all relative heights gt 49 ft (includes upper canopy) </td> </tr> </tbody> </table> Full descriptions of the creation of these raster outputs is available in the companion publication - https://www.mdpi.com/2072-4292/13/23/4763

Relative Density Canopy Cover Outputs for Choctawhatchee Lidar data in the Florida Panhandle 2018

Forest cover and density metrics (RDCC) extracted from 2018 Lidar in the Florida panhandle Choctawhatchee. These 5m horizontal resolution rasters consist of 28 bands of forest cover and density metrics created using the lidR R software package from 2018 Lidar data. The 28 bands are listed in the following table:  <table> <tbody> <tr> <td> RDCC Band </td> <td> lidR short name </td> <td> Metric Description </td> </tr> <tr> <td> Band 1 </td> <td> Num_Returns </td> <td> Total number of returns in cell </td> </tr> <tr> <td> Band 2  </td> <td> Num_GrndRet </td> <td> Number of ground returns in cell </td> </tr> <tr> <td> Band 3 </td> <td> Num_1stRet </td> <td> Number of first returns in cell </td> </tr> <tr> <td> Band 4 </td> <td> Grnd_Elev </td> <td> Ground Elevations (above geoid, etc) </td> </tr> <tr> <td> Band 5 </td> <td> Mn_RH </td> <td> Mean of all Relative Heights </td> </tr> <tr> <td> Band 6 </td> <td> SD_RH </td> <td> Std. Dev of all Relative heights </td> </tr> <tr> <td> Band 7 </td> <td> RHt_95th </td> <td> Relative Height 95% </td> </tr> <tr> <td> Band 8 </td> <td> RHt_90th </td> <td> Relative Height 90% </td> </tr> <tr> <td> Band 9 </td> <td> RHt_75th </td> <td> Relative Height 75% </td> </tr> <tr> <td> Band 10 </td> <td> RHt_50th </td> <td> Relative Height 50% </td> </tr> <tr> <td> Band 11 </td> <td> RHt_25th </td> <td> Relative Height 25% </td> </tr> <tr> <td> Band 12 </td> <td> RHt_10th </td> <td> Relative Height 10% </td> </tr> <tr> <td> Band 13 </td> <td> RHt_05th </td> <td> Relative Height  5% </td> </tr> <tr> <td> Band 14 </td> <td> RD_2to10ft </td> <td> Relative Density 2 to 10 ft (Shrubs) </td> </tr> <tr> <td> Band 15 </td> <td> RD_10to20ft </td> <td> Relative Density 10 to 20 ft (High shrub/low midstory) </td> </tr> <tr> <td> Band 16 </td> <td> RD_20to49ft </td> <td> Relative Density 20 to 49 ft (High midstory) </td> </tr> <tr> <td> Band 17 </td> <td> RD_gt2ft </td> <td> Relative Density all returns gt 2 ft (for comp w/ CC) </td> </tr> <tr> <td> Band 18 </td> <td> RD_gt10ft </td> <td> Relative Density all returns gt 10 ft (for comp w/ CC) </td> </tr> <tr> <td> Band 19 </td> <td> RD_gt20ft </td> <td> Relative Density all returns gt 20 ft (for comp w/ CC) </td> </tr> <tr> <td> Band 20 </td> <td> RD_gt49ft </td> <td> Relative Density greater than 49 ft (Canopy) </td> </tr> <tr> <td> Band 21 </td> <td> CC_gt2ft </td> <td> Canopy Cover gt 2 ft (based only on first returns) </td> </tr> <tr> <td> Band 22 </td> <td> CC_gt10ft </td> <td> Canopy Cover gt 10 ft (based only on first returns) </td> </tr> <tr> <td> Band 23 </td> <td> CC_gt20ft </td> <td> Canopy Cover gt 20 ft (based only on first returns) </td> </tr> <tr> <td> Band 24 </td> <td> CC_gt49ft </td> <td> Canopy Cover gt 49 ft (based only on first returns) </td> </tr> <tr> <td> Band 25 </td> <td> MnRHgt2ft </td> <td> Mean of all relative heights gt 2 ft (includes shrubs, midstory and upper canopy) </td> </tr> <tr> <td> Band 26 </td> <td> MnRHgt10ft </td> <td> Mean of all relative heights gt 10 ft (includes all midstory and upper canopy) </td> </tr> <tr> <td> Band 27 </td> <td> MnRHgt20ft </td> <td> Mean of all relative heights gt 20 ft (includes high midstory and upper canopy) </td> </tr> <tr> <td> Band 28 </td> <td> MnRHgt49ft </td> <td> Mean of all relative heights gt 49 ft (includes upper canopy) </td> </tr> </tbody> </table> Full descriptions of the creation of these raster outputs is available in the companion publication - https://www.mdpi.com/2072-4292/13/23/4763

Relative Density Canopy Cover Outputs for Block3 Lidar data in the Florida Panhandle 2018

Forest cover and density metrics (RDCC) extracted from 2018 Lidar in the Florida panhandle Block 3. These 5m horizontal resolution rasters consist of 28 bands of forest cover and density metrics created using the lidR R software package from 2018 Lidar data. The 28 bands are listed in the following table:  <table> <tbody> <tr> <td> RDCC Band </td> <td> lidR short name </td> <td> Metric Description </td> </tr> <tr> <td> Band 1 </td> <td> Num_Returns </td> <td> Total number of returns in cell </td> </tr> <tr> <td> Band 2  </td> <td> Num_GrndRet </td> <td> Number of ground returns in cell </td> </tr> <tr> <td> Band 3 </td> <td> Num_1stRet </td> <td> Number of first returns in cell </td> </tr> <tr> <td> Band 4 </td> <td> Grnd_Elev </td> <td> Ground Elevations (above geoid, etc) </td> </tr> <tr> <td> Band 5 </td> <td> Mn_RH </td> <td> Mean of all Relative Heights </td> </tr> <tr> <td> Band 6 </td> <td> SD_RH </td> <td> Std. Dev of all Relative heights </td> </tr> <tr> <td> Band 7 </td> <td> RHt_95th </td> <td> Relative Height 95% </td> </tr> <tr> <td> Band 8 </td> <td> RHt_90th </td> <td> Relative Height 90% </td> </tr> <tr> <td> Band 9 </td> <td> RHt_75th </td> <td> Relative Height 75% </td> </tr> <tr> <td> Band 10 </td> <td> RHt_50th </td> <td> Relative Height 50% </td> </tr> <tr> <td> Band 11 </td> <td> RHt_25th </td> <td> Relative Height 25% </td> </tr> <tr> <td> Band 12 </td> <td> RHt_10th </td> <td> Relative Height 10% </td> </tr> <tr> <td> Band 13 </td> <td> RHt_05th </td> <td> Relative Height  5% </td> </tr> <tr> <td> Band 14 </td> <td> RD_2to10ft </td> <td> Relative Density 2 to 10 ft (Shrubs) </td> </tr> <tr> <td> Band 15 </td> <td> RD_10to20ft </td> <td> Relative Density 10 to 20 ft (High shrub/low midstory) </td> </tr> <tr> <td> Band 16 </td> <td> RD_20to49ft </td> <td> Relative Density 20 to 49 ft (High midstory) </td> </tr> <tr> <td> Band 17 </td> <td> RD_gt2ft </td> <td> Relative Density all returns gt 2 ft (for comp w/ CC) </td> </tr> <tr> <td> Band 18 </td> <td> RD_gt10ft </td> <td> Relative Density all returns gt 10 ft (for comp w/ CC) </td> </tr> <tr> <td> Band 19 </td> <td> RD_gt20ft </td> <td> Relative Density all returns gt 20 ft (for comp w/ CC) </td> </tr> <tr> <td> Band 20 </td> <td> RD_gt49ft </td> <td> Relative Density greater than 49 ft (Canopy) </td> </tr> <tr> <td> Band 21 </td> <td> CC_gt2ft </td> <td> Canopy Cover gt 2 ft (based only on first returns) </td> </tr> <tr> <td> Band 22 </td> <td> CC_gt10ft </td> <td> Canopy Cover gt 10 ft (based only on first returns) </td> </tr> <tr> <td> Band 23 </td> <td> CC_gt20ft </td> <td> Canopy Cover gt 20 ft (based only on first returns) </td> </tr> <tr> <td> Band 24 </td> <td> CC_gt49ft </td> <td> Canopy Cover gt 49 ft (based only on first returns) </td> </tr> <tr> <td> Band 25 </td> <td> MnRHgt2ft </td> <td> Mean of all relative heights gt 2 ft (includes shrubs, midstory and upper canopy) </td> </tr> <tr> <td> Band 26 </td> <td> MnRHgt10ft </td> <td> Mean of all relative heights gt 10 ft (includes all midstory and upper canopy) </td> </tr> <tr> <td> Band 27 </td> <td> MnRHgt20ft </td> <td> Mean of all relative heights gt 20 ft (includes high midstory and upper canopy) </td> </tr> <tr> <td> Band 28 </td> <td> MnRHgt49ft </td> <td> Mean of all relative heights gt 49 ft (includes upper canopy) </td> </tr> </tbody> </table> Full descriptions of the creation of these raster outputs is available in the companion publication - https://www.mdpi.com/2072-4292/13/23/4763

Relative Density Canopy Cover Outputs for Block2 Lidar data in the Florida Panhandle 2018

Forest cover and density metrics (RDCC) extracted from 2018 Lidar in the Florida panhandle Block 2. These 5m horizontal resolution rasters consist of 28 bands of forest cover and density metrics created using the lidR R software package from 2018 Lidar data. The 28 bands are listed in the following table:  <table> <tbody> <tr> <td>RDCC Band</td> <td>lidR short name</td> <td>Metric Description</td> </tr> <tr> <td>Band 1</td> <td>Num_Returns</td> <td>Total number of returns in cell</td> </tr> <tr> <td>Band 2 </td> <td>Num_GrndRet</td> <td>Number of ground returns in cell</td> </tr> <tr> <td>Band 3</td> <td>Num_1stRet</td> <td>Number of first returns in cell</td> </tr> <tr> <td>Band 4</td> <td>Grnd_Elev</td> <td>Ground Elevations (above geoid, etc)</td> </tr> <tr> <td>Band 5</td> <td>Mn_RH</td> <td>Mean of all Relative Heights</td> </tr> <tr> <td>Band 6</td> <td>SD_RH</td> <td>Std. Dev of all Relative heights</td> </tr> <tr> <td>Band 7</td> <td>RHt_95th</td> <td>Relative Height 95%</td> </tr> <tr> <td>Band 8</td> <td>RHt_90th</td> <td>Relative Height 90%</td> </tr> <tr> <td>Band 9</td> <td>RHt_75th</td> <td>Relative Height 75%</td> </tr> <tr> <td>Band 10</td> <td>RHt_50th</td> <td>Relative Height 50%</td> </tr> <tr> <td>Band 11</td> <td>RHt_25th</td> <td>Relative Height 25%</td> </tr> <tr> <td>Band 12</td> <td>RHt_10th</td> <td>Relative Height 10%</td> </tr> <tr> <td>Band 13</td> <td>RHt_05th</td> <td>Relative Height  5%</td> </tr> <tr> <td>Band 14</td> <td>RD_2to10ft</td> <td>Relative Density 2 to 10 ft (Shrubs)</td> </tr> <tr> <td>Band 15</td> <td>RD_10to20ft</td> <td>Relative Density 10 to 20 ft (High shrub/low midstory)</td> </tr> <tr> <td>Band 16</td> <td>RD_20to49ft</td> <td>Relative Density 20 to 49 ft (High midstory)</td> </tr> <tr> <td>Band 17</td> <td>RD_gt2ft</td> <td>Relative Density all returns gt 2 ft (for comp w/ CC)</td> </tr> <tr> <td>Band 18</td> <td>RD_gt10ft</td> <td>Relative Density all returns gt 10 ft (for comp w/ CC)</td> </tr> <tr> <td>Band 19</td> <td>RD_gt20ft</td> <td>Relative Density all returns gt 20 ft (for comp w/ CC)</td> </tr> <tr> <td>Band 20</td> <td>RD_gt49ft</td> <td>Relative Density greater than 49 ft (Canopy)</td> </tr> <tr> <td>Band 21</td> <td>CC_gt2ft</td> <td>Canopy Cover gt 2 ft (based only on first returns)</td> </tr> <tr> <td>Band 22</td> <td>CC_gt10ft</td> <td>Canopy Cover gt 10 ft (based only on first returns)</td> </tr> <tr> <td>Band 23</td> <td>CC_gt20ft</td> <td>Canopy Cover gt 20 ft (based only on first returns)</td> </tr> <tr> <td>Band 24</td> <td>CC_gt49ft</td> <td>Canopy Cover gt 49 ft (based only on first returns)</td> </tr> <tr> <td>Band 25</td> <td>MnRHgt2ft</td> <td>Mean of all relative heights gt 2 ft (includes shrubs, midstory and upper canopy)</td> </tr> <tr> <td>Band 26</td> <td>MnRHgt10ft</td> <td>Mean of all relative heights gt 10 ft (includes all midstory and upper canopy)</td> </tr> <tr> <td>Band 27</td> <td>MnRHgt20ft</td> <td>Mean of all relative heights gt 20 ft (includes high midstory and upper canopy)</td> </tr> <tr> <td>Band 28</td> <td>MnRHgt49ft</td> <td>Mean of all relative heights gt 49 ft (includes upper canopy)</td> </tr> </tbody> </table> Full descriptions of the creation of these raster outputs is available in the companion publication - https://www.mdpi.com/2072-4292/13/23/4763