Package: lidaRtRee 4.0.5
lidaRtRee: Forest Analysis with Airborne Laser Scanning (LiDAR) Data
Provides functions for forest analysis using airborne laser scanning (LiDAR remote sensing) data: tree detection (method 1 in Eysn et al. (2015) <doi:10.3390/f6051721>) and segmentation; forest parameters estimation and mapping with the area-based approach. It includes complementary steps for forest mapping: co-registration of field plots with LiDAR data (Monnet and Mermin (2014) <doi:10.3390/f5092307>); extraction of both physical (gaps, edges, trees) and statistical features from LiDAR data useful for e.g. habitat suitability modeling (Glad et al. (2020) <doi:10.1002/rse2.117>) and forest maturity mapping (Fuhr et al. (2022) <doi:10.1002/rse2.274>); model calibration with ground reference, and maps export.
Authors:
lidaRtRee_4.0.5.tar.gz
lidaRtRee_4.0.5.zip(r-4.5)lidaRtRee_4.0.5.zip(r-4.4)lidaRtRee_4.0.5.zip(r-4.3)
lidaRtRee_4.0.5.tgz(r-4.4-any)lidaRtRee_4.0.5.tgz(r-4.3-any)
lidaRtRee_4.0.5.tar.gz(r-4.5-noble)lidaRtRee_4.0.5.tar.gz(r-4.4-noble)
lidaRtRee_4.0.5.tgz(r-4.4-emscripten)lidaRtRee_4.0.5.tgz(r-4.3-emscripten)
lidaRtRee.pdf |lidaRtRee.html✨
lidaRtRee/json (API)
# Install 'lidaRtRee' in R: |
install.packages('lidaRtRee', repos = c('https://jmmonnet.r-universe.dev', 'https://cloud.r-project.org')) |
- chm_chablais3 - Canopy height model
- quatre_montagnes - Field plot inventory in the Quatre Montagnes area
- tree_inventory_chablais3 - Tree inventory data in France
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:2c3c1249e2. Checks:OK: 5 NOTE: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 29 2024 |
R-4.5-win | NOTE | Oct 29 2024 |
R-4.5-linux | NOTE | Oct 29 2024 |
R-4.4-win | OK | Oct 29 2024 |
R-4.4-mac | OK | Oct 29 2024 |
R-4.3-win | OK | Oct 29 2024 |
R-4.3-mac | OK | Oct 29 2024 |
Exports:.aba_metricsaa_las_chablais3aba_build_modelaba_combine_strataaba_inferenceaba_metricsaba_plotaba_predictadd_vegetation_indicesboxcox_itrboxcox_itr_bias_corboxcox_trcimg2Rastercircle2Rasterclean_rasterclouds_metricsclouds_tree_metricsconvert_rastercoregistrationcreate_diskdem_filteringedge_detectionellipses4Crowngap_detectionheight_regressionhist_detectionhist_stacklma_checkmaxima_detectionmaxima_selectionplot_matchedplot_tree_inventorypointList2polypoints2DSMpoints2DTMpolar2Projectedraster_chull_maskraster_local_maxraster_metricsraster_xy_maskraster_zonal_statsraster2Cimgrasters_moving_corrasters2Corseg_adjustsegmentationspecies_colorstd_tree_metricsterrain_points_metricstree_detectiontree_extractiontree_matchingtree_segmentation
Dependencies:abindbackportsbase64encBHbmpbootbroombslibcachemcallrcarcarDatacheckmateclassclassIntclicolorspacecowplotcpp11data.tableDBIdensEstBayesDerivdescdigestdistributionaldoBydownloaderdplyre1071evaluatefansifarverfastmapfontawesomeFormulafsgenericsggplot2gluegridExtragtablegvlmahighrhtmltoolshtmlwidgetsigraphimagerinlineisobandjpegjquerylibjsonliteKernSmoothknitrlabelinglatticelazyevalleapslidRlifecyclelme4loomagrittrMASSMatrixMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgbuildpkgconfigpngposteriorprocessxproxypspurrrquantregQuickJSRR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelreadbitmapreldistrglrlangrlasrmarkdownrstanrstantoolss2sassscalessfSparseMStanHeadersstarsstringistringrsurvivaltensorAterratibbletidyrtidyselecttifftinytexunitsutf8vctrsviridisLitewithrwkxfunyaml