DOI: 10.5281/zenodo.1198584
title: Forest Quality And Land Use Intensity Indicators

description: <b>Description: </b>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation<b>Project: </b>This dataset was collected as part of the following SAFE research project: <b>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</b><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available here<b>Data worksheets: </b>There are 4 data worksheets in this dataset:<b>Canopy-based forest quality metrics</b> (Worksheet Canopy)Dimensions: 213 rows by 6 columnsDescription: Fractional canopy cover and leaf area indexFields: <b>Plot</b>: SAFE project plot number (Field type: Location)<b>Date</b>: Date images were collected (Field type: Date)<b>LAI</b>: Leaf area index, corrected for clumping of leaves at plot level (25 m x 25 m) (Field type: Numeric)<b>fcover</b>: Fractional canopy cover (mean) (Field type: Numeric)<b>sdfcov</b>: Fractional canopy cover (standard deviation) (Field type: Numeric)<br><b>Above-ground biomass</b> (Worksheet AGB)Dimensions: 203 rows by 11 columnsDescription: Was derived from DBH and Height of trees for individuals &gt;= 10 cm DBH using five different algorithms on the raw data. We additionally binned heights of trees to account for uncertainties in tree height measurements and used multiple published equations combined with wood density estimates drawn from a distribution of wood density values that differs for unogged, logged and severely logged forest stands. We used oil palm specific equations for biomass estimations in oil palm plots. They will be identical estimates across the five algorithms used. Oil palms have a fundamentally different physical structure to forest trees, so we estimated AGB in oil palm plantations separately using the equation 〖AGB〗_palm=   (0.3747*height*100+3.6334)/1000 (Thenkabail et al. 2004). See Pfeifer M, Lefebvre V, Turner E, Cusack J, Khoo M, Chey VK, Peni M, Ewers RMet al. 2015, Deadwood biomass: an underestimated carbon stock in degraded tropical forests?, ENVIRONMENTAL RESEARCH LETTERS, Vol: 10:044019. Fields: <b>Plot</b>: SAFE project plot number (Field type: Location)<b>Date</b>: Date field plot data were collected (Field type: Date)<b>AGB_Saner</b>: AGB estimates developed for mixed-species forest stands in East Kalimantan, Indonesia (Field type: Numeric)<b>AGB_Chave_wet</b>: AGB estimates developed for wet forest (Field type: Numeric)<b>AGB_Chave_moist</b>: AGB estimates developed for moist forest (Field type: Numeric)<b>AGB_K09</b>: AGB estimates developed for logged over old growth forest in Malaysian Sabah (Field type: Numeric)<b>AGB_N10</b>: AGB estimate developed for old growth forest in Malaysia for forests 110 km south-east of Kuala Lumpur (Field type: Numeric)<b>AGB_Chave14</b>: AGB estimates developed for pantropical forest assuming a wood density of 0.64 (Field type: Numeric)<b>AGB_Chave14_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)<b>AGB_Chave14_Bin5_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)<br><b>SAFE project forest quality scores</b> (Worksheet Quality)Dimensions: 203 rows by 4 columnsDescription: Visual assessment of forest disturbanceFields: <b>Plot</b>: SAFE project plot number (Field type: Location)<b>Date</b>: Date of assessment (Field type: Date)<b>ForestQuality</b>: SAFE Project forest quality scores (Field type: Ordered Categorical)<br><b>Caneye software analyses carried out using Caneye v6.3.8 in August/September 2013</b> (Worksheet LAI_Caneye)Dimensions: 237 rows by 16 columnsDescription: LAI, fcover and fAPAR estimates derived from hemispherical images or using Sunscan Delta T device (Cambridge) if applicableFields: <b>Plotname</b>: SAFE project plot number (Field type: Location)<b>Date</b>: Date on which photographs were taken (Field type: Date)<b>HemiUp</b>: Number of sample points = number of pictures taken - upward looking fisheye pictures (Field type: Numeric)<b>LAI_eff_v6</b>: LAI effective estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)<b>LAI_true_v6</b>: LAI true (accounting for vegetation clumping) estimated following algorithm of  Caneye version 6 (v6.3.8) (Field type: Numeric)<b>LAI_eff_v5</b>: LAI effective estimated according to Caneye version 5 (Field type: Numeric)<b>LAI_true_v5</b>: LAI true (accounting for vegetation clumping) estimated according to Caneye version 5 (Field type: Numeric)<b>ALAeffv5</b>: Effective average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)<b>ALAtruev5</b>: True  average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)<b>Fap_meas_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)<b>Fap_mod_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation)  modelled (Field type: Numeric)<b>Fap_meas_Dif</b>: White - sky diffuse fAPAR  (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)<b>Fap_mod_Dif</b>: White - sky diffuse fAPAR  (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)<b>fcover</b>: Fractional canopy cover (mean). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)<b>sdfcov</b>: Fractional canopy cover (standard deviation). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)<br><b>Date range: </b>2010-07-01 to 2014-01-10<b>Latitudinal extent: </b>4.4245 to 4.7714<b>Longitudinal extent: </b>116.9477 to 117.7028