DOI: 10.5061/dryad.2jm63xt0d
title: Imaging spectroscopy reveals topographic variability effects on grassland functional traits and drought responses

description: Functional traits and their variations are essential indicators of plant
 metabolism, growth, distribution, and survival and determine how a plant
 and an ecosystem function. Under the same climatic condition, traits can
 vary significantly between species and within the same species growing in
 different topographic conditions. When drought stress occurs, plants
 growing in these conditions may respond in various ways as their tolerance
 and adaptability are influenced by differences in topography. Insights
 into topographic variability-driven trait variation and drought response
 can improve our prediction of ecosystem functioning and ecological
 impacts. Imaging spectroscopy enables accurate identification of plant
 species, extraction of functional traits, and characterization of
 topography-driven and drought-related impacts on trait variation across
 spatial scales. However, applying this data in a heterogeneous grassland
 ecosystem is challenging as species are small, high mixed, spectrally and
 texturally similar, and highly varied with small-scale variation in
 topography. This paper presents the first study to explore the use of
 high-resolution airborne imaging spectroscopy for characterizing the
 variation of key traits—such as chlorophylls (Chl), carotenoids (Car),
 Chl/Car ratio, water content (WC), and leaf area index (LAI)—across
 topographic gradients and under drought stress at the species level in a
 heterogeneous grassland. The results demonstrate significant relationships
 between functional traits and topographic variability, with the strength
 of these relationships varying among species and across different
 environmental conditions. Additionally, drought-induced trait responses
 differed notably both within and between species, particularly between
 drought-tolerant invasive species and native species, as well as between
 lower and upper slope positions. The study makes a significant
 contribution to advancing our understanding of biological and ecological
 processes, enhancing the ability to predict plant invasion mechanism and
 ecosystem functioning under stressed environments.
description: # Data and Codes: Imaging spectroscopy reveals topographic variability
 effects on grassland functional traits and drought responses ## Data ###
 Hyperspectral images (hyperspectral\_images): Hyperspectral images used in
 this study include **20160823_HSI.tif** for the year of 2016 and
 **20170820_HSI_INT_N.tif** for the year of 2017. Both datasets have 301
 bands ranging from 400 nm to 1000 nm with a spectral interval of 2 nm. The
 reflectance values of pixels in the **20170820_HSI_INT_N.tif** dataset are
 scaled up by multiplying the original reflectance by 10000 and stored in
 the integer data format. Hyperspectral images were acquired on 23 August
 2016 and 20 August 2017 by a Mirco-Hyperspec VNIR push-broom sensor
 (Headwall Photonics Inc, USA), operated on a JetRanger helicopter. ###
 Species classification training data
 (species\_classification\_training\_data): This dataset contains the
 training and test datasets in shapefile format for species classification
 model training and validation. The training dataset (80%) is stored in the
 train_split_Aug folder while the test dataset (20%) is stored in the
 test_split_Aug folder. In these folders, data of each species is stored in
 a separate shapefile. ### Soil and shadow mask (soil\_shadow\_mask):
 **20170820_soil_shadow_mask.tif**: a mask file for masking out soil and
 shadow background in the species classification results. ###
 Spectral-trait data to build trait models (traits\_spectra):
 **20160823_PlantProp_full_leaf.csv**: leaf-level trait and spectral data
 collected in 2016 * *Site*: The name of the field plot * *Splname*: The
 name of the leaf sample * *Species*: The name of the species * *Greeness*:
 The leaf greenness levels * *Chl*: The total chlorophyll content of the
 leaf sample (µg/cm^2) * *Chl_ab*: The chlorophyll-a to chlorophyll-b ratio
 of the leaf sample * *Car*: The total carotenoid content of the leaf
 sample (µg/cm^2) * *Chl_car*: The chlorophyll to carotenoid ratio of the
 leaf sample * *WC*: The water content of the leaf sample (g/cm^2) * *SLA*:
 The specific leaf area of the leaf sample (cm^2/g) * *B_0350 to B_2500*:
 The spectral reflectance values from band 350 nm to band 2,500 nm
 **20160823_PlantProp_full_image.csv**: image-level trait and spectral data
 collected in 2016 * *Site*: The name of the field plots * *Species*: The
 name of the species * *Chl*: The total chlorophyll content of the field
 plot (µg/cm^2) * *Chl_ab*: The chlorophyll-a to chlorophyll-b ratio of the
 field plot * *Car*: The total carotenoid content of the field plot
 (µg/cm^2) * *Chl_car*: The chlorophyll to carotenoid ratio of the field
 plot * *WC*: The water content of the field plot (g/cm^2) * *LAI*: The
 Leaf Area Index of the field plot * *B_0400 to B_1000*: The spectral
 reflectance values from band 400 nm to band 1,000 nm
 **20170820_PlantProp_full_leaf.csv**: leaf-level trait and spectral data
 collected in 2017 * *Site*: The name of the field plot * *Splname*: The
 name of the leaf sample * *Species*: The name of the species * *Greeness*:
 The leaf greenness levels * *Chl*: The total chlorophyll content of the
 leaf sample (µg/cm^2) * *Chl_ab*: The chlorophyll-a to chlorophyll-b ratio
 of the leaf sample * *Car*: The total carotenoid content of the leaf
 sample (µg/cm^2) * *Chl_car*: The chlorophyll to carotenoid ratio of the
 leaf sample * *WC*: The water content of the leaf sample (g/cm^2) * *SLA*:
 The specific leaf area of the leaf sample (cm^2/g) * *B_0350 to B_2500*:
 The spectral reflectance values from band 350 nm to band 2,500 nm
 **20170820_PlantProp_full_image.csv**: image-level trait and spectral data
 collected in 2017 * *Site*: The name of the field plots * *Species*: The
 name of the species * *Chl*: The total chlorophyll content of the field
 plot (µg/cm^2) * *Chl_ab*: The chlorophyll-a to chlorophyll-b ratio of the
 field plot * *Car*: The total carotenoid content of the field plot
 (µg/cm^2) * *Chl_car*: The chlorophyll to carotenoid ratio of the field
 plot * *WC*: The water content of the field plot (g/cm^2) * *LAI*: The
 Leaf Area Index of the field plot * *B_0400 to B_1000*: The spectral
 reflectance values from band 400 nm to band 1,000 nm Leaf and field
 reflectance measurements were taken using a FieldSpec 3 spectroradiometer
 from Malvern PANalytical Company, United Kingdom. The sensor measured
 reflectance from 400 nm to 2500 nm at an interval of 1 nm. ### Topographic
 position index data (topographic\_position\_index):
 **tpi_2m_rescale_clip.tif** This is a raster dataset of the topographic
 position index of the study area. ### Ground sampling sites
 (ground\_sampling\_sites): These datasets contain the locational data of
 the field sampling sites of both 2016 (**GroundSites_2016.shp**) and 2017
 (**GroundSites_2017.shp**). These datasets allow us to correlate field and
 laboratory data with airborne hyperspectral images. ### Tabular data
 (tabular\_data): **plantprop_canopy_TPI_moisture.csv** This file contains
 soil moisture data measured at 42 field sites in 2016 and 2017 and its
 corresponding topographic position index (TPI) values. This dataset is
 used to explore the correlation between soil moisture and TPI in both
 drought year (2016) and normal year (2017). * *Site*: The name of the
 field plot * *Species*: The name of the species * *Year*: The year of
 collection * *Soil_moisture*: The soil moisture value of the field plot
 (%) * *TPI*: The Topographic Position Index value of the field plot
 **plantprop_canopy_box.csv** This file contains the values of topographic
 position index (TPI) and five canopy-level traits, including chlorophyll
 content (Chl), chlorophylls to carotenoids ratio (Chl/Car), carotenoid
 content (Car), water content (WC) and leaf area index (LAI), at sampling
 field sites of the year of 2016, 2017, and 2016-2017 combined. This
 dataset is used to examine the correlation between TPI and these traits at
 the canopy level. * *Site*: The name of the field plot * *Species*: The
 name of the species * *TPI*: The Topographic Position Index value of the
 field plot * *Year*: The year of collection * *Chl*: The total chlorophyll
 content of the field plot (µg/cm^2) * *Car*: The total carotenoid content
 of the field plot (µg/cm^2) * *Chl_Car*: The chlorophyll to carotenoid
 ratio of the field plot * *WC*: The water content of the field plot
 (g/cm^2) * *LAI*: The Leaf Area Index value of the field plot
 **plantprop_2016_2017_N_box.csv** This file contains the values of
 topographic position index (TPI), the six classified TPI categories (ridge
 (RD), upper slope (US), midslope (MS), toe slope (TS), flat (FL), and
 valey (VL)), and five canopy-level traits, including chlorophyll content
 (Chl), chlorophylls to carotenoids ratio (Chl/Car), carotenoid content
 (Car), water content (WC) and leaf area index (LAI), extracted from trait
 maps of the years of 2016 and 2017. This dataset is used to examine the
 trait variation both within and between species in both drought year
 (2016) and normal year (2017) across topographic variability categories. *
 *Species*: The name of the species * *TPI_Cat*: The topographic category
 of pixels extracted from the TPI category maps * *TPI*: The Topographic
 Position Index of pixels extracted from the TPI maps * *Year*: The year of
 collection * *Chl*: The total chlorophyll content of pixels extracted from
 the Chl trait maps (µg/cm^2) * *Chl_Car*: The chlorophyll to carotenoid
 ratio of pixels extracted from the Chl_Car trait maps * *Car*: The total
 carotenoid content of pixels extracted from the Car trait maps (µg/cm^2) *
 *WC*: The water content of pixels extracted from the WC trait maps
 (g/cm^2) * *LAI*: The Leaf Area Index value of pixels extracted from the
 LAI trait maps **plantprop_2016_2017_N_dif.csv** This file contains the
 values of topographic position index (TPI), the six classified TPI
 categories (ridge (RD), upper slope (US), midslope (MS), toe slope (TS),
 flat (FL), and valey (VL)), and the differences in five canopy-level
 traits, including chlorophyll content (Chl), chlorophylls to carotenoids
 ratio (Chl/Car), carotenoid content (Car), water content (WC) and leaf
 area index (LAI) between 2016 and 2017, extracted from trait maps. This
 dataset is used to examine the trait variation both within and between
 species under drought stress across topographic variability categories. *
 *Species*: The name of the species * *TPI_Cat*: The topographic category
 of pixels extracted from the TPI category maps * *TPI*: The Topographic
 Position Index of pixels extracted from the TPI maps * *Chl_diff*: The
 difference in chlorophyll content (µg/cm^2) between 2016 and 2017 of each
 pixel * *Chl_Car_diff*: The difference in chlorophyll/carotenoid ratio
 between 2016 and 2017 of each pixel * *Car_diff*: The difference in
 carotenoid content (µg/cm^2) between 2016 and 2017 of each pixel *
 *WC_diff*: The difference in water content (g/cm^2) between 2016 and 2017
 of each pixel * *LAI_diff*: The difference in Leaf Area Index between 2016
 and 2017 of each pixel ## Codes
 **Species_classification_RandomForest.ipynb** The Python notebook for
 species classification with the Random Forest classifier.
 **PLSR_leaf_model.R** The R code for building leaf-level trait model using
 the Partial Least Squares Regression (PLSR) method. **PLSR_image_model.R**
 The R code for building image-level trait model and deriving trait maps
 from hyperspectral images using the Partial Least Squares Regression
 (PLSR) method. **Statistical_analysis.R** The R code for all other
 statistical analyses of the study. ## Usage notes ### Handling shapefiles
 (.shp) All the shapefiles (.shp) used in this study contain the geometry
 and attributes of geospatial features (e.g., points, lines, polylines,
 polygons). The file bundle contains the main file .shp and companion files
 including: .cpg, .dbf, .prj, .sbn, .sbx, .shx. Description of these file
 extensions is given as follows: **.shp**: The main geospatial data file
 that contains feature geometry. **.cpg**: The file specifying the codepage
 to identify the characterset. **.dbf**: The dBASE that contains the
 attributes of features. **.prj**: The file that contains the coordinate
 system and map projection information. **.sbn**: The file containing the
 spatial index of features. **.sbx**: The file containing the spatial index
 of features. **.shx**: The file containing the index of feature geometry.
 More details about these file extensions and the information they contain
 can be found at:
 [https://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/shapefile-file-extensions.htm#:~:text=xml%E2%80%94Metadata%20for%20ArcGIS%E2%80%94stores,the%20characterset%20to%20be%20used](https://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/shapefile-file-extensions.htm#:~:text=xml%E2%80%94Metadata%20for%20ArcGIS%E2%80%94stores,the%20characterset%20to%20be%20used). The main .shp file can be opened and analyzed by Python, R, and many other programming languages, and open-source geospatial software such as QGIS, SAGA GIS, GRASS GIS, GeoDa, etc. ### Handling GeoTIFF files (.tif) All the shapefiles (.tif) used in this study to store raster data and images and its associated geospatial information. The file bundle contains the main file .tif and companion files including (not all files need all these companion files): .hdr, .aux, .cpg, .dbf, .enp, .ovr, .tfw, .xml. Description of these file extensions is given as follows: **.tif**: Stores image information and raster graphics. **.hdr**: The header file that stores information of an image or a raster, including: cell size, data type, compression technique, blocking factor, and tile. **.aux**: Stores additional image or raster information that cannot be stored in the TIFF file. **.cpg**: The file specifying the codepage to identify the characterset. **.dbf**: The dBASE that contains the attributes of an image or a raster file. **.enp**: Stores a pyramid file for display and visualization in ENVI software. **.ovr**: Stores a pyramid file for display and visualization. **.tfw**: Contains georeferencing information for a raster. **.xml**: Metadata of an image or a raster file. The main .tif file can be opened and analyzed by Python, R, and many other programming languages, and open-source geospatial software such as QGIS, SAGA GIS, GRASS GIS, GeoDa, etc, or proprietary software such as ArcGIS Pro, ENVI, ERDAS Imagine, etc.