scholarly journals Assessment of Unmanned Aerial Systems (UAS) Surveys in Dryland Ecosystems

2020 ◽  
Author(s):  
Anna V. Roser
Keyword(s):  
Land Surface ◽  
Field Data ◽  
Unmanned Aircraft ◽  
Plant Functional Types ◽  
Percent Cover ◽  
Field Observations ◽  
Fractional Cover ◽  
Functional Types ◽  
Dryland Ecosystems ◽  
Field Plots

Drylands cover 41% of the global land surface and provide ecosystem services to 38% of the world’s population. Dryland ecosystems have already been degraded or threatened by the increased rates of wildfire and invasive annual grasses, as well as changes in precipitation patterns. We cannot protect, mitigate, or restore drylands without comprehensive vegetation surveys. To understand ecosystem processes, we need to know the composition of vegetation at the patch and plant scales. Field observations are limited in coverage, and are expensive and time intensive. Data from Unmanned Aircraft Systems (UAS) will fill the niche between field data and medium scale remotely sensed data, and support the potential for upscaling. UAS-based remote sensing will also help extend the spatiotemporal scope of field surveys, improving efficiency and effectiveness. This study aims to test UAS methods to estimate two important vegetation metrics (1) fractional photosynthetic cover and (2) fractional cover of plant functional types. For both objectives, a series of surveys were conducted using fine-scale spatial resolution (1-4 cm pixel-1) multispectral UAS data collected in Reynolds Creek Experimental Watershed in Southwestern Idaho, USA. Data were collected at three sites along an elevation and precipitation gradient. Each site is characterized by a different type of sagebrush: Wyoming Big Sage, Low sage, and Mountain big sage. The first study in this thesis tests multiple vegetation indices at each site to assess their accuracy in modeling photosynthetic cover. We found the Modified Soil Adjusted Vegetation index (MSAVI) had the highest accuracy when modeling photosynthetic cover at each site (62-93%). The modeled photosynthetic cover was compared to field data consisting of point frame plots (n = 30) at each site. Correlations between field and UAS-derived cover estimates showed significant positive relationships at the Low Sage (r = 0.75, pr = 0.55, p = 0.002), but not at Wyoming Big Sage (r = 0.10, p = 0.61). These results demonstrate methods to estimate photosynthetic cover at fine scales in three types of sagebrush using UAS imagery. Additionally, these results suggest that UAS surveys has high correlation with field measurements at mid and high elevation sagebrush sites, but more studies are needed in low elevation sites to understand the potential of integrating UAS and field observations of photosynthetic cover. Our second study quantified fractional cover of plant functional types in the same three sagebrush sites listed above. First, we tested Object-Based Image Analysis (OBIA) for classification of UAS surveys into plant functional types. We assessed the accuracy of the maps using confusion matrices; overall classification accuracies were strong: Wyoming Big Sage (70%), Low Sage (73%), and Mountain Big Sage (78%). The classified maps of plant functional types were compared to data from field plots (n = 30) at each site. We found significant positive correlations for shrubs (r = 0.58; 0.83), forbs (r = 0.39; 0.94), and bare ground (r = 0.61; 0.70) at our Low Sage and Mountain Big Sage. However we did not find significant relationships for the gramminoid class at any site (r = 0.18; 0.3; 0.32). Second, we tested the application of OBIA to sum shrub abundance from UAS imagery. Abundance data from field plots (n= 24 per site) were tested for agreement with UAS imagery. We found no correlation at any site with field observations at the 10m2 scale (r = -0.22; 0.12; 0.26). Overall, we were able to calculate percent cover for large-unit plant functional types, such as shrubs, trees, and some forbs. Accuracy for gramminoid classification was low due to small plant size, confounding soil reflectance, and grasses that grew beneath shrub canopies. This research demonstrates that UAS methods can be used to estimate photosynthetic cover and map plant functional types. Using UAS surveys also increased coverage and sampling density of data when compared to traditional field observations. These findings help land managers, restoration experts, and other researchers who monitor, manage, and protect dryland ecosystems by demonstrating an accurate and less expensive approach to collecting ecosystem data.

scholarly journals Integrating Arctic Plant Functional Types in a Land Surface Model Using Above‐ and Belowground Field Observations

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Benjamin N. Sulman ◽  
Verity G. Salmon ◽  
Colleen M. Iversen ◽  
Amy L. Breen ◽  
Fengming Yuan ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Plant Functional Types ◽  
Surface Model ◽  
Field Observations ◽  
Functional Types

scholarly journals Integrating Arctic plant functional types in a land surface model using above- and belowground field observations

2020 ◽  
Author(s):  
Benjamin N Sulman ◽  
Verity Gale Salmon ◽  
Colleen Marie Iversen ◽  
Amy L Breen ◽  
Fengming Yuan ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Plant Functional Types ◽  
Surface Model ◽  
Field Observations ◽  
Functional Types

Spatial Distribution of Roots across Three Dryland Ecosystems and Plant Functional Types

2019 ◽  
Vol 79 (2) ◽  
pp. 159 ◽  
Author(s):  
Jessica G. Swindon ◽  
William K. Lauenroth ◽  
Daniel R. Schlaepfer ◽  
Ingrid C. Burke
Keyword(s):  
Spatial Distribution ◽  
Plant Functional Types ◽  
Functional Types ◽  
Dryland Ecosystems ◽  
Distribution Of Roots

scholarly journals Towards spatial assessment of carbon sequestration in peatlands: spectroscopy based estimation of fractional cover of three plant functional types

2009 ◽  
Vol 6 (2) ◽  
pp. 275-284 ◽  
Author(s):  
G. Schaepman-Strub ◽  
J. Limpens ◽  
M. Menken ◽  
H. M. Bartholomeus ◽  
M. E. Schaepman
Keyword(s):  
Environmental Changes ◽  
Spatial Scales ◽  
Vascular Plant ◽  
C Sequestration ◽  
Plant Functional Types ◽  
Vegetation Composition ◽  
Fractional Cover ◽  
Functional Types ◽  
C Stocks ◽  
Historical Times

Abstract. Peatlands accumulated large carbon (C) stocks as peat in historical times. Currently however, many peatlands are on the verge of becoming sources with their C sequestration function becoming sensitive to environmental changes such as increases in temperature, decreasing water table and enhanced nitrogen deposition. Long term changes in vegetation composition are both, a consequence and indicator of future changes in C sequestration. Spatial continuous accurate assessment of the vegetation composition is a current challenge in keeping a close watch on peatland vegetation changes. In this study we quantified the fractional cover of three major plant functional types (PFTs; Sphagnum mosses, graminoids, and ericoid shrubs) in peatlands, using field spectroscopy reflectance measurements (400–2400 nm) on 25 plots differing in PFT cover. The data was validated using point intercept methodology on the same plots. Our results showed that the detection of open Sphagnum versus Sphagnumcovered by vascular plants (shrubs and graminoids) is feasible with an R2 of 0.81. On the other hand, the partitioning of the vascular plant fraction into shrubs and graminoids revealed lower correlations of R2 of 0.54 and 0.57, respectively. This study was based on a dataset where the reflectance of all main PFTs and their pure components within the peatland was measured at local spatial scales. Spectrally measured species or plant community abundances can further be used to bridge scaling gaps up to canopy scale, ultimately allowing upscaling of the C balance of peatlands to the ecosystem level.

scholarly journals Gross and net land cover changes based on plant functional types derived from the annual ESA CCI land cover maps

2017 ◽  
Author(s):  
Wei Li ◽  
Natasha MacBean ◽  
Philippe Ciais ◽  
Pierre Defourny ◽  
Céline Lamarche ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Land Surface ◽  
Climate Models ◽  
Plant Functional Types ◽  
Forest Loss ◽  
Land Cover Changes ◽  
Cropland Area ◽  
Functional Types ◽  
Land Cover Maps

Abstract. Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes at global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas, gross and net changes of different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km2 in 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013) and Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992–2015 are 1.5 and 0.9 million km2 respectively, resulting in a net forest loss of 0.6 million km2, mainly occurring in South and Central America. The magnitudes of gross changes of forest, cropland and grassland PFTs in ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA while after 2007 in HYDE3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long time-series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics, and in climate models to simulate land-cover change feedbacks on climate. The annual ESA CCI land cover products can be downloaded from http://maps.elie.ucl.ac.be/CCI/viewer/download.php (Land Cover Maps – v2.0.7; see details in Section 2.5).

scholarly journals Global Vegetation Photosynthetic Phenology Products Based on MODIS Vegetation Greenness and Temperature: Modeling and Evaluation

2021 ◽  
Vol 13 (24) ◽  
pp. 5080
Author(s):  
Xiaojun Xu ◽  
Yan Tang ◽  
Yiling Qu ◽  
Zhongsheng Zhou ◽  
Junguo Hu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Forest Type ◽  
Gross Primary Production ◽  
Plant Functional Types ◽  
Linear Regression Models ◽  
Climatic Regions ◽  
Functional Types ◽  
Global Vegetation

Land surface phenology (LSP) products that are derived from different data sources have different definitions and biophysical meanings. Discrepancies among these products and their linkages with carbon fluxes across plant functional types and climatic regions remain somewhat unclear. In this study, to differentiate LSP related to gross primary production (GPP) from LSP related to remote sensing data, we defined the former as vegetation photosynthetic phenology (VPP), including the starting and ending days of GPP (SOG and EOG, respectively). Specifically, we estimated VPP based on a combination of observed VPP from 145 flux-measured GPP sites together with the vegetation index and temperature data from MODIS products using multiple linear regression models. We then compared VPP estimates with MODIS LSP on a global scale. Our results show that the VPP provided better estimates of SOG and EOG than MODIS LSP, with a root mean square error (RMSE) for SOG of 12.7 days and a RMSE for EOG of 10.5 days. The RMSE was approximately three weeks for both SOG and EOG estimates of the non-forest type. Discrepancies between VPP and LSP estimates varied across plant functional types (PFTs) and climatic regions. A high correlation was observed between VPP and LSP estimates for deciduous forest. For most PFTs, using VPP estimates rather than LSP improved the estimation of GPP. This study presents a useful method for modeling global VPP, investigates in detail the discrepancies between VPP and LSP, and provides a more effective global vegetation phenology product for carbon cycle modeling than the existing ones.

Wetland chronosequence as a model of peatland development: Vegetation succession, peat and carbon accumulation

The Holocene ◽  
2012 ◽  
Vol 23 (1) ◽  
pp. 25-35 ◽  
Author(s):  
Eeva-Stiina Tuittila ◽  
Sari Juutinen ◽  
Steve Frolking ◽  
Minna Väliranta ◽  
Anna M Laine ◽  
...  
Keyword(s):  
Plant Communities ◽  
Growth Models ◽  
Plant Functional Types ◽  
Carbon Accumulation ◽  
Field Observations ◽  
N Accumulation ◽  
Age Cohorts ◽  
Peat Core ◽  
Functional Types ◽  
Peatland Development

Model validation experiments are fundamental to ensure that the peat growth models correspond with the diversity in nature. We evaluated the Holocene Peatland Model (HPM) simulation against the field observations from a chronosequence of peatlands and peat core data. The ongoing primary peatland formation on the isostatically rising coast of Finland offered us an exceptional opportunity to study the peatland succession along a spatial continuum and to compare it with the past succession revealed by vertical peat sequences. The current vegetation assemblages, from the seashore to a 3000 year old bog, formed a continuum from minerotrophic to ombrotrophic plant communities. A similar sequence of plant communities was found in the palaeovegetation. The distribution of plant functional types was related to peat thickness and water-table depth (WTD) supporting the assumptions in HPM, though there were some differences between the field data and HPM. Palaeobotanical evidence from the oldest site showed a rapid fen–bog transition, indicated by a coincidental decrease in minerotrophic plant functional types and an increase in ombrotrophic plant functional types. The long-term mean rate of carbon (C) accumulation varied from 2 to 34 g C/m2 per yr, being highest in the intermediate age cohorts. Mean nitrogen (N) accumulation varied from 0.1 to 3.9 g N/m2 per yr being highest in the youngest sites. WTD was the deepest in the oldest sites and its variation there was temporally the least but spatially the highest. Evaluation of the HPM simulations against the field observations indicated that HPM reasonably well simulates peatland development, except for very young peatlands.

scholarly journals Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion

2021 ◽  
Vol 25 (5) ◽  
pp. 2399-2417
Author(s):  
Yanlan Liu ◽  
Nataniel M. Holtzman ◽  
Alexandra G. Konings
Keyword(s):  
Data Fusion ◽  
Land Surface ◽  
Large Scale ◽  
Microwave Remote Sensing ◽  
Plant Functional Types ◽  
Drought Response ◽  
Model Data ◽  
Plant Hydraulics ◽  
Functional Types ◽  
Vegetation Water

Abstract. Droughts are expected to become more frequent and severe under climate change, increasing the need for accurate predictions of plant drought response. This response varies substantially, depending on plant properties that regulate water transport and storage within plants, i.e., plant hydraulic traits. It is, therefore, crucial to map plant hydraulic traits at a large scale to better assess drought impacts. Improved understanding of global variations in plant hydraulic traits is also needed for parameterizing the latest generation of land surface models, many of which explicitly simulate plant hydraulic processes for the first time. Here, we use a model–data fusion approach to evaluate the spatial pattern of plant hydraulic traits across the globe. This approach integrates a plant hydraulic model with data sets derived from microwave remote sensing that inform ecosystem-scale plant water regulation. In particular, we use both surface soil moisture and vegetation optical depth (VOD) derived from the X-band Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; collectively AMSR-E). VOD is proportional to vegetation water content and, therefore, closely related to leaf water potential. In addition, evapotranspiration (ET) from the Atmosphere–Land Exchange Inverse (ALEXI) model is also used as a constraint to derive plant hydraulic traits. The derived traits are compared to independent data sources based on ground measurements. Using the K-means clustering method, we build six hydraulic functional types (HFTs) with distinct trait combinations – mathematically tractable alternatives to the common approach of assigning plant hydraulic values based on plant functional types. Using traits averaged by HFTs rather than by plant functional types (PFTs) improves VOD and ET estimation accuracies in the majority of areas across the globe. The use of HFTs and/or plant hydraulic traits derived from model–data fusion in this study will contribute to improved parameterization of plant hydraulics in large-scale models and the prediction of ecosystem drought response.

scholarly journals Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

2018 ◽  
Vol 10 (1) ◽  
pp. 219-234 ◽  
Author(s):  
Wei Li ◽  
Natasha MacBean ◽  
Philippe Ciais ◽  
Pierre Defourny ◽  
Céline Lamarche ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Land Surface ◽  
Climate Models ◽  
Plant Functional Types ◽  
Forest Loss ◽  
Land Cover Changes ◽  
Cropland Area ◽  
Functional Types ◽  
Land Cover Maps

Abstract. Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes on global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI (climate change initiative) land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas and gross and net changes in different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km2 in the year 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013) or Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE 3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992–2015 are 1.5 and 0.9 million km2 respectively, resulting in a net forest loss of 0.6 million km2, mainly occurring in South and Central America. The magnitudes of gross changes in forest, cropland and grassland PFTs in the ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE 3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA and after 2007 in HYDE 3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long-term time series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics, and in climate models to simulate land-cover change feedbacks on climate. The annual ESA CCI land cover products can be downloaded from http://maps.elie.ucl.ac.be/CCI/viewer/download.php (Land Cover Maps – v2.0.7; see details in Sect. 5). The PFT map translation protocol and an example in 2000 can be downloaded from https://doi.org/10.5281/zenodo.834229. The annual ESA CCI PFT maps from 1992 to 2015 at 0.5∘×0.5∘ resolution can also be downloaded from https://doi.org/10.5281/zenodo.1048163.

Sign in / Sign up

Export Citation Format

Share Document