Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands

Wachendorf, M.; Fricke, Thomas; Möckel, Thomas

doi:10.1111/gfs.12312

Cited by 116 publications

(109 citation statements)

References 122 publications

(154 reference statements)

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“…In this respect, we need data analysis methods that allow interpretation of eddy-covariance flux measurements and remote sensing measurements (albedo, NDVI-derived estimates of LAI and biomass; see Wachendorf et al [112] for a review of available methods and their potential application in grassland research) in terms of biodiversity. Such data (or proxies obtained from model analysis, e.g., [113]) are needed to link models across spatial scales (both upscaling and downscaling).…”

Section: Discussionmentioning

confidence: 99%

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

2018

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There is increasing evidence that the impact of climate change on the productivity of grasslands will at least partly depend on their biodiversity. A high level of biodiversity may confer stability to grassland ecosystems against environmental change, but there are also direct effects of biodiversity on the quantity and quality of grassland productivity. To explain the manifold interactions, and to predict future climatic responses, models may be used. However, models designed for studying the interaction between biodiversity and productivity tend to be structurally different from models for studying the effects of climatic impacts. Here we review the literature on the impacts of climate change on biodiversity and productivity of grasslands. We first discuss the availability of data for model development. Then we analyse strengths and weaknesses of three types of model: ecological, process-based and integrated. We discuss the merits of this model diversity and the scope for merging different model types.

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Section: Discussionmentioning

confidence: 99%

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

2018

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“…Therefore, much time and effort is required to receive reliable data especially on large areas.Sensors attached on UAVs are useful non-destructive tools for obtaining spatial information from large and remote areas. There exist different sensor systems, such as LiDAR, ultrasound and RGB (red, green, blue) imaging to collect spatial data for a rapid quantification of aboveground biomass [3,[12][13][14]. A UAV in combination with a consumer-grade digital camera for RGB imaging represents a low-cost approach for estimating yield, which may be affordable und workable for farmers.…”

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confidence: 99%

“…The latter used a high-resolution camera (pixel size = 1 cm) mounted on a UAV and combined CH with spectral vegetation indices (VI), resulting in highly significant correlations with biomass. VIs from multior hyperspectral sensors also show promising potential for qualitative and quantitative biomass estimation; however extensive spectral calibration work is necessary for these technique [3].To understand the spatial variability and dynamics in grasslands over the entire growing season multi-temporal studies are needed. So far, no study using yield prediction models by SfM considered different proportions of legumes (including pure legume and grass stands as well as legume-grass mixtures), which frequently occur in practical grassland farming.…”

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Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging

2019

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An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and remote areas. Photogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH). The aim of this study was the development of prediction models for dry matter yield (DMY) in temperate grassland based on CH data generated by UAV RGB imaging over a whole growing season including four cuts. The multi-temporal study compared the remote sensing technique with two conventional methods, i.e., destructive biomass sampling and ruler height measurements in two legume-grass mixtures with red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) in combination with Italian ryegrass (Lolium multiflorum Lam.). To cover the full range of legume contribution occurring in a practical grassland, pure stands of legumes and grasses contained in each mixture were also investigated. The results showed, that yield prediction by SfM-based UAV RGB imaging provided similar accuracies across all treatments (R 2 = 0.59-0.81) as the ruler height measurements (R 2 = 0.58-0.78). Furthermore, results of yield prediction by UAV RGB imaging demonstrated an improved robustness when an increased CH variability occurred due to extreme weather conditions. It became apparent that morphological characteristics of clover-based canopies (R 2 = 0.75) allow a better remotely sensed prediction of total annual yield than for lucerne-grass mixtures (R 2 = 0.64), and that these crop-specific models cannot be easily transferred to other grassland types.Destructive biomass sampling is considered to be the most accurate yield estimation method but can also be considered as the most labor-intensive method [4]. Another approach for estimating biomass in grasslands is the assessment of canopy height (CH), which was frequently found to be positively correlated with crop biomass [5,6]. Traditional height measurements in grassland are often conducted with a rising plate meter, determining the compressed sward height, or with a ruler stick [7,8]. Furthermore, several portable technical devices for non-destructive biomass estimation were developed in the recent years, which so far were not widely distributed in agricultural practice, e.g., leaf area meter to asses leaf area index (LAI) [9], electronic capacitance meter, which measures the difference of capacitance between air and biomass [1] and a reflectometer, which measures intensity of spectral reflectance by light emitting diodes (LED) [10]. Biomass sampling, manual height measurement and the above mentioned technical devices need a substantial number of repetitions in combination with a spatially uniform distributions of the measurements to generate a reliable yield estimation [11]. Therefore, much time an...

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“…As summarized by Ali, Cawkwell, Dwyer, Barrett, and Green (2016), remote sensing from satellites equipped with optical, radio detection and ranging (radar) or light detection and ranging (LiDAR) sensors can be used to estimate temperate grassland crop biomass (Wachendorf, Fricke, & Möckel, 2017). These methods are non-invasive and address some of the issues encountered by the groundbased monitoring approaches described in the previous paragraph, such as the labour intensity, the temporal limitations (and also spatial, depending on the required resolution) and human bias (Ali et al, 2016).…”

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confidence: 99%

Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery

Borra‐Serrano

Swaef

Muylle

et al. 2019

Grass and Forage Science

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In perennial ryegrass breeding programmes, dry‐matter yield (DMY) of individual plots is monitored destructively at the different cuts or derived from non‐destructive canopy height measurements using devices like rising plate meters (RPM). These approaches both have constraints. Destructive sampling implies low temporal resolution, restraining the study of dry‐matter accumulation rates, while RPM measurements are influenced by the canopy structure and limit intra‐field variability identification. We present a phenotyping methodology, based on the use of an affordable RGB camera mounted on an unmanned aerial vehicle (UAV), to monitor the spatial and temporal evolution of canopy height and to estimate DMY. Weekly flights were carried out from April to October above a field comprising a diverse set of accessions. To test the capacity of UAV imagery to estimate canopy height, 8 ground control points and 28 artificial height references were placed at different locations. Accurate flights with an RMSE as low as 0.94 cm were achieved. In addition, canopy height was recorded using an RPM and destructive biomass samples were collected. Different models (linear, multiple linear, principal components, partial least squares regression and random forest) were used to predict DMY, and their performance was evaluated. The best estimations were obtained by combining variables including canopy height, vegetation indices and environmental data in a multiple linear regression (R2 = .81). All models built using UAV data obtained a lower RMSE than the one using RPM data. The approach presented is a possibility for breeders to incorporate new information in their selection process.

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Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands

Cited by 116 publications

References 122 publications

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging

Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery

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