Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Möckel, Thomas; Löfgren, Oskar; Prentice, Honor C.; Eklundh, Lars; Hall, Karin

doi:10.1016/j.ecolind.2016.01.049

Cited by 13 publications

(12 citation statements)

References 119 publications

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“…For DMP the MPLSR prediction was only best for the common model and date 1 (R²CV of 0.76 and 0.67), while for the other dates the NDSI showed the best results (R²CV between 0.43 and 0.68) (Figure 3). This regression approach integrates spectral information from the whole hyperspectral range and its usefulness for measuring grassland properties has been acknowledged by other studies [36][37][38][39][40]. The predictive power of WorldView2 (WV2) bands (R² 0.13-0.55) was not satisfactory and never outperformed the NDSI or MPLSR approach.…”

Section: Exclusive Use Of Spectral Datamentioning

confidence: 99%

Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

et al. 2017

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An accurate estimation of biomass is needed to understand the spatio-temporal changes of forage resources in pasture ecosystems and to support grazing management decisions. A timely evaluation of biomass is challenging, as it requires efficient means such as technical sensing methods to assess numerous data and create continuous maps. In order to calibrate ultrasonic and spectral sensors, a field experiment with heterogeneous pastures continuously stocked by cows at three grazing intensities was conducted. Sensor data fusion by combining ultrasonic sward height (USH) with narrow band normalized difference spectral index (NDSI) (R 2 CV = 0.52) or simulated WorldView2 (WV2) (R 2 CV = 0.48) satellite broad bands increased the prediction accuracy significantly, compared to the exclusive use of USH or spectral measurements. Some combinations were even better than the use of the full hyperspectral information (R 2 CV = 0.48). Spectral regions related to plant water content were found to be of particular importance (996-1225 nm). Fusion of ultrasonic and spectral sensors is a promising approach to assess biomass even in heterogeneous pastures. However, the suggested technique may have limited usefulness in the second half of the growing season, due to an increasing abundance of senesced material.

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Section: Exclusive Use Of Spectral Datamentioning

confidence: 99%

Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

et al. 2017

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“…In contrast to the poor ability of the NDVI to explain levels of specialist richness in the grasslands, NDVI May explained 38% of the variation in mN, the majority of which represented shared effects with AGE (Figure b). Studies using hyperspectral remote sensing reveal strong relationships between mN and reflectance in the NIR and red spectral regions, which are sensitive to variation in biomass (e.g., Moeckel, Löfgren, Prentice, Eklundh, & Hall, ; Schweiger et al., ). The positive relationship between NDVI and mN in this study is consistent with the fact that both variables are used as proxies for productivity.…”

Section: Discussionmentioning

confidence: 99%

Landscape history confounds the ability of the NDVI to detect fine‐scale variation in grassland communities

et al. 2018

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The NDVI is a remotely sensed vegetation index that is frequently used in ecological studies. There is, however, a lack of studies that evaluate the ability of the NDVI to detect fine‐scale variation in grassland plant community composition and species richness. Ellenberg indicators characterize the environmental preferences of plant species—and community‐mean Ellenberg values have been used to explore the environmental drivers of community assembly. We used variation partitioning to test the ability of satellite‐based NDVI to explain community‐mean Ellenberg nutrient (mN) and moisture (mF) indices, and the richness of habitat‐specialist species in dry grasslands of different ages. The grasslands represent a gradient of decreasing soil nutrient status. If community composition is determined by the responses of individual species to the underlying environmental conditions and if, at the same time, community composition determines the optical characteristics of the vegetation canopy, then positive relationships between the NDVI and mN and mF are expected. Many grassland specialists are intolerant of nutrient‐rich soils. If specialist richness is negatively related to soil‐nutrient levels, then a negative association between the NDVI and specialist richness is expected. However, because grassland community composition is not only influenced by abiotic variables but also by other spatial and temporal drivers, we included spatial variables and grassland age in the statistical analyses. The NDVI explained the majority of the variation in mF, and also contributed to a substantial proportion of the variation in mN. However, variation in specialist richness and the lowest values of mN were explained by grassland age and spatial variables—but were poorly explained by the NDVI. Synthesis and applications. The NDVI showed a good ability to detect variation in plant community composition, and should provide a valuable tool for assessing fine‐scale environmental variation in grasslands or for monitoring changes in grassland habitat properties. However, because the concentration of grassland specialists not only depends on environmental variables but also on the age and spatial context of the grasslands, the NDVI is unlikely to allow the identification of grasslands with high numbers of specialist species.

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“…Based on this sensor combination, HyspIRI is expected to be useful in quantification of forest canopy biochemistry and components in the biogeochemical cycles of FES [21,[201][202][203], the health status of forests based on biochemical-biophysical ST, or the pattern and spatial distribution and diversity of forest plant species and communities [204]. Furthermore, it will be able to discriminate and monitor forest species [204,205], forest plant functional types, [206,207] or invasive species [21] as well as natural disasters and disturbance regimes, i.e., volcano eruptions, wildfires, beetle infestations [208,209], and the global carbon cycles.…”

Section: Multi-sensor Approachesmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

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Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Cited by 13 publications

References 119 publications

Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

Landscape history confounds the ability of the NDVI to detect fine‐scale variation in grassland communities

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

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