Farmers, as well as agronomists, are intrigued by efficient quantification of grassland biomass at field-scale. Canopy surface height (CSH) based non-destructive grassland biomass estimation over a larger area has important advantages compared to destructive methods. 3D point clouds generated from remote sensing (RS) data offer a systematic methodology to derive CSH and estimate grassland biomass. This study evaluated 3D point clouds derived from a terrestrial laser scanner (TLS) and an unmanned aerial vehicle (UAV)-borne structure from motion (SFM) approach for grassland biomass estimation over three grasslands with different composition and management practice in northern Hesse, Germany. TLS data, UAV-borne images and reference biomass data were collected two days before each harvest from the selected grasslands in 2017. Three levels of linear empirical models (i.e. sampling date-specific, grassland-specific and global) were developed to estimate grasslands fresh and dry biomass using CSH derived from TLS and SFM 3D point clouds. The aforementioned three level models resulted in an average nRMSE of 17.2%, 25.3%, and 28.7% respectively for the grassland dry biomass estimation based on CSH from TLS data. Similarly, models based on CSH from SFM data estimated dry biomass with somewhat higher average nRMSE of 19.5%, 27.1%, and, 36.2% respectively. In general, models with 3D point clouds from UAV-borne SFM was slightly outperformed by models with TLS point cloud data. This study also identified that the utilisation of UAV-borne SFM developed digital terrain model as a reference layer to derive CSH could enhance the performance of the models with SFM data. Furthermore, the performance of the biomass estimation models was affected by both species richness and sward heterogeneity of the grasslands. Overall, these results disclosed the potential of 3D point cloud derived from RS for estimating field-scale grassland biomass.

Pea necrotic yellow dwarf virus (PNYDV) ist ein neuer Nanovirus in Mitteleurope, der verschiedene Leguminosen befallt und persistent durch Blattlause ubertragen wird. In dieser on-farm Studie untersuchten wir sechs Ackerbohnen-Felder mit PNYDV-symptomatischen Nestern mittels multispektraler Drohnen-Aufnahmen, die mit ground-truth Pflanzenwachstumsparametern korreliert werden. Symptomatische Pflanzen zeigten einen signifikanten Ruckgang der Sprosslange, oberirdische Trockenmasse, Hulsenzahl sowie Anzahl und Trockenmasse N-fixierender Wurzelknollchen. Im Gegensatz zu gesunden Pflanzen hatten symptomatische Pflanzen keine rosa oder rot gefarbten Knollcheninhalte, stattdessen hohere Anteile zerfallender und inaktiver Knollchen. Verschiedene Indices der multispektralen Aufnahmen zeigten einen deutlichen Unterschied in der spekteralen Information von symptomatischem Infektionsherd gegenuber der umgebenden Referenz.

3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

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.

Urbanisation is a global trend rapidly transforming the biophysical and socioeconomic structures of metropolitan areas. To better understand (and perhaps control) these processes, more interdisciplinary research must be dedicated to the rural–urban interface. This also calls for a common reference system describing intermediate stages along a rural–urban gradient. The present paper constructs a simple index of urbanisation for villages in the Greater Bangalore Area, using GIS analysis of satellite images, and combining basic measures of building density and distance. The correlation of the two parameters and discontinuities in the frequency distribution of the combined index indicate highly dynamic stages of transformation, spatially clustered in the rural–urban interface. This analysis is substantiated by a qualitative assessment of village morphologies. The index presented here serves as a starting point in a large, coordinated study of rural–urban transitions. It was used to stratify villages for random sampling in order to perform a representative socioeconomic household survey, along with agricultural experiments and environmental assessments in various subsamples. Later on, it will also provide a matrix against which the results can be aligned and evaluated. In this process, the measures and classification systems themselves can be further refined and elaborated.

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) (R2CV = 0.52) or simulated WorldView2 (WV2) (R2CV = 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 (R2CV = 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.

Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414–2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson’s diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 m × 4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400–700 nm) and to vegetation structural properties, such as above-ground biomass (700–1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity.

Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m x 4 m grassland) plots on the Baltic island of Ol and was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m x 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R-2 values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape. (C)2016 Elsevier Ltd. All rights reserved.

The present study aimed to test a mobile device equipped with ultrasonic and spectral sensors for the assessment of biomass from diverse pastures and to compare its prediction accuracy to that from static measurements. Prediction of biomass by mobile application of sensors explained > 63 % of the variation in manually determined reference plots representing the biomass range of each paddock. Accuracy of biomass prediction improved with increasing grazing intensity. A slight overestimation of the true values was observed at low levels of biomass, whereas an underestimation occurred at high values, irrespective of stocking rate and years. Prediction accuracy with a mobile application of sensors was always lower than when sensors were applied statically. Differences between mobile and static measurements may be caused by position errors, which accounted for 8.5 cm on average. Beside GPS errors (±1–2 cm horizontal accuracy and twice that vertically), position inaccuracy predominantly originated from undirected vehicle movements due to heaps and hollows on the ground surface. However, the mobile sensor system in connection with biomass prediction models may provide acceptable prediction accuracies for practical application, such as mapping. The findings also show the limits even sophisticated sensor combinations have in the assessment of biomass of extremely heterogeneous grasslands, which is typical for very leniently stocked pastures. Thus, further research is needed to develop improved sensor systems for supporting practical grassland farming.

As a consequence of agricultural intensification, large areas of species-rich grasslandshave been lost and farmland biodiversity has declined. Previous studies have shownthat the continuity of grazing management can have a significant influence on theenvironmental conditions and the levels of plant species diversity in grasslandhabitats. The preservation of species-rich grasslands has become a high conservationpriority within the European Union and the mapping of grazed grassland vegetationacross wide areas has been identified as a central task for biodiversity conservation inagricultural landscapes. The fact that detailed field inventories of plant communitiesare time-consuming may limit the spatial extent of grassland habitat surveys. Ifremote sensing data are able to identify grassland sites characterised by differentenvironmental conditions and plant species diversity, then field sampling efforts couldbe directed towards sites that are of potential conservation interest.In the thesis, I have examined the potential of hyperspectral and multispectral remotesensing imagery to map grassland vegetation at detailed scales in dry grazed grasslandhabitats. Fieldwork included the recording of vascular plant species andenvironmental variables in grasslands plots representing three age-classes within anarable-to-grassland succession in an agricultural landscape on the Baltic island ofOland (Sweden). Remotely sensed data were acquired with the help of two airborneHySpex hyperspectral spectrometers (415–2501 nm) and by the multispectralWorldView-2 satellite.The results of the thesis show that the soil nutrient and moisture status withingrassland plots influenced the hyperspectral reflectance. Hyperspectral data had theability to classify grassland plots into different age-classes. Hyperspectral reflectancemeasurements could be used to predict plant indicator values for nutrient and soilmoisture in grassland plots. Prediction models developed from hyperspectral datawere successfully used to assess levels of plant species diversity (species richness andSimpsons’s diversity). In addition, between-plot dissimilarities in the satellite spectralreflectance were shown to be related to between-plot dissimilarities in the speciescomposition in old grassland sites.The findings of the thesis demonstrate that remote sensing data are capable ofcapturing detailed-scale information that discriminates between grassland plantcommunities representing different environmental conditions and levels of plantspecies diversity. The results suggest that remote sensing data may have the ability foruse as a decision-support tool to help conservation planners identify grassland habitatsin agricultural landscapes that are of high conservation interest.

Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm) remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden) and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77) than one using the full set of wavebands (77%, Kappa statistic value = 0.65). Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages.

Plant species beta diversity is influenced by spatial heterogeneity in the environment. This heterogeneity can potentially be characterised with the help of remote sensing. We used WorldView-2 satellite data acquired over semi-natural grasslands on The Baltic island of Öland (Sweden) to examine whether dissimilarities in remote sensing response were related to fine-scale, between-plot dissimilarity (beta diversity) in non-woody vascular plant species composition within the grasslands. Fieldwork, including the on-site description of a set of 30 2 m × 2 m plots and a set of 30 4 m × 4 m plots, was performed to record the species dissimilarity between pairs of same-sized plots. Spectral data were extracted by associating each plot with a suite of differently sized pixel windows, and spectral dissimilarity was calculated between pairs of same-sized pixel windows. Relationships between spectral dissimilarity and beta diversity were analysed using univariate regression and partial least squares regression. The study revealed significant positive relationships between spectral dissimilarity and fine-scale (2 m × 2 m and 4 m × 4 m) between-plot species dissimilarity. The correlation between the predicted and the observed species dissimilarity was stronger for the set of large plots (4 m × 4 m) than for the set of small plots (2 m × 2 m), and the association between spectral and species data at both plot scales decreased when pixel windows larger than 3 × 3 pixels were used. We suggest that the significant relationship between spectral dissimilarity and species dissimilarity is a reflection of between-plot environmental heterogeneity caused by differences in grazing intensity (which result in between-plot differences in field-layer height, and amounts of biomass and litter). This heterogeneity is reflected in dissimilarities in both the species composition and the spectral response of the grassland plots. Between-plot dissimilarities in both spectral response and species composition may also be caused by between-plot variations in edaphic conditions. Our results indicate that high spatial resolution satellite data may potentially be able to complement field-based recording in surveys of fine-scale species diversity in semi-natural grasslands.