Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha-1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.

10.1002/rse2.228

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A Systematic Procedure for Land Use Land Cover Mapping Using Interpretable Machine Learning Model with Satellite Data

Satya Prakash Jayan Wijesingha Sunil Nautiyal M. Wachendorf

Satellite imagery and machine learning (ML) techniques for land use land cover (LULC) mapping have become prevalent in remote sensing (RS). However, these developed models usually need comprehensive explanations of model functioning and limitations. Therefore, this study aims to establish a systematic ML model training process for LULC mapping, encompassing model interpretation and performance evaluation of the new data. The study compares three ML models - Random Forest, extreme gradient boosting (XGB), and light gradient boosting - for classifying a LISS IV satellite image. The optimal algorithm was selected based on random and spatial cross-validation (CV) outcomes, and model interpretation was achieved using Shapely (SHAP) values. Dissimilarity index explicated model predictions on new data. Results indicate XGB's superior accuracy across both CV methods. SHAP values effectively explained data behaviour in XGB model predictions. Ultimately, this systematic workflow established a standard for LULC mapping with RS imagery using ML.

Land Cover

10.1109/ingarss59135.2023.10490409

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Heavy Metals and Polycyclic Aromatic Hydrocarbons in Urban Leaf Litter Designated for Combustion

Energies (2017)

Meike Nitsche Nodirjon Nurmatov Frank Hensgen M. Wachendorf

Vast amounts of leaf litter have to be disposed of by city administrations. This biomass has the potential for energy conversion, but contamination with pollutants can adversely affect this usage. We investigated leaf litter samples from the city of Kassel by analyzing their heavy metal and polycyclic aromatic hydrocarbon (PAH) concentrations. Leaf samples were indeed contaminated with heavy metals and PAHs and contamination was influenced by provenience and collection technique. A simple cleaning system of washing and subsequent mechanical dewatering significantly reduced heavy metal concentration. Regression models were developed for contamination with heavy metals which showed that contamination could be successfully estimated using the total ash content of the sample as a predictor, with an R2 of up to 0.77. It can be concluded that leaf litter from cities is a possible feedstock for energetic conversion, provided a cleaning step is applied.

Litter

Polycyclic aromatic hydrocarbon

Poultry litter

10.3390/en10030298

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Top losses of corn silage sealed with oxygen barrier film.

Grassland in a changing world. Proceedings of the 23rd General Meeting of the European Grassland Federation, Kiel, Germany, 29th August - 2nd September 2010 (2010)

Fernanda Carvalho Basso Ricardo Andrade Reis Marcos Augusto dos Reis Nogueira Thiago Fernandes Bernardes A. P. T. P. Roth

Silage

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Influence of drought stress and fertilisation on carbon isotopes as indicators of water use of grassland differing in diversity.

Grassland in a changing world. Proceedings of the 23rd General Meeting of the European Grassland Federation, Kiel, Germany, 29th August - 2nd September 2010. (2010)

N. Wrage Ludwig J. Gauckler E. Steep Frank Küchenmeister Kai Küchenmeister

Methods • Permanent grassland (> 14 years old), Relliehausen, Solling, Germany (Fig. 2) • Hierarchical design: diversity = main plot, fertilisation and drought stress on subplots, n = 4 • Different diversity: herbicide treatment against dicots in autumn 2006 (‚non diverse‘; untreated: ‚diverse‘) • Drought: rainout shelters established for five weeks from beginning of May 2009 • Fertilisation: 90 kg N/ha, calcium ammonium nitrate, in April 2009 • Sampling: diversity, soil water content at harvest, biomass, δ13C

Water Stress

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THE BENEFIT OF SPECTRAL AND POINT-CLOUD DATA FOR HERBAGE YIELD AND QUALITY ASSESSMENT OF GRASSLANDS

The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences (2019)

M. Wachendorf Thomas Astor

Abstract. Grassland represents the largest single agricultural vegetation in Germany and provides a multitude of ecosystem services. Timely and accurate information about herbage yield and quality is essential for an efficient use of resources, e.g. to be able to match the actual available feed with a demand of animals or with other industrial uses. Grasslands frequently exhibit small-scale botanical and structural heterogeneity with pronounced spatio-temporal dynamics. These features present particular challenges for sensor applications, which, apart from limitations posed by high costs and low temporal and spatial resolutions of many available remote sensing (RS) systems, may be the reason for so far little commercial applications of RS in practical grassland farming. This paper considers recent developments in the use of spectral and point-cloud data for herbage yield and quality assessment of grasslands. Former research showed that single sensor systems mounted on unmanned aerial vehicles produce similar prediction errors in crude protein or acid detergent fibre concentrations as proximal sensing tools (e.g. field spectroscopy). However, further improvements are needed. Beside improvements of single sensor types, the development of systems with complementary sensors is seen as a promising research area. It will help to overcome the limitations of single sensors and provide better information about herbage yield and quality. From an agronomic point of view, thematic maps of farm fields are suggested as the central outcome of RS and data analysis. These maps are representing the relevant grassland features and therefore can be used as low-cost, appropriate and timely information to support farmers’ decision-making.

Thematic map

Precision Agriculture

10.5194/isprs-archives-xlii-2-w16-267-2019

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Biomasseabschätzung im ökologischen Feldfutterbau mittels drohnengestützten RGB-Aufnahmen

Esther Grüner Thomas Möckel M. Wachendorf

Monitoringansatze im okologischen Futterbau, die den Biomasseertrag uber den gesamten Betrieb abschatzen, ermoglichen es dem Landwirt, standortspezifische Managemententscheidungen zu treffen. Die pflanzliche Biomasse und ihre Hohe korrelieren miteinander, wodurch die Hohe des Bestandes zur Ertragsprognose verwendet werden kann. Fernerkundliche Messmethoden sind nutzlich, um raumliche Informationen uber die Pflanzenhohe auch uber grosere Flachen zu erhalten. In dieser Studie wurde eine RGB-Kamera (rot, grun, blau), die an einem kleinen unbemannten Luftfahrzeug (Drohne) angebracht war, zur Ertragsabschatzung in einem okologischen Futterbauexperiment verwendet. Das Experiment wurde 2016 mit zwei Leguminosen-Grasmischungen (Klee- und Luzernegras) sowie mit ihren reinen Leguminosen und Grasern angelegt. In der Vegetationsperiode des nachsten Jahres wurden jede zweite Woche RGB-Aufnahmen durchgefuhrt. Parallel dazu wurden Referenzdaten fur die Biomasseertrage und manuelle Hohenmessungen erstellt. 3D-Modelle, die aus den RGB-Aufnahmen erzeugt wurden, wurden zur Berechnung der Bestandeshohe verwendet. Der Biomasseertrag wurde mit zufriedenstellenden Ergebnissen vorhergesagt.

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Continuous and rotational grazing system with horses: effects on gorse production understorey developed under Pinus radiata stand.

Grassland in a changing world. Proceedings of the 23rd General Meeting of the European Grassland Federation, Kiel, Germany, 29th August - 2nd September 2010 (2010)

M. R. Mosquera‐Losada Rabia Mouhbi M. P. González-Hernández A. Boubaker A. Rigueiro‐Rodríguez

Pinus radiata

Understory

Radiata

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Citations (3)