HERDECT -Utilisation des données satellites Sentinel-2 pour quantifier la production d'herbe

Grazed grass is the cheapest feed item in a feed ration. Good grass management requires knowledge ofthe available amount of grass. Simplifying and automating these grass measurements can help inmaintaining or even developing grazing. The HERDECT project aims to build methods for estimating thequantity of grass from remote sensing tools (remote acquisition) and to estimate their operationalfeasibility. This research presents a comparison of regression methods on several variables extractedfrom Sentinel-2 images with field data. The goal is to develop predictive models of grass height andbiomass. A set of experimental field data was collected on 18 sites mainly located in the western part ofmainland France. These data were used to assess the reliability of the models. The biomass and grassheight estimates obtained with satellites were compared with field data from HERDECT project farms and"grass growth" network. The results showed a high forecast quality for common use by farmers.L’herbe pâturée est l’aliment disponible le moins coûteux dans les exploitations d’élevage. L’optimisationde la gestion de l’herbe passe entre autres par une connaissance des quantités disponibles. Afin desimplifier et d’automatiser ces mesures d’herbe, et ainsi contribuer au maintien voire au développementdu pâturage, le projet HERDECT s’est attaché à construire des méthodes d’estimation de la biomassedes prairies à partir d’outils de télédétection (d’acquisition à distance) et à en estimer la faisabilitéopérationnelle. Cette recherche présente une comparaison de méthodes de régression sur plusieursvariables extraites des images Sentinel-2 avec des données terrains afin de développer des modèles deprévision de hauteur d’herbe et de biomasse sur pied. Un ensemble de données expérimentales deterrain, collectées sur 18 sites majoritairement situés dans la partie Ouest de la France métropolitaine, aété utilisé pour évaluer la capacité des modèles produits à estimer la hauteur d’herbe et la biomasse desprairies. Les estimations biomasses et hauteurs d’herbe obtenues grâce au satellite ont été comparéesaux données terrain issues des fermes du projet HERDECT et du réseau « pousse de l’herbe ». Lesrésultats présentés montrent une bonne qualité de la prévision utile pour un usage de masse

Integrating biodiversity, remote sensing, and auxiliary information for the study of ecosystem functioning and conservation at large spatial scales

Assessing patterns and processes of plant functional, taxonomic, genetic, and structural biodiversity at large scales is essential across many disciplines, including ecosystem management, agriculture, ecosystem risk and service assessment, conservation science, and forestry. In situ data housed in databases necessary to perform such assessments over large parts of the world are growing steadily. Integrating these in situ data with remote sensing (RS) products helps not only to improve data completeness and quality but also to account for limitations and uncertainties associated with each data product. Here, we outline how auxiliary environmental and socioeconomic data might be integrated with biodiversity and RS data to expand our knowledge about ecosystem functioning and inform the conservation of biodiversity. We discuss concepts, data, and methods necessary to assess plant species and ecosystem properties across scales of space and time and provide a critical discussion of outstanding issues

Assessment of the spatial variability in tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones

Hay poca información disponible sobre el grado de variabilidad dentro del campo de la producción potencial de alto agropiro alargado (Thinopyrum ponticum) de forraje bajo secano condiciones. El objetivo de este estudio fue caracterizar la variabilidad espacial de la biomasa acumulada (AB) sin limitaciones nutricionales a través de índices de vegetación, y luego utilizar esta información para determinar el potencial de gestión zonas. Se eligió un tamaño de celda de la cuadrícula 27- × -27-m y 84 zonas de muestreo de biomasa (BSA), cada 2 m2 de superficie, fueron georreferenciados. Fertilizantes de nitrógeno y fósforo se aplicaron después de un corte inicial a 3 cm de altura. A 500 ° C día, el AB de cada área de muestreo, se recogido y evaluado. La variabilidad espacial de AB fue estimada con mayor precisión utilizando la Diferencia Normalizada Índice de vegetación (NDVI), calculada a partir de Landsat 8 imágenes obtenidas el 24 de noviembre 2014 (NDVInov) y 10 de diciembre 2014 (NDVIdec) porque el potencial AB fue altamente asociada con NDVInov y NDVIdec (r2 = 0,85 y 0,83, respectivamente). Estas modelos de datos entre el potencial de AB y NDVI eran evaluadas por el error cuadrático medio (RMSE) y relativa error cuadrático medio (RRMSE). Este último coeficiente fue 12 y 15% para NDVInov y NDVIdec, respectivamente. Correlación espacial potencial de AB y NDVI se cuantificaron con semivariogramas. La dependencia espacial de AB fue baja. Seis clases de NDVI se analizaron para la comparación, y se establecieron dos zonas de manejo (MZ) con ellos. Con el fin de evaluar si el método NDVI permite nos delimitar MZ con diferentes rendimientos alcanzables, la AB estimada para estos MZ se compararon a través de una prueba de ANOVA. El potencial AB tenía diferencias significativas entre MZ. Basándose en estos resultados, se puede concluir NDVI que obtuvo de LANDSAT 8 imágenes pueden ser utilizado de forma fiable para la creación de MZ en suelos bajo permanente pasturas dominadas por agropiro alargado.Little information is available on the degree of within-field variability of potential production of Tall wheatgrass (Thinopyrum ponticum) forage under unirrigated conditions. The aim of this study was to characterize the spatial variability of the accumulated biomass (AB) without nutritional limitations through vegetation indexes, and then use this information to determine potential management zones. A 27-×-27-m grid cell size was chosen and 84 biomass sampling areas (BSA), each 2 m2 in size, were georeferenced. Nitrogen and phosphorus fertilizers were applied after an initial cut at 3 cm height. At 500 °C day, the AB from each sampling area, was collected and evaluated. The spatial variability of AB was estimated more accurately using the Normalized Difference Vegetation Index (NDVI), calculated from LANDSAT 8 images obtained on 24 November 2014 (NDVInov) and 10 December 2014 (NDVIdec) because the potential AB was highly associated with NDVInov and NDVIdec (r2 = 0.85 and 0.83, respectively). These models between the potential AB data and NDVI were evaluated by root mean squared error (RMSE) and relative root mean squared error (RRMSE). This last coefficient was 12 and 15 % for NDVInov and NDVIdec, respectively. Potential AB and NDVI spatial correlation were quantified with semivariograms. The spatial dependence of AB was low. Six classes of NDVI were analyzed for comparison, and two management zones (MZ) were established with them. In order to evaluate if the NDVI method allows us to delimit MZ with different attainable yields, the AB estimated for these MZ were compared through an ANOVA test. The potential AB had significant differences among MZ. Based on these findings, it can be concluded that NDVI obtained from LANDSAT 8 images can be reliably used for creating MZ in soils under permanent pastures dominated by Tall wheatgrass.EEA BalcarceFil: Cicore, Pablo Leandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Sousa, Adelia. University of Évora, Escola de Ciências e Tecnologia. Instituto de Ciências Agrárias e Ambientais Mediterrânicas; Portugal. Centro de Inovação em Tecnologias de Informação; PortugalFil: Costa, Jose Luis. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Marques da Silva, José Rafael. Centre for Interdisciplinary Development and Research on Environment, Applied Management and Space; Portugal. University of Évora, Escola de Ciências e Tecnologia. Instituto de Ciências Agrárias e Ambientais Mediterrânicas; Portugal. Centro de Inovação em Tecnologias de Informação; PortugalFil: Serrano, João. University of Évora, Escola de Ciências e Tecnologia. Instituto de Ciências Agrárias e Ambientais Mediterrânicas; PortugalFil: Shahidian, Shakib. University of Évora, Escola de Ciências e Tecnologia. Instituto de Ciências Agrárias e Ambientais Mediterrânicas; Portuga