• Rural Digital Europe
• Research dataclose
• Other research productsclose

Eddy covariance-Rawdata 2023 Hordorf DoY032 - DoY059 (February 2023), acquisistion of CSAT3 and LI7500 in 2.3m height with eddymeas, 20Hz, slt and dia files, meta-information in cfg-files file: HD2023_Eddy-Data_DOY032-DOY059.zip more files available in the same repository

L'ancien agriculteur, éloigné aujourd'hui du Groenland, détaille le fonctionnement de son ancienne ferme, le contexte écosystémique, les changements à l’œuvre, ainsi que la vie locale dans le fond du fjord d'Eric le Rouge. Entretien avec un ancien fermier de Qassiarsuk, qui détaille ce qu'est une exploitation agricole au sud du Groenland, ses atouts et ses contraintes, le fonctionnement annuel de la ferme, les autres sources de revenus, etc.

Entretien ethnographique avec une habitante de Quéménès au sujet de l'alimentation sur l'île.

Entretien ethnographique avec deux femmes douarnenistes, descendantes d'une famille d'agriculteurs sur l'île Tristan (DZ).

Aim: To map and interpret floristic and geoecological patterns across the Amazon basin by combining extensive field data with basin-wide Landsat imagery and climatic data Location: Amazonia Taxon: Ground truth data on ferns and lycophytes; remote sensing results reflect forest canopy properties Methods: We used field plot data to assess main ecological gradients across Amazonia and to relate floristic ordination axes to soil base cation concentration, CHELSA climatic variables and reflectance values from a basin-wide Landsat image composite with generalized linear models (GLM). Ordination axes were then predicted across all Amazonia using Landsat and CHELSA, and a regional subdivision was obtained using k-medoid classification. Results: The primary floristic gradient was strongly related to base cation concentration in the soil, and the secondary gradient to climatic variables. The Landsat image composite revealed a tapestry of broad-scale variation in canopy reflectance characteristics across Amazonia. Ordination axis scores predicted using Landsat and CHELSA variables produced spatial patterns consistent with existing knowledge on soils, geology and vegetation, but also suggested new floristic patterns. The clearest dichotomy was between central Amazonia and the peripheral areas, and the available data supported a classification into at least eight subregions. Main conclusions Landsat data are capable of predicting soil-related species compositional patterns of understory ferns and lycophytes across the Amazon basin with surprisingly high accuracy. Although the exact floristic relationships may differ among plant groups, the observed ecological gradients must be relevant for other plants as well, since surface reflectance recorded by satellites is mostly influenced by the tree canopy. This opens exciting prospects for species distribution modelling, conservation planning, and biogeographical and ecological studies on Amazonian biota. Our maps provide a preliminary geoecological subdivision of Amazonia that can now be tested and refined using field data of other plant groups and from hitherto unsampled areas. Environmental and fern species data for Amazonian plotsSpecies and soil data were collected in the field by the researchers mentioned as data providers for each plot. Reflectance data come from a Landsat TM/ETM+ image composite based on images acquired in 2000-2009 and processed using R code written by Jasper Van doninck. CHELSA and SRTM data come from respective online portals. See README file for explanation of the data contained in each column.Tuomisto_etal_JBI-18-0631_data.csv

The branching pattern of a tree determines the efficiency of light interception and carbon assimilation. Pruning can modify the branching pattern, because of changes in physiological and environmental conditions, and ultimately pruning can have major effects on yield. For one of the major tropical tree crops, cocoa (Theobroma cacao), very little is known about branching response to pruning. To address this knowledge gap, we performed a pruning experiment on young cocoa trees in Côte d’Ivoire. We applied five treatments: two heading treatments (the removal of the terminal apex or 66% of a primary branch) and two thinning treatments (the removal of 1 or 2 primary branches) and one unpruned control. The secondary-branching pattern of the primary branches was described by the number, position, and length of secondary branches right after pruning, and the same observations were repeated after a cycle of leaf production. The probability of branching and the length of secondary branches along a primary branch, in pruned and unpruned conditions, was analyzed using generalized mixed effect models. In unpruned conditions, the probability of secondary-branch presence was higher towards the middle of the primary branches and lower at the extremes. Secondary-branch length decreased going from the base to the tip of a primary branch. After one cycle of leaf production, new secondary branches emerged preferentially on the distal section of a primary branch, but probability of branch emergence was reduced by the presence of other secondary branches. Pruning increased the probability of branch emergence mostly towards the tip of a primary branch, with heavy heading having the strongest effect. By contrast, heavy thinning increased branch emergence also toward the base of the primary branch. Our results can be applied to improve formation pruning, as this may trigger branching in different parts of the crown, depending on the form of pruning. Our study also assists the development of three-dimensional tree models that could further our understanding of the impact of pruning on cocoa growth and productivity.

This dataset encompasses in-situ chlorophyll-a (Chl-a) concentrations and co-located atmospherically corrected pixel values from Landsat 5, Landsat 7, Landsat 8, and Sentinel-2 satellites for Western Lake Ontario between 2000 and 2022. The dataset includes values for various spectral bands and semi-empirical Chl-a retrieval indices applied to the atmospherically corrected images. MATLAB codes are provided for data preprocessing, regression analysis, and visualizations such as heatmaps, enabling users to investigate relationships between Chl-a concentrations and remote sensing data.

The River Sediment Database (RiverSed) database contains Total Suspended Sediment (TSS) concentrations derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. TSS concentrations represent reach integrated medians concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected ithin each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). Files: 1) Metadata (RiverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (riverSed_usa_v1.0.txt). Table of TSS concentration and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv). 6) Matchup database with extended metadata on locations and in-situ data (Aquasat_TSS_v1.0.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_v1.csv) 8) The xgboost model that can make TSS predictions over inland waters in USA with 9 Landsat bands/band combinations (final_model_xgbLinear_v1.rds). The model can be loaded in R. Future version will have the xgb object to be compatible across languages.