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  • Rural Digital Europe
  • Research data
  • Research software
  • 2013-2022

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  • Open Access
    Authors: 
    Mondal, Pinki; Walter, Matthew; Miller, Jarrod; Epanchin-Niell, Rebecca; Yawatkar, Vishruta; Nguyen, Elizabeth; Gedan, Keryn; Tully, Katherine;
    Publisher: Zenodo

    Abstract: Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below: Raster value Land cover/use category 1 Forest 2 Marsh 3 Salt patch 4 Built 5 Open water 6 Farmland 7 Bare soil 8 Other vegetation Input Data: These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE. Methods: NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel. NDVI = (Near-infrared – Red) / (Near-infrared + Red) NDWI = (Green – Near-infrared) / (Green + Near-infrared) SI = (256 – Blue) * (256 + Blue) In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel. EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1) A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands. Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits. Accuracy assessment: A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below. Delaware 2013 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 88.46% 90.89% 0.90 86.37% 0.83 Marsh 84.03% 80.13% 0.82 Salt patch 97.02% 71.18% 0.82 Built 94.56% 95.06% 0.95 Water 91.01% 96.63% 0.94 Farmland 83.16% 86.19% 0.85 Bare Soil 87.70% 87.30% 0.88 Other Vegetation 82.46% 84.20% 0.83 Delaware 2017 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 95.07% 90.30% 0.93 91.37% 0.90 Marsh 88.86% 92.44% 0.91 Salt patch 91.82% 85.59% 0.89 Built 87.58% 93.54% 0.90 Water 92.68% 90.48% 0.92 Farmland 91.61% 93.61% 0.93 Bare Soil 95.67% 87.67% 0.91 Other Vegetation 91.16% 90.24% 0.91 Maryland 2011 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 88.32% 90.97% 0.90 87.20% 0.85 Marsh 87.14% 82.08% 0.85 Salt patch 96.74% 78.76% 0.87 Built 89.02% 88.50% 0.89 Water 92.74% 96.10% 0.94 Farmland 84.31% 89.83% 0.87 Bare Soil 87.53% 90.05% 0.89 Other Vegetation 86.11% 80.57% 0.83 Maryland 2017 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 88.66% 88.66% 0.89 87.34% 0.84 Marsh 87.65% 88.35% 0.88 Salt patch 93.29% 68.30% 0.79 Built 93.44% 86.92% 0.90 Water 92.36% 92.36% 0.92 Farmland 83.84% 94.55% 0.89 Bare Soil 92.86% 82.61% 0.87 Other Vegetation 86.56% 76.00% 0.81 Virginia 2012 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 84.65% 91.18% 0.88 86.88% 0.84 Marsh 87.03% 84.39% 0.86 Salt patch 97.67% 72.41% 0.83 Built 90.97% 86.24% 0.89 Water 94.17% 87.39% 0.91 Farmland 86.87% 91.81% 0.89 Bare Soil 85.82% 83.04% 0.84 Other Vegetation 83.60% 80.59% 0.82 Virginia 2016 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 84.65% 86.00% 0.85 85.83% 0.83 Marsh 86.25% 90.72% 0.88 Salt patch 90.61% 62.12% 0.74 Built 88.70% 77.72% 0.83 Water 92.12% 84.41% 0.88 Farmland 85.17% 90.36% 0.88 Bare Soil 83.30% 88.54% 0.86 Other Vegetation 84.49% 84.17% 0.84 While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface. Data format: The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis. Datasets for download: Two zipped data layers for Delaware: DE_3counties_2013 DE_3counties_2017 These data layers cover 3 counties: Kent, New Castle, Sussex. Two zipped data layers for Mary Funding acknowledgment: This research was made possible by the National Science Foundation EPSCoR Grant No. 1757353 and the State of Delaware. This work was also supported by the National Aeronautics and Space Administration EPSCoR Grant No. DE-80NSSC20M0220, and the Delaware Space Grant College and Fellowship Program (NASA Grant 80NSSC20M0045). We acknowledge partial support provided by the National Fish and Wildlife Foundation, the State of Maryland, and Harry R. Hughes Center for Agro-Ecology.

  • Chinese
    Authors: 
    Ming, Feng Yi; Kun, Qiao; Ang, Feng Shi; Lei, Xi; Zhao, Qi; Lan, Lan;
    Publisher: Science Data Bank

    This dataset is based on the GEE remote sensing cloud platform, using the LANDSAT satellite vegetation growing season (April-October) images from 1990 to 2021 as the data source, and extracting the vegetation cover results of the Ring Tarim Basin for 7 periods during 1990-2021. This dataset contains the distribution data of FVC of vegetation growing season within the Ring Tarim Basin from 1990 to 2021, and the data are stored in a folder named by year for each year, with 7 periods of data. The data are named as FVC+year, and the geographic coordinate system is WGS84. The data type of this dataset is GeoTIFF raster data, the spatial resolution is 30 m, and the data volume is about 11.2 GB. The data can be read, viewed, analyzed, processed and applied in common GIS and remote sensing platforms (such as ArcGIS, QGIS, ENVI, etc.).

  • Open Access
    Authors: 
    Polley, Herbert; Jones, Katherine; Kolodziejczyk, Chris; Fay, Philip;
    Publisher: Zenodo

    Grassland production is sensitive to both precipitation and plant N accumulation and utilization, such that change in one variable influences grassland response to the second variable. We investigated effects of interannual variation in precipiation on the response of 'community'-scale values of relative growth rate (RGR) to two multiplicative components of RGR, nitrogen productivity (NP; rate of change in biomass/plant N), an index of N utilization efficiency, and plant N concentration ([N]), in two grassslands in Texas, USA. Grasslands included a planted mixture of perennial grass and forb species and a monoculture of the perennial C4 grass Panicum vigatum that was invaded by multiple plant species. RGR and its N components were measured at the spatial scale of 7-m diameter circular patches near the spring peak in mixture biomss during each of 5 years. We found that RGR varied substantially among patches and years and between the planted mixture and monoculture. RGR variation was strongly correlated with variation in NP. Precipitation during the 3 months prior to RGR measurement mediated that RGR response to NP by altering the correlation between NP and [N] in both grasslands. Reduced precipitation led to more negative NP-[N] correlation coefficients, which reduced proportional change in RGR per change in NP by as much as 30% even in the absence of a precipitation effect on means of RGR and NP. Our results highlight an under-appreciated aspect of the pervasive role of precipitation in grassland growth that was mediated via change in the growth benefit derived from plant N. We used remote sensing techniques to calculate relative growth rate (RGR), nitrogen productivity (NP), and plant N concentration ([N]) at the scale of 7-m diameter circular patches (n = 104) in each of two grassland types (mixture of perennial grass and forb species, planted monoculture of the grass switchgrass). 'Community'-scale values RGR and its N components (NP, [N]) were calculated near the spring biomass peak in each of 5 years. We examined correlations among spatial variation in RGR, NP, and [N] in each grassland as influenced by interannual variation in precipitation.

  • Open Access
    Authors: 
    ONOJEGHUO, ALEX; Onojeghuo, Ajoke Ruth;
    Publisher: 4TU.ResearchData
    Country: Netherlands

    This is a repository of reference data used for the study.

  • Open Access English
    Authors: 
    Navarro-Miró, David; Blanco-Moreno, José M.; Ciaccia, Corrado; Testani, Elena; Iocola, Ileana; Depalo, Laura; Burgio, Giovanni; Lakkenborg Kristensen, Hanne; Hefner, Margita; Tamm, Kalvi; +17 more
    Publisher: Dryad

    1. Although organic farming was originally promoted as an alternative farming system to address agronomic, environmental, and ecological issues, its conventionalisation has led to an intensification and specialisation of production. In light of this, several studies have questioned the environmental benefits of organic farming as well as its agronomic viability. Thus, there is a need to improve organic vegetable systems to reduce their environmental impact without affecting their productivity. To tackle this challenge, European farmers and researchers have recently started to focus on agroecological service crops (ASCs). However, few studies have simultaneously evaluated the agronomic, environmental, and ecological aspects of ASC management under different European pedo-climatic conditions. 2. We evaluated effects of the ASC management strategies: no-till roller crimping (NT-RC) and green manuring (T-GM) on cropping system performance using agronomic, environmental, and ecological indicators, to exemplify the need for multidimensional analysis to understand management implications for addressing environmental and agronomic challenges. We combined the results from eleven organic vegetable field trials conducted in seven European countries over a period of two years to test for general trends. 3. Our results provide solid evidence that NT-RC management across different pedo-climatic conditions in Europe enhances the activity density of ground and rove beetles, and improves both the potential energy recycling within the system and weed control. However, in NT-RC plots lower cash crop yield and quality, energetic efficiency of production, and activity density of spiders was observed compared to T-GM. 4. Synthesis and applications: Multidimensional analyses using agronomic, environmental, and ecological indicators are required to understand the implications of agricultural management in agroecosystem functioning. Introducing agroecological service crops combined with the use of no-till roller crimping is a promising strategy for improving agronomic performance (e.g., fewer weeds) and reducing environmental (e.g., increasing the potentially recyclable energy), and ecological (e.g., enhancing the activity density of beneficial taxa such as ground and rove beetles) impacts. However, our study also indicates a need for agronomic and environmental improvements while promoting a wider acceptance of this strategy.29-Nov-2021 -- Dataset specifications: This dataset gathers data from 11 organic arable vegetable field trials located in Belgium (BE), Denmark (DK), Estonia (EE), France (FR), Italy (IT), Slovenia (SI), and Spain (ES). Two parallel field experiment types were carried out during two crop cycles. Field experiment type A (FtA) involved the introduction of cold-rainy season ASCs into the crop rotation, followed by a spring-summer cash crop. Field experiment type B (FtB) was performed only at the IT and ES locations where the Mediterranean climatic conditions enabled introduction of the ASCs in the warm-dry season (i.e., summer), followed by the transplantation of an autumn-winter cash crop. This dataset contains: Ecological and environmental indicators: Activity density of ground (Carabidae) and rove (Staphylinidae) beetles and spiders (Araneae); Beta-glucosidase enzyme activity assessment; Nitrogen leaching potential measured by soil mineral nitrogen assessment at cash crop harvest; and the potentially recyclable energy use efficiency indicator (PRE-EUE) (Navarro-Miró, Iocola, et al., 2019). Agronomic indicators: Cash crop marketable yield, and the cash crop quality; The energy efficiency of the marketable production was determined by the energy-use efficiency indicator (M-EUE) (Barut, Ertekin, & Karaagac, 2011). Weed control was analysed by determining weed density (individuals m-2). Information about Repetition/block and sample can be found in Appendix "S1. Trial details". Repetition/block and sample are sometimes not included because the measure under consideration has been taken at higher level (i.e., ASC, termination, plot).

  • Open Access
    Authors: 
    Xiao Zhiqiang;
    Publisher: Zenodo

    The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2019. Please click here to download the MUSES LAI product in 2018. Dataset Characteristics: Spatial Coverage: Global Temporal Coverage: 2019 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 Citation (Please cite this paper whenever these data are used): Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225. Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223. Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318. Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

  • Open Access
    Authors: 
    Xiao Zhiqiang;
    Publisher: Zenodo

    The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. Dataset Characteristics: Spatial Coverage: 180º W – 180º E, 90º S – 90º N Temporal Coverage: 1981 – 2019 Spatial Resolution: 0.05º (approximately 5 km) Temporal Resolution: 1 month Projection: Geographic Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 Citation (Please cite this paper whenever these data are used): Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225. Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223. Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318. Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

  • Open Access
    Authors: 
    Statistics Canada;
    Publisher: Open Data Canada

    Données sur la production des broyages des grains, huiles et tourteaux. Data on the production of the crushing of seed, oil and meal.

  • Open Access
    Authors: 
    Statistics Canada;
    Publisher: Open Data Canada

    Production de certains produits de beurre, Canada et les provinces (en tonnes métriques). Les données sont disponibles sur une base mensuelle. Production of selected butter products, Canada and provinces (in tonnes). Data are available on a monthly basis.

  • Open Access
    Authors: 
    Statistics Canada | Statistique Canada;
    Publisher: Government of Canada

    Fabrication de sous-produits concentrés de lait, Canada et les provinces (en tonnes). Les données sont disponibles sur une base mensuelle. Production of concentrated milk products, Canada and provinces (in tonnes). Data are available on a monthly basis.

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The following results are related to Rural Digital Europe. Are you interested to view more results? Visit OpenAIRE - Explore.
22,555 Research products, page 1 of 2,256
  • Open Access
    Authors: 
    Mondal, Pinki; Walter, Matthew; Miller, Jarrod; Epanchin-Niell, Rebecca; Yawatkar, Vishruta; Nguyen, Elizabeth; Gedan, Keryn; Tully, Katherine;
    Publisher: Zenodo

    Abstract: Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below: Raster value Land cover/use category 1 Forest 2 Marsh 3 Salt patch 4 Built 5 Open water 6 Farmland 7 Bare soil 8 Other vegetation Input Data: These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE. Methods: NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel. NDVI = (Near-infrared – Red) / (Near-infrared + Red) NDWI = (Green – Near-infrared) / (Green + Near-infrared) SI = (256 – Blue) * (256 + Blue) In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel. EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1) A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands. Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits. Accuracy assessment: A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below. Delaware 2013 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 88.46% 90.89% 0.90 86.37% 0.83 Marsh 84.03% 80.13% 0.82 Salt patch 97.02% 71.18% 0.82 Built 94.56% 95.06% 0.95 Water 91.01% 96.63% 0.94 Farmland 83.16% 86.19% 0.85 Bare Soil 87.70% 87.30% 0.88 Other Vegetation 82.46% 84.20% 0.83 Delaware 2017 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 95.07% 90.30% 0.93 91.37% 0.90 Marsh 88.86% 92.44% 0.91 Salt patch 91.82% 85.59% 0.89 Built 87.58% 93.54% 0.90 Water 92.68% 90.48% 0.92 Farmland 91.61% 93.61% 0.93 Bare Soil 95.67% 87.67% 0.91 Other Vegetation 91.16% 90.24% 0.91 Maryland 2011 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 88.32% 90.97% 0.90 87.20% 0.85 Marsh 87.14% 82.08% 0.85 Salt patch 96.74% 78.76% 0.87 Built 89.02% 88.50% 0.89 Water 92.74% 96.10% 0.94 Farmland 84.31% 89.83% 0.87 Bare Soil 87.53% 90.05% 0.89 Other Vegetation 86.11% 80.57% 0.83 Maryland 2017 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 88.66% 88.66% 0.89 87.34% 0.84 Marsh 87.65% 88.35% 0.88 Salt patch 93.29% 68.30% 0.79 Built 93.44% 86.92% 0.90 Water 92.36% 92.36% 0.92 Farmland 83.84% 94.55% 0.89 Bare Soil 92.86% 82.61% 0.87 Other Vegetation 86.56% 76.00% 0.81 Virginia 2012 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 84.65% 91.18% 0.88 86.88% 0.84 Marsh 87.03% 84.39% 0.86 Salt patch 97.67% 72.41% 0.83 Built 90.97% 86.24% 0.89 Water 94.17% 87.39% 0.91 Farmland 86.87% 91.81% 0.89 Bare Soil 85.82% 83.04% 0.84 Other Vegetation 83.60% 80.59% 0.82 Virginia 2016 Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 84.65% 86.00% 0.85 85.83% 0.83 Marsh 86.25% 90.72% 0.88 Salt patch 90.61% 62.12% 0.74 Built 88.70% 77.72% 0.83 Water 92.12% 84.41% 0.88 Farmland 85.17% 90.36% 0.88 Bare Soil 83.30% 88.54% 0.86 Other Vegetation 84.49% 84.17% 0.84 While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface. Data format: The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis. Datasets for download: Two zipped data layers for Delaware: DE_3counties_2013 DE_3counties_2017 These data layers cover 3 counties: Kent, New Castle, Sussex. Two zipped data layers for Mary Funding acknowledgment: This research was made possible by the National Science Foundation EPSCoR Grant No. 1757353 and the State of Delaware. This work was also supported by the National Aeronautics and Space Administration EPSCoR Grant No. DE-80NSSC20M0220, and the Delaware Space Grant College and Fellowship Program (NASA Grant 80NSSC20M0045). We acknowledge partial support provided by the National Fish and Wildlife Foundation, the State of Maryland, and Harry R. Hughes Center for Agro-Ecology.

  • Chinese
    Authors: 
    Ming, Feng Yi; Kun, Qiao; Ang, Feng Shi; Lei, Xi; Zhao, Qi; Lan, Lan;
    Publisher: Science Data Bank

    This dataset is based on the GEE remote sensing cloud platform, using the LANDSAT satellite vegetation growing season (April-October) images from 1990 to 2021 as the data source, and extracting the vegetation cover results of the Ring Tarim Basin for 7 periods during 1990-2021. This dataset contains the distribution data of FVC of vegetation growing season within the Ring Tarim Basin from 1990 to 2021, and the data are stored in a folder named by year for each year, with 7 periods of data. The data are named as FVC+year, and the geographic coordinate system is WGS84. The data type of this dataset is GeoTIFF raster data, the spatial resolution is 30 m, and the data volume is about 11.2 GB. The data can be read, viewed, analyzed, processed and applied in common GIS and remote sensing platforms (such as ArcGIS, QGIS, ENVI, etc.).

  • Open Access
    Authors: 
    Polley, Herbert; Jones, Katherine; Kolodziejczyk, Chris; Fay, Philip;
    Publisher: Zenodo

    Grassland production is sensitive to both precipitation and plant N accumulation and utilization, such that change in one variable influences grassland response to the second variable. We investigated effects of interannual variation in precipiation on the response of 'community'-scale values of relative growth rate (RGR) to two multiplicative components of RGR, nitrogen productivity (NP; rate of change in biomass/plant N), an index of N utilization efficiency, and plant N concentration ([N]), in two grassslands in Texas, USA. Grasslands included a planted mixture of perennial grass and forb species and a monoculture of the perennial C4 grass Panicum vigatum that was invaded by multiple plant species. RGR and its N components were measured at the spatial scale of 7-m diameter circular patches near the spring peak in mixture biomss during each of 5 years. We found that RGR varied substantially among patches and years and between the planted mixture and monoculture. RGR variation was strongly correlated with variation in NP. Precipitation during the 3 months prior to RGR measurement mediated that RGR response to NP by altering the correlation between NP and [N] in both grasslands. Reduced precipitation led to more negative NP-[N] correlation coefficients, which reduced proportional change in RGR per change in NP by as much as 30% even in the absence of a precipitation effect on means of RGR and NP. Our results highlight an under-appreciated aspect of the pervasive role of precipitation in grassland growth that was mediated via change in the growth benefit derived from plant N. We used remote sensing techniques to calculate relative growth rate (RGR), nitrogen productivity (NP), and plant N concentration ([N]) at the scale of 7-m diameter circular patches (n = 104) in each of two grassland types (mixture of perennial grass and forb species, planted monoculture of the grass switchgrass). 'Community'-scale values RGR and its N components (NP, [N]) were calculated near the spring biomass peak in each of 5 years. We examined correlations among spatial variation in RGR, NP, and [N] in each grassland as influenced by interannual variation in precipitation.

  • Open Access
    Authors: 
    ONOJEGHUO, ALEX; Onojeghuo, Ajoke Ruth;
    Publisher: 4TU.ResearchData
    Country: Netherlands

    This is a repository of reference data used for the study.

  • Open Access English
    Authors: 
    Navarro-Miró, David; Blanco-Moreno, José M.; Ciaccia, Corrado; Testani, Elena; Iocola, Ileana; Depalo, Laura; Burgio, Giovanni; Lakkenborg Kristensen, Hanne; Hefner, Margita; Tamm, Kalvi; +17 more
    Publisher: Dryad

    1. Although organic farming was originally promoted as an alternative farming system to address agronomic, environmental, and ecological issues, its conventionalisation has led to an intensification and specialisation of production. In light of this, several studies have questioned the environmental benefits of organic farming as well as its agronomic viability. Thus, there is a need to improve organic vegetable systems to reduce their environmental impact without affecting their productivity. To tackle this challenge, European farmers and researchers have recently started to focus on agroecological service crops (ASCs). However, few studies have simultaneously evaluated the agronomic, environmental, and ecological aspects of ASC management under different European pedo-climatic conditions. 2. We evaluated effects of the ASC management strategies: no-till roller crimping (NT-RC) and green manuring (T-GM) on cropping system performance using agronomic, environmental, and ecological indicators, to exemplify the need for multidimensional analysis to understand management implications for addressing environmental and agronomic challenges. We combined the results from eleven organic vegetable field trials conducted in seven European countries over a period of two years to test for general trends. 3. Our results provide solid evidence that NT-RC management across different pedo-climatic conditions in Europe enhances the activity density of ground and rove beetles, and improves both the potential energy recycling within the system and weed control. However, in NT-RC plots lower cash crop yield and quality, energetic efficiency of production, and activity density of spiders was observed compared to T-GM. 4. Synthesis and applications: Multidimensional analyses using agronomic, environmental, and ecological indicators are required to understand the implications of agricultural management in agroecosystem functioning. Introducing agroecological service crops combined with the use of no-till roller crimping is a promising strategy for improving agronomic performance (e.g., fewer weeds) and reducing environmental (e.g., increasing the potentially recyclable energy), and ecological (e.g., enhancing the activity density of beneficial taxa such as ground and rove beetles) impacts. However, our study also indicates a need for agronomic and environmental improvements while promoting a wider acceptance of this strategy.29-Nov-2021 -- Dataset specifications: This dataset gathers data from 11 organic arable vegetable field trials located in Belgium (BE), Denmark (DK), Estonia (EE), France (FR), Italy (IT), Slovenia (SI), and Spain (ES). Two parallel field experiment types were carried out during two crop cycles. Field experiment type A (FtA) involved the introduction of cold-rainy season ASCs into the crop rotation, followed by a spring-summer cash crop. Field experiment type B (FtB) was performed only at the IT and ES locations where the Mediterranean climatic conditions enabled introduction of the ASCs in the warm-dry season (i.e., summer), followed by the transplantation of an autumn-winter cash crop. This dataset contains: Ecological and environmental indicators: Activity density of ground (Carabidae) and rove (Staphylinidae) beetles and spiders (Araneae); Beta-glucosidase enzyme activity assessment; Nitrogen leaching potential measured by soil mineral nitrogen assessment at cash crop harvest; and the potentially recyclable energy use efficiency indicator (PRE-EUE) (Navarro-Miró, Iocola, et al., 2019). Agronomic indicators: Cash crop marketable yield, and the cash crop quality; The energy efficiency of the marketable production was determined by the energy-use efficiency indicator (M-EUE) (Barut, Ertekin, & Karaagac, 2011). Weed control was analysed by determining weed density (individuals m-2). Information about Repetition/block and sample can be found in Appendix "S1. Trial details". Repetition/block and sample are sometimes not included because the measure under consideration has been taken at higher level (i.e., ASC, termination, plot).

  • Open Access
    Authors: 
    Xiao Zhiqiang;
    Publisher: Zenodo

    The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2019. Please click here to download the MUSES LAI product in 2018. Dataset Characteristics: Spatial Coverage: Global Temporal Coverage: 2019 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 Citation (Please cite this paper whenever these data are used): Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225. Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223. Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318. Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

  • Open Access
    Authors: 
    Xiao Zhiqiang;
    Publisher: Zenodo

    The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 0.05º spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from time-series Land Long-Term Data Record (LTDR) Advanced very high resolution radiometer (AVHRR) daily surface reflectance product (Version 4) using general regression neural networks (GRNNs) (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. Dataset Characteristics: Spatial Coverage: 180º W – 180º E, 90º S – 90º N Temporal Coverage: 1981 – 2019 Spatial Resolution: 0.05º (approximately 5 km) Temporal Resolution: 1 month Projection: Geographic Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 Citation (Please cite this paper whenever these data are used): Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225. Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223. Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318. Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

  • Open Access
    Authors: 
    Statistics Canada;
    Publisher: Open Data Canada

    Données sur la production des broyages des grains, huiles et tourteaux. Data on the production of the crushing of seed, oil and meal.

  • Open Access
    Authors: 
    Statistics Canada;
    Publisher: Open Data Canada

    Production de certains produits de beurre, Canada et les provinces (en tonnes métriques). Les données sont disponibles sur une base mensuelle. Production of selected butter products, Canada and provinces (in tonnes). Data are available on a monthly basis.

  • Open Access
    Authors: 
    Statistics Canada | Statistique Canada;
    Publisher: Government of Canada

    Fabrication de sous-produits concentrés de lait, Canada et les provinces (en tonnes). Les données sont disponibles sur une base mensuelle. Production of concentrated milk products, Canada and provinces (in tonnes). Data are available on a monthly basis.