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  • 1
    ISSN: 0277-5387
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 0020-1693
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Soil Science Society of America journal 62 (1998), S. 725-730 
    ISSN: 1435-0661
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Elytrigia intermedia [Host] Nevski ssp. intermedia), pubescent wheatgrass (Elytrigia intermedia [Schur.] A. Lö ve ssp. barbulata) and smooth brome (Bromus inermis Leysser), and in winter wheat (Triticum aestivum L.)-fallow fields. Mineralizable C increased from 0.37 g m−2 d−1 in wheat-fallow fields to 0.99 g m−2d−1 in CRP fields; mineralizable N and coarse particulate C were consistently but not significantly higher in CRP fields. Fine particulate and total soil C and N were not significantly different between CRP and wheat-fallow. Within CRP fields, mineralizable C was significantly higher under grasses than in interspaces (1.96 vs. 0.73 g m2d−1, respectively), and mineralizable N and coarse particulate C and N were consistently but not significantly higher under grasses than in interspaces. Soil C and N have increased only slightly after 6 yr of CRP management, and future changes in land use management on these CRP fields, including grazing and cropping, may accrue some small benefits associated with improved soil fertility status.
    Type of Medium: Electronic Resource
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  • 4
    ISSN: 1573-515X
    Keywords: grassland soils ; plant effects on soil ; semiarid grassland ; soil organic matter ; soil resource islands ; subhumid grassland ; water-nutrient interactions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology , Geosciences
    Notes: Abstract We present a conceptual model in which plant-soil interactions in grasslands are characterized by the extent to which water is limiting. Plant-soil interactions in dry grasslands, those dominated by water limitation (‘belowground-dominance’), are fundamentally different from plant-soil interactions in subhumid grasslands, where resource limitations vary in time and space among water, nitrogen, and light (‘indeterminate dominance’). In the belowground-dominance grasslands, the strong limitation of soil water leads to complete (though uneven) occupation of the soil by roots, but insufficient resources to support continuous aboveground plant cover. Discontinuous aboveground plant cover leads to strong biological and physical forces that result in the accumulation of soil materials beneath individual plants in resource islands. The degree of accumulation in these resource islands is strongly influenced by plant functional type (lifespan, growth form, root:shoot ratio, photosynthetic pathway), with the largest resource islands accumulating under perennial bunchgrasses. Resource islands develop over decadal time scales, but may be reduced to the level of bare ground following death of an individual plant in as little as 3 years. These resource islands may have a great deal of significance as an index of recovery from disturbance, an indicator of ecosystem stability or harbinger of desertification, or may be significant because of possible feedbacks to plant establishment. In the grasslands in which the dominant resource limiting plant community dynamics is indeterminate, plant cover is relatively continuous, and thus the major force in plant-soil interactions is related to the feedbacks among plant biomass production, litter quality and nutrient availability. With increasing precipitation, the over-riding importance of water as a limiting factor diminishes, and four other factors become important in determining plant community and ecosystem dynamics: soil nitrogen, herbivory, fire, and light. Thus, several different strategies for competing for resources are present in this portion of the gradient. These strategies are represented by different plant traits, for example root:shoot allocation, height and photosynthetic pathway type (C3 vs. C4) and nitrogen fixation, each of which has a different influence on litter quality and thus nutrient availability. Recent work has indicated that there are strong feedbacks between plant community structure, diversity, and soil attributes including nitrogen availability and carbon storage. Across both types of grasslands, there is strong evidence that human forces that alter plant community structure, such as invasions by nonnative annual plants or changes in grazing or fire regime, alters the pattern, quantity, and quality of soil organic matter in grassland ecosystems. The reverse influence of soils on plant communities is also strong; in turn, alterations of soil nutrient supply in grasslands can have major influences on plant species composition, plant diversity, and primary productivity.
    Type of Medium: Electronic Resource
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  • 5
    Publication Date: 2022-02-16
    Type: info:eu-repo/semantics/conferenceObject
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  • 6
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    In:  IEEE Transactions on Geoscience and Remote Sensing
    Publication Date: 2022-02-15
    Description: Deep learning (DL) has proven to be a suitable approach for despeckling synthetic aperture radar (SAR) images. So far, most DL models are trained to reduce speckle that follows a particular distribution, either using simulated noise or a specific set of real SAR images, limiting the applicability of these methods for real SAR images with unknown noise statistics. In this article, we present a DL method, deSpeckNet, 1 that estimates the speckle noise distribution and the despeckled image simultaneously. Since it does not depend on a specific noise model, deSpeckNet generalizes well across SAR acquisitions in a variety of landcover conditions. We evaluated the performance of deSpeckNet on single polarized Sentinel-1 images acquired in Indonesia, The Democratic Republic of Congo, and The Netherlands, a single polarized ALOS-2/PALSAR-2 image acquired in Japan and an Iceye X2 image acquired in Germany. In all cases, deSpeckNet was able to effectively reduce speckle and restore the images in high quality with respect to the state of the art.
    Type: info:eu-repo/semantics/article
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  • 7
    Publication Date: 2022-09-27
    Description: National-scale assessments of post-deforestation land-use are crucial for decreasing deforestation and forest degradation-related emissions. In this research, we assess the potential of different satellite data modalities (single-date, multi-date, multi-resolution, and an ensemble of multi-sensor images) for classifying land-use following deforestation in Ethiopia using the U-Net deep neural network architecture enhanced with attention. We performed the analysis on satellite image data retrieved across Ethiopia from freely available Landsat-8, Sentinel-2 and Planet-NICFI satellite data. The experiments aimed at an analysis of (a) single-date images from individual sensors to account for the differences in spatial resolution between image sensors in detecting land-uses, (b) ensembles of multiple images from different sensors (Planet-NICFI/Sentinel-2/Landsat-8) with different spatial resolutions, (c) the use of multi-date data to account for the contribution of temporal information in detecting land-uses, and, finally, (d) the identification of regional differences in terms of land-use following deforestation in Ethiopia. We hypothesize that choosing the right satellite imagery (sensor) type is crucial for the task. Based on a comprehensive visually interpreted reference dataset of 11 types of post-deforestation land-uses, we find that either detailed spatial patterns (single-date Planet-NICFI) or detailed temporal patterns (multi-date Sentinel-2, Landsat-8) are required for identifying land-use following deforestation, while medium-resolution single-date imagery is not sufficient to achieve high classification accuracy. We also find that adding soft-attention to the standard U-Net improved the classification accuracy, especially for small-scale land-uses. The models and products presented in this work can be used as a powerful data resource for governmental and forest monitoring agencies to design and monitor deforestation mitigation measures and data-driven land-use policy.
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 8
    Publication Date: 2023-06-21
    Description: Advancements in satellite-based forest monitoring increasingly enable the near real-time detection of small-scale tropical forest disturbances. However, there is an urgent need to enhance such monitoring methods with automated direct driver attributions to detected disturbances. This would provide important additional information to make forest disturbance alerts more actionable and useful for uptake by different stakeholders. In this study, we demonstrate spatially explicit and near real-time methods to monitor direct drivers of small-scale tropical forest disturbance across a range of tropical forest conditions in Suriname, the Republic of the Congo and the Democratic Republic of the Congo. We trained a convolutional neural network with Sentinel-1 and Sentinel-2 data to continuously classify newly detected RAdar for Detecting Deforestation (RADD) alerts as smallholder agriculture, road development, selective logging, mining or other. Different monitoring scenarios were evaluated based on varying sensor combinations, post-disturbance time periods and confidence levels. In general, the use of Sentinel-2 data was found to be most accurate for driver classifications, especially with data composited over a period of 4 to 6 months after the disturbance detection. Sentinel-1 data showed to be valuable for more rapid classifications of specific drivers, especially in areas with persistent cloud cover. Throughout all monitoring scenarios, smallholder agriculture was classified most accurately, while road development, selective logging and mining were more challenging to distinguish. An accuracy assessment throughout the full extent of our study regions revealed a Macro-F1 score of 0.861 and an Overall Accuracy of 0.897 for the best performing model, based on the use of 6-month post-disturbance Sentinel-2 composites. Finally, we addressed three specific monitoring use cases that relate to rapid law enforcement against illegal activities, ecological impact assessments and timely carbon emission reporting, by optimizing the trade-off in classification timeliness and confidence to reach required accuracies. Our findings demonstrate the strong capacities of high spatiotemporal resolution satellite data for monitoring direct drivers of small-scale forest disturbance, considering different user interests.
    Type: info:eu-repo/semantics/article
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  • 9
    Publication Date: 2024-01-19
    Description: African forest are increasingly in decline as a result of land-use conversion due to human activities. However, a consistent and detailed characterization and mapping of land-use change that results in forest loss is not available at the spatial-temporal resolution and thematic levels suitable for decisionmaking at the local and regional scales; so far they have only been provided on coarser scales and restricted to humid forests. Here we present the first high-resolution (5 m) and continental-scale mapping of land use following deforestation in Africa, which covers an estimated 13.85% of the global forest area, including humid and dry forests. We use reference data for 15 different land-use types from 30 countries and implement an active learning framework to train a deep learning model for predicting land-use following deforestation with an F1-score of 84 ± 0.7 for the whole of Africa. Our results show that the causes of forest loss vary by region. In general, small-scale cropland is the dominant driver of forest loss in Africa, with hotspots in Madagascar and DRC. In addition, commodity crops such as cacao, oil palm, and rubber are the dominant drivers of forest loss in the humid forests of western and central Africa, forming an “arc of commodity crops” in that region. At the same time, the hotspots for cashew are found to increasingly dominate in the dry forests of both western and southeastern Africa, while larger hotspots for large-scale croplands were found in Nigeria and Zambia. The increased expansion of cacao, cashew, oil palm, rubber, and large-scale croplands observed in humid and dry forests of western and south-eastern Africa suggests they are vulnerable to future land-use changes by commodity crops, thus creating challenges for achieving the zero deforestation supply chains, support REDD+ initiatives, and towards sustainable development goals.
    Type: info:eu-repo/semantics/article
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  • 10
    Publication Date: 2014-05-01
    Print ISSN: 0011-183X
    Electronic ISSN: 1435-0653
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Wiley
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