Search results for "computers in earth sciences"

Efficient Kernel Cook's Distance for Remote Sensing Anomalous Change Detection

2021

Detecting anomalous changes in remote sensing images is a challenging problem, where many approaches and techniques have been presented so far. We rely on the standard field of multivariate statistics of diagnostic measures, which are concerned about the characterization of distributions, detection of anomalies, extreme events, and changes. One useful tool to detect multivariate anomalies is the celebrated Cook's distance. Instead of assuming a linear relationship, we present a novel kernelized version of the Cook's distance to address anomalous change detection in remote sensing images. Due to the large computational burden involved in the direct kernelization, and the lack of out-…

Comparative study of three satellite image time-series decomposition methods for vegetation change detection

2018

International audience; Satellite image time-series (SITS) methods have contributed notably to detection of global change over the last decades, for instance by tracking vegetation changes. Compared with multi-temporal change detection methods, temporally highly resolved SITS methods provide more information in a single analysis, for instance on the type and consistency of change. In particular, SITS decomposition methods show a great potential in extracting various components from non-stationary time series, which allows for an improved interpretation of the temporal variability. Even though many case studies have applied SITS decomposition methods, a systematic comparison of common algori…

Introduction to the Special Section on Temporal Change Observation for Bio-Geophysical Parameters

2011

The nine papers in this special section are contributions from the AgriSAR-2006 Campaign Workshop, held at ESA-ESTEC, Noordwijk, The Netherlands, in October 2008.

Multitemporal Mosaicing for Sentinel-3/FLEX Derived Level-2 Product Composites

2020

The increasing availability of remote sensing data raises important challenges in terms of operational data provision and spatial coverage for conducting global studies and analyses. In this regard, existing multitemporal mosaicing techniques are generally limited to producing spectral image composites without considering the particular features of higher-level biophysical and other derived products, such as those provided by the Sentinel-3 (S3) and Fluorescence Explorer (FLEX) tandem missions. To relieve these limitations, this article proposes a novel multitemporal mosaicing algorithm specially designed for operational S3-derived products and also studies its applicability within the FLEX…

Neural Network Emulation of Synthetic Hyperspectral Sentinel-2-Like Imagery With Uncertainty

2023

Hyperspectral satellite imagery provides highly-resolved spectral information for large areas and can provide vital information. However, only a few imaging spectrometer missions are currently in operation. Aiming to generate synthetic satellite-based hyperspectral imagery potentially covering any region, we explored the possibility of applying statistical learning, i.e. emulation. Based on the relationship of a Sentinel-2 (S2) scene and a hyperspectral HyPlant airborne image, this work demonstrates the possibility to emulate a hyperspectral S2-like image. We tested the role of different machine learning regression algorithms (MLRA) and varied the image-extracted training dataset size. We f…

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

2017

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and funct…

Estimation of the time lag occurring between vegetation indices and aridity indices in a Sicilian semi-arid catchment

2009

The evolution of drought phenomena in a Sicilian semi-arid catchment has been analyzed processing both remote sensing images and climatic data for the period 1985-2000. The remote sensing dataset includes Landsat TM and ETM+ multispectral images, while the climatic dataset includes monthly rainfall and air temperature. The results have been specifically discussed for areas where it is possible to neglect agricultural activities and vegetation growth is only influenced by natural forcing. The main outcome of this study is the quantification of the time lag between the remote sensing retrieved vegetation indices and the aridity indices (AIs) calculated from climatic data. Moreover the obtaine…

Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

2022

Carbon Stocks in Peri-Urban Areas: A Case Study of Remote Sensing Capabilities

2014

Peri-urban areas are the extension of cities into contiguous areas, where households and farms coexist. Carbon stocks (CSs) assessment, a concept here extended to urban features, has not yet been studied in depth over peri-urban areas due to uncertainties in such CSs quantification, level of detail required about construction materials, and the high spatial variability of those stocks. Remote sensing (RS)-based techniques have been successfully utilized in urban areas for assessing phenomena such as soil sealing, sprawl patterns, and dynamics of surface imperviousness, especially focusing on land cover classification at high to medium spatial scales. Over the floodplain study area of Emilia…

Integration of fuzzy logic and image analysis for the detection of gullies in the Calhoun Critical Zone Observatory using airborne LiDAR data

2017

Abstract The entire Piedmont of the Southeastern United States, where the Calhoun Critical Zone Observatory (CCZO) is located, experienced one of the most severe erosive events of the last two centuries. Forested areas were cleared to cultivate cotton, tobacco, and other crops during the nineteenth and early twentieth century and these land use changes, together with intense rainfalls, initiated deep gullying. An accurate mapping of these landforms is important since, despite some gully stabilization and reforestation efforts, gullies are still major contributors of sediment to streams. Mapping gullies in the CCZO area is hindered by the presence of dense canopy, which precludes the identif…