High-resolution SAR and high-resolution optical data integration for sub-urban land-cover classification

Rusmini, Marco; Candiani, Gabriele; Frassy, Federico; Maianti, Pieralberto; Marchesi, Andrea; Nodari, Francesco Rota; Dini, Luigi; Gianinetto, Marco

doi:10.1109/igarss.2012.6352492

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Due to the aforementioned issues, up to now, no standard data processing technique has been presented to detect oil slicks with optical images. This paper describes a new method for the tracking of surface oil slicks using MODIS imagery, which is based on the fluorescence/emissivity index (Gianinetto et al 2010a) and object-based image analysis OBIA (Rusmini et al 2012). Two case studies are presented: the Deepwater Horizon (DWH) and the Campos Basin (CB) oil spill accidents.…”

Section: Introductionmentioning

confidence: 99%

Monitoring large oil slick dynamics with moderate resolution multispectral satellite data

Maianti

Rusmini²,

Tortini

et al. 2014

Nat Hazards

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Accidental release of crude oil into the sea due to human activity causes water pollution and heavy damages to natural ecosystems killing birds, fish, mammals and other organisms. A number of monitoring systems are used for tracking the spills and their effects on the marine environment, as well as for collecting data for feeding models. Among them, Earth observation technologies play a crucial role and moderate spatial resolution satellite systems are able to collect images with a very short revisit time or even daily. This paper describes the use of MODIS (Moderate Resolution Imaging Spectroradiometer) data for monitoring large oil slicks with the Fluorescence/Emissivity Index (FEI) and Object Based Image Analysis (OBIA). Two case studies are presented: the Deepwater Horizon (2010) and the Campos Basin (2011) oil spill accidents. Results show that is possible to track the dynamics of the slick both for massive and long-lasting accidents and for smaller and very quick accidents. The main advantages of the method proposed are a straightforward implementation, a fast and semi-automated data processing and the capability of integration of daytime and nighttime acquisitions, as well as its adaptability to different sensors

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Section: Introductionmentioning

confidence: 99%

Monitoring large oil slick dynamics with moderate resolution multispectral satellite data

Maianti

Rusmini²,

Tortini

et al. 2014

Nat Hazards

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“…However, most of the literature considered medium-resolution data, typically Landsat-TM or SPOT for optical and RADARSAT or ERS for radar. At present, only few studies reported case histories of thematic classification involving both MS and SAR images collected with metric or submetric spatial detail [7], [8], [10], [11], [13], [30], [31]. Furthermore, most of them debated the mapping in urban areas or in natural areas and only few managed both LC/LU in the same classification workflow.…”

Section: Integration Of Cosmo-skymed and Geoeye-1 Datamentioning

confidence: 98%

Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis

Gianinetto

Rusmini

Marchesi

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper describes the potentialities of data integration of high spatial resolution multispectral and single-polarization X-band radar for Object Based Image Analysis (OBIA) using already available algorithms and techniques. GeoEye-1 multispectral images (0.5/2.0 m) and COSMO-SkyMed stripmap images (3.0 m) were collected over a complex test site in the Venetian Lagoon, made up of an intricate mixture of settlements, cultivations, channels, roads, and marshes. The validation confirmed that the integration of optical and radar data substantially increased the thematic accuracy (about 20-30% for overall accuracy and about 25-35% for k coefficient) of multispectral data and, unlike the outcomes of some new researches, also confirmed that with appropriate pre-processing traditional OBIA could be applied also to X-band radar data without the need of developing ad hoc algorithms

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“…The integration of SAR and optic imagery was found to be very useful in crop classification using various methods, such as artificial intelligence (AI) algorithms or wavelet transformation [13][14][15]. For crop monitoring, this integration was studied via two approaches.…”

Section: Introductionmentioning

confidence: 99%

Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots

Beeri¹,

Netzer

Munitz

et al. 2020

Remote Sensing

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Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.

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High-resolution SAR and high-resolution optical data integration for sub-urban land-cover classification

Cited by 6 publications

References 24 publications

Monitoring large oil slick dynamics with moderate resolution multispectral satellite data

Monitoring large oil slick dynamics with moderate resolution multispectral satellite data

Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis

Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots

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