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Pieralberto Maianti
Laboratory of Remote Sensing, Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, via Ponzio 31, 20133 Milan, Italy

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Technical note
Published: 15 June 2015 in Remote Sensing
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Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the landscape scale, yet monitoring harvests and regrowth in these forests is challenging. Remote sensing is an obvious route to detect and monitor small-scale land use dynamics over large areas. Using an annual series of Landsat-5 Thematic Mapper (TM) images and a GIS shapefile of property boundaries, we identified units where harvests occurred from 2005 to 2011 using an Object-Based Change Detection (OBCD) approach. Percent of basal area harvested was verified using stand-level harvest data. Our method detected all harvests above 20% basal area removal in all forest types (northern hardwoods, mixed deciduous/coniferous, coniferous), on properties as small as 10 acres (0.4 ha; approximately four Landsat pixels). Our results had a resolution of about 10% basal area (that is, a selective harvest removal of 30% could be distinguished from one of 40%). Our method can be automated and used to measure annual harvest rates and intensities for large areas of the United States, providing critical information on land use transition.

ACS Style

Riccardo Tortini; Audrey L. Mayer; Pieralberto Maianti. Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan. Remote Sensing 2015, 7, 7809 -7825.

AMA Style

Riccardo Tortini, Audrey L. Mayer, Pieralberto Maianti. Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan. Remote Sensing. 2015; 7 (6):7809-7825.

Chicago/Turabian Style

Riccardo Tortini; Audrey L. Mayer; Pieralberto Maianti. 2015. "Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan." Remote Sensing 7, no. 6: 7809-7825.

Journal article
Published: 15 May 2015 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This paper describes the potentialities of data integration of high spatial resolution multispectral (MS) and single-polarization X-band radar for object-based image analysis (OBIA) using already available algorithms and techniques. GeoEye-1 (GE1) MS images (0.5/2.0 m) and COSMO-SkyMed (CSK®) 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 (OA) and about 25%-35% for k coefficient] of MS data, and unlike the outcomes of some new researches, also confirmed that, with appropriate preprocessing, traditional OBIA could also be applied to X-band radar data without the need of developing ad hoc algorithms.

ACS Style

M. Gianinetto; M. Rusmini; A. Marchesi; P. Maianti; F. Frassy; Giorgio Dalla Via; Luigi Dini; F. Rota Nodari. Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2015, 8, 2282 -2293.

AMA Style

M. Gianinetto, M. Rusmini, A. Marchesi, P. Maianti, F. Frassy, Giorgio Dalla Via, Luigi Dini, F. Rota Nodari. Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015; 8 (5):2282-2293.

Chicago/Turabian Style

M. Gianinetto; M. Rusmini; A. Marchesi; P. Maianti; F. Frassy; Giorgio Dalla Via; Luigi Dini; F. Rota Nodari. 2015. "Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 5: 2282-2293.

Journal article
Published: 27 August 2014 in Sensors
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The World Health Organization estimates that 100 thousand people in the world die every year from asbestos-related cancers and more than 300 thousand European citizens are expected to die from asbestos-related mesothelioma by 2030. Both the European and the Italian legislations have banned the manufacture, importation, processing and distribution in commerce of asbestos-containing products and have recommended action plans for the safe removal of asbestos from public and private buildings. This paper describes the quantitative mapping of asbestos-cement covers over a large mountainous region of Italian Western Alps using the Multispectral Infrared and Visible Imaging Spectrometer sensor. A very large data set made up of 61 airborne transect strips covering 3263 km2 were processed to support the identification of buildings with asbestos-cement roofing, promoted by the Valle d’Aosta Autonomous Region with the support of the Regional Environmental Protection Agency. Results showed an overall mapping accuracy of 80%, in terms of asbestos-cement surface detected. The influence of topography on the classification’s accuracy suggested that even in high relief landscapes, the spatial resolution of data is the major source of errors and the smaller asbestos-cement covers were not detected or misclassified.

ACS Style

Federico Frassy; Gabriele Candiani; Marco Rusmini; Pieralberto Maianti; Andrea Marchesi; Francesco Rota Nodari; Giorgio Dalla Via; Carlo Albonico; Marco Gianinetto. Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps. Sensors 2014, 14, 15900 -15913.

AMA Style

Federico Frassy, Gabriele Candiani, Marco Rusmini, Pieralberto Maianti, Andrea Marchesi, Francesco Rota Nodari, Giorgio Dalla Via, Carlo Albonico, Marco Gianinetto. Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps. Sensors. 2014; 14 (9):15900-15913.

Chicago/Turabian Style

Federico Frassy; Gabriele Candiani; Marco Rusmini; Pieralberto Maianti; Andrea Marchesi; Francesco Rota Nodari; Giorgio Dalla Via; Carlo Albonico; Marco Gianinetto. 2014. "Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps." Sensors 14, no. 9: 15900-15913.

Journal article
Published: 13 April 2014 in Natural 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 Moderate-Resolution Imaging Spectroradiometer data for monitoring large oil slicks with the fluorescence/emissivity index and object-based image analysis. Two case studies are presented: the Deepwater Horizon (2010) and the Campos Basin (2011) oil spill accidents. Results show that it 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.

ACS Style

Pieralberto Maianti; Marco Rusmini; Riccardo Tortini; Giorgio Dalla Via; Federico Frassy; Andrea Marchesi; Francesco Rota Nodari; Marco Gianinetto. Monitoring large oil slick dynamics with moderate resolution multispectral satellite data. Natural Hazards 2014, 73, 473 -492.

AMA Style

Pieralberto Maianti, Marco Rusmini, Riccardo Tortini, Giorgio Dalla Via, Federico Frassy, Andrea Marchesi, Francesco Rota Nodari, Marco Gianinetto. Monitoring large oil slick dynamics with moderate resolution multispectral satellite data. Natural Hazards. 2014; 73 (2):473-492.

Chicago/Turabian Style

Pieralberto Maianti; Marco Rusmini; Riccardo Tortini; Giorgio Dalla Via; Federico Frassy; Andrea Marchesi; Francesco Rota Nodari; Marco Gianinetto. 2014. "Monitoring large oil slick dynamics with moderate resolution multispectral satellite data." Natural Hazards 73, no. 2: 473-492.