Management zone delineation using a modified watershed algorithm

Le zonage intra-parcellaire est une méthode couramment utilisée pour gérer la variabilité intra-parcellaire. Ce concept consiste à partitionner une parcelle en zones de management selon une ou plusieurs caractéristiques du sol et/ou du couvert végétal de cette parcelle. Cet article propose une méthode de zonage originale, basée sur l'utilisation d'une méthode de segmentation d'image puissante et rapide : l'algorithme de ligne de partage des eaux. Cet algorithme d'analyse d'image a été adapté aux spécificités de l'agriculture de précision. Les performances de notre méthodes ont été testées sur des cartes biophysiques haute résolution de plusieurs champs de blé situés en Bourgogne. / Site-specific management (SSM) is a common way to manage within-field variability. This concept divides fields into site-specific management zones (SSMZ) according to one or several soil or crop characteristics. This paper proposes an original methodology for SSMZ delineation which is able to manage different kinds of crop and/or soil images using a powerful segmentation tool: the watershed algorithm. This image analysis algorithm was adapted to the specific constraints of precision agriculture. The algorithm was tested on high-resolution bio-physical images of a set of fields in France.ZONAGE;PARCELLE;TELEDETECTION;BLE;SEGMENTATION D'IMAGE;AGRICULTURE DE PRECISION;FRANCE;BOURGOGNE;PRECISION AGRICULTURE;MANAGEMENT ZONES;REMOTE SENSING;IMAGE ANALYSIS;WATERSHED SEGMENTATION

Pilot investigation of remote sensing for intertidal oyster mapping in coastal South Carolina: a methods comparison

South Carolina’s oyster reefs are a major component of the coastal landscape. Eastern oysters Crassostrea virginica are an important economic resource to the state and serve many essential functions in the environment, including water filtration, creek bank stabilization and habitat for other plants and animals. Effective conservation and management of oyster reefs is dependent on an understanding of their abundance, distribution, condition, and change over time. In South Carolina, over 95% of the state’s oyster habitat is intertidal. The current intertidal oyster reef database for South Carolina was developed by field assessment over several years. This database was completed in the early 1980s and is in need of an update to assess resource/habitat status and trends across the state. Anthropogenic factors such as coastal development and associated waterway usage (e.g., boat wakes) are suspected of significantly altering the extent and health of the state’s oyster resources. In 2002 the NOAA Coastal Services Center’s (Center) Coastal Remote Sensing Program (CRS) worked with the Marine Resources Division of the South Carolina Department of Natural Resources (SCDNR) to develop methods for mapping intertidal oyster reefs along the South Carolina coast using remote sensing technology. The objective of this project was to provide SCDNR with potential methodologies and approaches for assessing oyster resources in a more efficiently than could be accomplished through field digitizing. The project focused on the utility of high-resolution aerial imagery and on documenting the effectiveness of various analysis techniques for accomplishing the update. (PDF contains 32 pages

Global distribution of modern shallow marine shorelines. Implications for exploration and reservoir analogue studies

Acknowledgments Support for this work came from the SAFARI consortium which was funded by Bayern Gas, ConocoPhillips, Dana Petroleum, Dong Energy, Eni Norge, GDF Suez, Idemitsu, Lundin, Noreco, OMV, Repsol, Rocksource, RWE, Statoil, Suncor, Total, PDO, VNG and the Norwegian Petroleum Directorate (NPD). This manuscript has benefited from discussion with Bruce Ainsworth, Rachel Nanson and Christian Haug Eide. Boyan Vakarelov and Richard Davis Jr. are thanked for their constructive reviews and valuable comments that helped to improve the manuscript.Peer reviewedPostprin

Delineation of the Canadian Agricultural Ecumene for 1991

Agribusiness,

Spectral characterization of the Nigerian shoreline using Landsat imagery

The challenges of shoreline mapping include the high costs of acquiring up-to-date survey data over the coastal area. As a result, in many developing countries, the shoreline has not been consistently mapped. The variety of methods used for this mapping and the large time differences between the surveys (on the order of decades) could result in inaccuracies in shoreline data. This study presents the development of a shoreline characterization procedure for the Nigerian coastline using satellite remote sensing technology. The study goal is to produce a complete, consistent and continuous shoreline map using publicly available data processed in a GIS environment. A spectral analysis using different satellite bands was conducted to define the land/water boundary and characterize the coastal area around the shoreline. The satellite-derived shorelines were compared to charted shorelines for adequacy and consistency. The procedure was developed based on study sites along the Nigerian coastline. Although the shoreline characterization procedure is developed based on datasets from Nigeria, the procedure should be suitable for use in mapping other developing areas around world

A segmentation approach to delineate zones for differential nitrogen intervention.

Multi-source and -temporal data integration is expected to support the delineation of within-field management zones that may better conform to unique combinations of crop yield variations. This work addresses the evaluation of zone delineation approaches based on image classification and segmentation methods. An object based segmentation is introduced using ancillary data from multivariate analysis of yield maps. A simple economic evaluation is conducted to compare delineation methods aiming variable-rate Nitrogen applications. Advantages and penalties are suggested for 2, 3, and 4 management zones. Results show that a procedure combining multiresolution, watershed and region grow segmentation algorithms has systematically resulted in greater net worth. It is suggested that segmentation methods have potential application for zone management delineations supporting contiguous patter

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales