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

MODIS-Based Fractional Crop Mapping in the U.S. Midwest with Spatially Constrained Phenological Mixture Analysis

Since the 2000s, bioenergy land use has been rapidly expanded in U.S. agricultural lands. Monitoring this change with limited acquisition of remote sensing imagery is difficult because of the similar spectral properties of crops. While phenology-assisted crop mapping is promising, relying on frequently observed images, the accuracies are often low, with mixed pixels in coarse-resolution imagery. In this paper, we used the eight-day, 500 m MODIS products (MOD09A1) to test the feasibility of crop unmixing in the U.S. Midwest, an important bioenergy land use region. With all MODIS images acquired in 2007, the 46-point Normalized Difference Vegetation Index (NDVI) time series was extracted in the study region. Assuming the phenological pattern at a pixel is a linear mixture of all crops in this pixel, a spatially constrained phenological mixture analysis (SPMA) was performed to extract crop percent covers with endmembers selected in a dynamic local neighborhood. The SPMA results matched well with the USDA crop data layers (CDL) at pixel level and the Crop Census records at county level. This study revealed more spatial details of energy crops that could better assist bioenergy decision-making in the Midwest

Image segmentation by iterative parallel region growing with application to data compression and image analysis

Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image

Exploring Parallel Efficiency and Synergy for Max-P Region Problem Using Python

Given a set of n areas spatially covering a geographical zone such as a province, forming contiguous regions from homogeneous neighboring areas satisfying a minimum threshold criterion over each region is an interesting NP-hard problem that has applications in various domains such as political science and GIS. We focus on a specific case, called Max-p regions problem, in which the main objective is to maximize the number of regions while keeping heterogeneity in each region as small as possible. The solution is broken into two phases: Construction phase and Optimization phase. We present a parallel implementation of the Max-p problem using Python multiprocessing library. By exploiting an intuitive data structure based on multi-locks, we achieve up 12-fold and 19-fold speeds up over the best sequential algorithm for the construction and optimization phases respectively. We provide extensive experimental results to verify our algorithm

Spatial Distribution and Quantification of Forest Treatment Residues for Bioenergy Production

The availability and spatial distribution of forest treatment residues are prerequisites to supply chain development for bioenergy production. To accurately estimate potential residue quantities, data must be provided to simulate stand-level silviculture across the landscape of interest. However, biomass utilization assessments often consider broad regions where adequate data are not supplied. At present, these measures are addressed using strategic level assessments and broad-based management that may not be applicable to all areas of the landscape. This thesis introduces a new methodology for spatially describing stand-level treatment residue quantities based on detailed silvicultural prescriptions and site specific management. Using National Agricultural Imagery Program (NAIP) imagery, the forest is segmented into treatment units based on user defined size constraints. Using a remote sensing model based on NAIP imagery and Forest Inventory and Analysis plot data, these units are attributed with stand-level descriptions of basal area, tree density, above ground biomass, and quadratic mean diameter . The outputs are used to develop silvicultural prescriptions and estimate available treatment residues under three alternative management scenarios at a range of delivered prices per bone dried ton (bdt) to a nearby bioenergy facility in southwestern Colorado. Using a marginal cost approach where treatment costs were covered by merchantable yields, the breakeven delivered price of treatment residues in this study is $48.94 per bdt yielding 167,685 bdt following a 10 year management simulation at a 5,000 acre per year annual allowable treatment level

Agriculture field characterization using GIS software and scanned color infrared aerial photographs

Non-Peer ReviewedThis paper addresses the potential of a color infrared aerial photograph to provide spatially distributed information for site specific management. In this process digitized color infrared aerial photographs were used to extract vegetation index information. In addition, important crop and soil information were also collected using a grid sampling technique. Crop and soil information contributing most to explain variability were determined and used in further analysis. Grain yield data obtained using combine sampling were noted along with the coordinate information of the sample points. Locational information were collected using GPS. Kriged surface were generated using soil and crop point sample information. Point information were extracted from each kriged surface using centroid of uniformly spaced grid (15 m cell). Fuzzy k-means with extragrades algorithms were used to delineate potential within-field management units based on soil and crop information and vegetation index separately. Then “goodness” of potential management zones generated using within zone variability of grain yield. Ideal number of zones were determined using the decrease in total within-zone variance. Finally, management zones determined using crop and soil information and vegetation index information were compared for similarity. The methodology is fast, can be easily automated in commercially available GIS software and has considerable advantages when comparing to other methods for delineating within-field management zones

Development of High Angular Resolution Diffusion Imaging Analysis Paradigms for the Investigation of Neuropathology

Diffusion weighted magnetic resonance imaging (DW-MRI), provides unique insight into the microstructure of neural white matter tissue, allowing researchers to more fully investigate white matter disorders. The abundance of clinical research projects incorporating DW-MRI into their acquisition protocols speaks to the value this information lends to the study of neurological disease. However, the most widespread DW-MRI technique, diffusion tensor imaging (DTI), possesses serious limitations which restrict its utility in regions of complex white matter. Fueled by advances in DW-MRI acquisition protocols and technologies, a group of exciting new DW-MRI models, developed to address these concerns, are now becoming available to clinical researchers. The emergence of these new imaging techniques, categorized as high angular resolution diffusion imaging (HARDI), has generated the need for sophisticated computational neuroanatomic techniques able to account for the high dimensionality and structure of HARDI data. The goal of this thesis is the development of such techniques utilizing prominent HARDI data models. Specifically, methodologies for spatial normalization, population atlas building and structural connectivity have been developed and validated. These methods form the core of a comprehensive analysis paradigm allowing the investigation of local white matter microarcitecture, as well as, systemic properties of neuronal connectivity. The application of this framework to the study of schizophrenia and the autism spectrum disorders demonstrate its sensitivity sublte differences in white matter organization, as well as, its applicability to large population DW-MRI studies