Delineation of site-specific management zone based on SPOT6/7 remote sensing image in black soil area, Northeast China

Liu, Huanjun; Qiu, Zhengchao; Meng, Linghua; Xu, Mengyuan; Pan, Yue; Zhang, Xinle

doi:10.1109/igarss.2017.8128137

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The objects that had a spectral difference below a certain threshold were merged [29,57]. The thresholds selected in this study are as follows: 255 for two MZs; 255 and 256 for three MZs; 240, 255 and 256 for four MZs; and 240, 251, 255 and 256 for five MZs.…”

Section: Spatial Segmentationmentioning

confidence: 99%

Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada

et al. 2021

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Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties, and therefore similar crop fertility requirements. Consequently, such MZs can often be used for site-specific management of crop production inputs. This study evaluated the effectiveness of four classification methods for delineating MZs in an 8-ha commercial potato field located in Prince Edward Island, Canada. The apparent electrical conductivity (ECa) at two depths from a commercial Veris sensor were used to delineate MZs using three classification methods without spatial constraints (i.e., fuzzy k-means, ISODATA and hierarchical) and one with spatial constraints (i.e., spatial segmentation method). Soil samples (0.0–0.15 m depth) from 104 sampling points was used to measure soil physical and chemical properties and their spatial variation in the field were used as reference data to evaluate four delineation methods. Significant Pearson correlations between ECa and soil properties were obtained (0.22 < r < 0.85). The variance reduction indicated that two to three MZs were optimal for representing the field’s spatial variability of soil properties. For two MZs, most soil physical and chemical properties differed significantly between MZs for all four delineation methods. For three MZs, there was greater discrimination among MZs for several soil properties for the spatial segmentation-based method compared with other delineation methods. Moreover, consideration of the spatial coordinates of the data improved the delineation of MZs and thereby increased the number of significant differences among MZs for individual soil properties. Therefore, the spatial segmentation method had the greatest efficiency in delineation of MZs from statistical and agronomic perspectives.

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Section: Spatial Segmentationmentioning

confidence: 99%

Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada

et al. 2021

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“…To take full advantage of multi-spectral information, we utilize the modified ASEI to confirm the best scale parameter. The segmentation evaluation index (SEI) [44,45] in band L for an object is defined by Equation (1), where σ L is the spectral standard deviation of the object, which is used as a homogeneity index. Its formula is shown in Equation (2), where ∆C L is the heterogeneity index derived from calculating the absolute value of the mean difference between the object and a neighbouring object.…”

Section: Mtis and Estimation Of Scale Parametersmentioning

confidence: 99%

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

Feng

Sui

et al. 2018

Remote Sensing

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Abstract:In the process of object-based change detection (OBCD), scale is a significant factor related to extraction and analyses of subsequent change data. To address this problem, this paper describes an object-based approach to urban area change detection (CD) using rotation forest (RoF) and coarse-to-fine uncertainty analyses of multi-temporal high-resolution remote sensing images. First, highly homogeneous objects with consistent spatial positions are identified through vector-raster integration and multi-scale fine segmentation. The multi-temporal images are stacked and segmented under the constraints of a historical land use vector map using a series of optimal segmentation scales, ranging from coarse to fine. Second, neighborhood correlation image analyses are performed to highlight pixels with high probabilities of being changed or unchanged, which can be used as a prerequisite for object-based analyses. Third, based on the coarse-to-fine segmentation and pixel-based pre-classification results, change possibilities are calculated for various objects. Furthermore, changed and unchanged objects identified at different scales are automatically selected to serve as training samples. The spectral and texture features of each object are extracted. Finally, uncertain objects are classified using the RoF classifier. Multi-scale classification results are combined using a majority voting rule to generate the final CD results. In experiments using two pairs of real high-resolution remote sensing datasets, our proposed approach outperformed existing methods in terms of CD accuracy, verifying its feasibility and effectiveness.

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Integrating multisource information to delineate oasis farmland salinity management zones in southern Xinjiang, China

Bai,

Wang,

et al. 2023

Agricultural Water Management

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Delineation of site-specific management zone based on SPOT6/7 remote sensing image in black soil area, Northeast China

Cited by 4 publications

References 5 publications

Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada

Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

Integrating multisource information to delineate oasis farmland salinity management zones in southern Xinjiang, China

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