Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images

Conrad, Christopher; Dech, Stefan; Dubovyk, Olena; Fritsch, Sebastian; Klein, Doris; Löw, Fabian; Schorcht, Gunther; Zeidler, Julian

doi:10.1016/j.compag.2014.02.003

Cited by 86 publications

(70 citation statements)

References 37 publications

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“…The difference in the calculated average OA at the pixel level was lower than 10%. These results are in accordance with similar studies that report on the successful application of object-based frameworks for crop type [9,24,26,27] or vegetation/tree species [1,3,4] classification. Classifying (even with a relatively small size of foliage segments) canopy objects instead of pixels can provide a more compact representation regarding the distinctive properties between different varieties/classes that a classifier searches for decision making.…”

Section: Variety Discrimination Using a Pixel-based Linear Svmsupporting

confidence: 93%

“…In particular, for every processing step, several experiments were performed based on features employed from the literature on similar crop identification/detection studies (e.g., [8,9,16,[22][23][24]27,30]), while the optimal ones for all datasets were selected from a larger pool, through several experiments, feature analysis tools (assessing how each feature contributes to the discrimination task) and a trial and error procedure for fine tuning their parameters. To this end, Trimble's eCognition Developer (ed.8), MathWorks' MATLAB (2015b) and in-house developed software were employed.…”

Section: Methodsmentioning

confidence: 99%

“…Regarding the developed vineyard detection methodology, in contrast to similar research efforts towards vineyard and/or other crop identification tasks [8,9,15,[22][23][24]27,30], the proposed approach employs very high resolution multispectral data, along with a specific set of features, rules, segmentation scales and a set of parameters that deliver relatively higher detection rates. Instead of employing only spectral features (e.g., [8,22,23,27]), we employ, as in [9,24], textural features, as well.…”

Section: Contributionmentioning

confidence: 99%

“…Instead of employing only spectral features (e.g., [8,22,23,27]), we employ, as in [9,24], textural features, as well. However, we solve the problem for very high resolution pan-sharpened data (at 50 cm) instead of the significantly coarser Landsat 8 and ASTER data (15 m/30 m).…”

Section: Contributionmentioning

confidence: 99%

“…In particular, apart from the within-field crop analysis and the estimation of the spatial variability in wine-grape composition and yield [17][18][19][20][21][22][23], there is plenty of research towards the classification, identification and delineation of crops in remote sensing data [24][25][26][27]. However, despite recent research efforts towards the detection and delineation on medium [28][29][30] and high resolution [31,32] spatial scales, the development and validation of efficient classification frameworks for operational vineyard detection in high resolution data and over large agricultural regions still remain a challenge.…”

Section: Introductionmentioning

confidence: 99%

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Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

2016

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Abstract:In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum.

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Section: Variety Discrimination Using a Pixel-based Linear Svmsupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

2016

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Land Suitability Assessment for Afforestation with Elaeagnus Angustifolia L. in Degraded Agricultural Areas of the Lower Amudarya River Basin

2014

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Degradation of irrigated croplands in arid regions is of great environmental concern worldwide. Based on a series of field trials, afforestation with well‐adapted tree species was evaluated as a viable option for the cropland rehabilitation in the lower reaches of the Amudarya River basin in Uzbekistan. Our aim was to extend available site‐specific information for the entire regional landscape and provide spatially explicit guidance in support of afforestation land rehabilitation efforts. To this end, a geographic information system—based multicriteria decision‐making approach has been developed for assessing the suitability of degraded irrigated cropland for introducing plantations of Elaeagnus angustifolia L. This approach utilized expert knowledge, fuzzy logic, and a weighted linear combination to produce an afforestation land suitability map. Overall, 75 287 ha of degraded croplands (18% of the irrigated cropland area) was identified as suitable for introduction of E. angustifolia. We also conclude that irrigation water supply and groundwater table depth are the critical factors determining the suitability of degraded irrigated land. These findings improve an understanding of the spatial variability of areas suitable for the initiation of agroforests and support better informed decisions on cropland rehabilitation. Copyright © 2014 John Wiley & Sons, Ltd.

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Economics of Land Degradation and Improvement in Uzbekistan

Aw‐Hassan¹,

Korol

Nishanov

et al. 2015

Economics of Land Degradation and Improvement – A Global Assessment for Sustainable Development

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Land degradation is a major challenge for agricultural and rural development in Uzbekistan. Our research findings indicate that the costs of land degradation in Uzbekistan are substantial; reaching about 0.85 billion USD annually resulting from the loss of valuable land ecosystem services due to land use and land cover changes alone between 2001 and 2009. On the other hand, economic simulations also show that the returns from actions to address land degradation can be four times higher their costs over a 30-year planning horizon, i.e. every dollar invested into land rehabilitation can yield 4 dollars of returns over this period. The

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Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images

Cited by 86 publications

References 37 publications

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

Land Suitability Assessment for Afforestation with Elaeagnus Angustifolia L. in Degraded Agricultural Areas of the Lower Amudarya River Basin

Economics of Land Degradation and Improvement in Uzbekistan

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