Use of textural measurements to map invasive wetland plants in the Hudson River National Estuarine Research Reserve with IKONOS satellite imagery

Laba, Magdeline; Blair, Betsy; Downs, Roger; Monger, Bruce C.; Philpot, William; Smith, Stephen D.; Sullivan, Patrick J.; Baveye, Philippe C.

doi:10.1016/j.rse.2009.12.002

Cited by 70 publications

(54 citation statements)

References 49 publications

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“…The land uses by humans such as building clusters, farming lands, and green belts, and the crowns of natural vegetation tend to present recognizable patterns and regular configurations on satellite remotely sensed imagery [42]. Previous studies have proved that the GLCM texture metric (e.g., variation) is useful for improving the classification accuracy of various land cover types and reducing the classification errors for those objects with similar spectral features [22]. Therefore, the NDVI and NDWI images were derived from the original Pléiades-1B images, and a PCA transformation was performed to extract the first component comprising brightness and the second component comprising surface structures.…”

Section: Spectral Feature Selection and Optimizationmentioning

confidence: 99%

“…Classification algorithms such as conventional decision tree, maximum likelihood, support vector machines (SVM), and artificial neural networks (ANN) have been employed in wetland classification [16][17][18][19][20][21]. With the improved spatial resolution of remotely sensed imagery, the object-oriented strategy has also been proposed [22][23][24]. However, the accuracy and robustness of the existing classification methods are not yet satisfactory for wetland management, bearing big omission and commission errors due to the sparse yet variable vegetation and the hydrological fluctuation in the wetlands of arid areas [25,26].…”

Section: Introductionmentioning

confidence: 99%

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Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

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Abstract:The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI) series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%-10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest classification by fusing multi-sensor data can retrieve better wetland landcover information than the other classifiers, which is significant for the monitoring and management of the wetland ecological resources in arid areas.

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Section: Spectral Feature Selection and Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

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“…suggests that these floating plants may be shading submerged macrophytes by recent aggressive colonization of the surface (see satellite imagery in Zhang et al 2011). Water chestnut plant rosettes typically completely cover the water surface, affecting dissolved oxygen levels in the water column (Goodwin et al, 2008) and enabling mapping by remote sensing imagery (Laba et al, 2010). Remote sensing of Shengjin Lake showed that the extent of Trapa spp.…”

Section: Introductionmentioning

confidence: 99%

Effects of shading onVallisneria natans(Lour.) H. Hara growth

Fox

Meng

Shen

et al. 2013

Knowl. Managt. Aquatic Ecosyst.

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Key-words:China, Macrophyte reproduction, Trapa, Vegetative reproduction, Yangtze RiverEffects of surface shading were measured on above-and below-ground biomass and fruit production of Vallisneria natans (Lour.) H. Hara plants grown from seed in replicated microcosm experiments, based on a control (no shading) and four treatments (25%, 50%, 75% and 90% shading). Above-and below-ground biomass was significantly reduced at treatments above 50% shading and first pistillate and staminate florescence dates were significantly delayed above 75% and 50% shading, respectively. Ratios of mature to unripe fruits produced (both in number or dry weight) did not differ between shading treatments, but dry weight fruit production was significantly reduced at 90% shading. We conclude that above 50% surface shading, V. natans plants suffer reductions in accumulated biomass and investment in sexual reproduction. We contend that recent expansions in the extent of the native floating water chestnut Trapa spp. at seasonally inundated wetlands in the Yangtze River floodplain could, by shading, have contributed to the reduction in annual biomass and seed production of V. natans, contributing to declines in distribution and abundance. RÉSUMÉEffets de l'ombrage sur la croissance de Vallisneria natans (Lour.) H. Hara Mots-clés :Chine, reproduction macrophytes, Trapa, reproduction végétative, Yangtsé Les effets de l'ombrage de surface ont été mesurés sur la biomasse au-dessus et en dessous du sol et la production de semences de Vallisneria natans (Lour.) H. Hara, plantes issues de semences, dans des expériences en microcosmes répli-qués, avec un contrôle (sans ombrage) et quatre traitements (25 %, 50 %, 75 % et 90 % d'ombrage). La biomasse au-dessus et en dessous du sol a été considé-rablement réduite aux traitements supérieurs à 50 % d'ombrage et les dates de floraison femelle et mâle ont été considérablement retardées au-dessus de 75 % et 50 % d'ombrage, respectivement. Les proportions de fruits produits à maturité ou non (tant en nombre ou qu'en poids sec) ne différaient pas entre les traitements d'ombrage, mais la production de fruits en poids sec a été significativement ré-duite à 90 % d'ombrage. Nous concluons qu'au-dessus de 50 % d'ombrage de surface, la plante V. natans souffre de réductions de sa croissance en biomasse et dans son investissement dans la reproduction sexuée. Nous soutenons que les expansions récentes de la châtaigne d'eau Trapa spp. dans les zones humides saisonnièrement inondées de la plaine du fleuve Yangtsé pourrait, par ombrage, avoir contribué à la réduction de la biomasse annuelle et de la production de semences de V. natans, contribuant à la baisse dans sa distribution et son abondance.

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“…In this research, object-based image analysis (OBIA) was employed for VHR aerial image classification and change detection due to its advantages over the pixel-based approach, such as adding object shape and context to spectral and textural information, avoiding a "salt-and-pepper" pattern in pixel-based classification [9,22,23]. In the following we summarize the approaches step-by-step for land cover classification and change detection of Shahu Lake with OBIA.…”

Section: Methodsmentioning

confidence: 99%

Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

Zhang

Tan

Zheng

et al. 2018

Water

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Abstract:The availability of very high spatial resolution (VHR) remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC) changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees) classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978-2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from~7.64 km 2 to~3.60 km 2 during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake's shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.

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Use of textural measurements to map invasive wetland plants in the Hudson River National Estuarine Research Reserve with IKONOS satellite imagery

Cited by 70 publications

References 49 publications

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

Effects of shading onVallisneria natans(Lour.) H. Hara growth

Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

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