Optimal attributes for the object based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery

Fernandes, María Rosário; Aguiar, Francisca C.; Silva, João M. N.; Ferreira, Maria Teresa; Pereira, José M. C.

doi:10.1016/j.jag.2014.03.026

Cited by 41 publications

(26 citation statements)

References 59 publications

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“…In addition, ALS can be carried out in a very standardised way that will help increase accuracy at a reduced cost. In line with studies focused on reed bed or wetland habitat mapping [23,[25][26][27], ALS is an innovative technical solution compared to classical approaches based on analysis of satellite imagery or of airborne optical data sources [6,44,45]. Compared to optical sensors in which recorded information on every image is only of top structures observable to an imaging sensor, ALS delivered valuable information in the horizontal as well as the vertical gradient.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Green-LiDAR Data for Mapping Extent, Density and Height of Aquatic Reed Beds at Lake Chiemsee, Bavaria—Germany

et al. 2017

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Abstract:Aquatic reed is an important indicator for the ecological assessment of freshwater lakes. Monitoring is essential to document its expansion or deterioration and decline. The applicability of Green-LiDAR data for the status assessment of aquatic reed beds of Bavarian freshwater lakes was investigated. The study focused on mapping diagnostic structural parameters of aquatic reed beds by exploring 3D data provided by the Green-LiDAR system. Field observations were conducted over 14 different areas of interest along 152 cross-sections. The data indicated the morphologic and phenologic traits of aquatic reed, which were used for validation purposes. For the automatic classification of aquatic reed bed spatial extent, density and height, a rule-based algorithm was developed. LiDAR data allowed for the delimitating of the aquatic reed frontline, as well as shoreline, and therefore an accurate quantification of extents (Hausdorff distance = 5.74 m and RMSE of cross-sections length 0.69 m). The overall accuracy measured for aquatic reed bed density compared to the simultaneously recorded aerial imagery was 96% with a Kappa coefficient of 0.91 and 72% (Kappa = 0.5) compared to field measurements. Digital Surface Models (DSM), calculated from point clouds, similarly showed a high level of agreement in derived heights of flat surfaces (RMSE = 0.1 m) and showed an adequate agreement of aquatic reed heights with evenly distributed errors (RMSE = 0.8 m). Compared to field measurements, aerial laser scanning delivered valuable information with no disturbance of the habitat. Analysing data with our classification procedure improved the efficiency, reproducibility, and accuracy of the quantification and monitoring of aquatic reed beds.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Green-LiDAR Data for Mapping Extent, Density and Height of Aquatic Reed Beds at Lake Chiemsee, Bavaria—Germany

et al. 2017

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“…Other studies have shown improved plant species differentiation by comparison to other image sources, including IKONOS (e.g. Pu and Landry, 2012), standard (4-band, 0.5 m spatial resolution) airborne imagery (Fernandes et al, 2014) or by simulation (e.g. Immitzer et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Testing the discrimination and detection limits of WorldView-2 imagery on a challenging invasive plant target

Robinson

Wardell‐Johnson

Pracilio³

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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“…This reduces the amount of herbicide applied (Fernandes et al 2014). For instance, in a potato field both mono and dicotyledonous weeds were detected by GPSMap 278 (Cepl and Kasal 2010) to produce a weed map.…”

Section: Variable Rate Technologymentioning

confidence: 99%

“…Objects can be generated based on particular attributes, such as size, texture, shape, spatial and spectra distribution. These factors can be combined with contextual and hierarchy procedures to give an accurate classification (Pena et al 2013;Fernandes et al 2014). This approach creates a more realistic presentation of the objects than individual pixels create (Lizarazo 2013).…”

Section: "Geographic Object-based Image Analysis (Geobia) Is a Sub-dimentioning

confidence: 99%

“…The Normalized Difference Vegetation Index (NDVI) can be used to distinguish plants from the background soil and discriminate weeds from crop plants (Fernandes et al 2014). It has been used for many applications and is widely used in disease detection (Calderon et al 2013) such as, detecting Brownheart in ‗Braeburn' apples (Clark et al 2003), discrimination of grassland species (Dale et al 2013), exploring physical and physiological attributes of vegetation ) and crop row orientation analysis (Marais Sicre et al 2014).…”

Section: Vegetation Index Analysis (Via)mentioning

confidence: 99%

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Site-Specific Weed Management Using Remote Sensing

Che’Ya¹

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The objective of this research was to develop a method to map weeds in sorghum as the first step as a procedure to control them using site-specific weed management (SSWM). Site-specific weed management is a method to limit the application of herbicides only to areas with weeds.Accurate mapping of weeds is a pre-requisite for applying SSWM. Analysis of hyperspectral remote sensing imagery is recognized as a potentially cost effective technique for discriminating between weeds and crop plants. This research involved: i) collecting hyperspectral reflectance spectra from weeds and sorghum plants, ii) Stepwise Linear Discriminant Analysis (SLDA) to identify the most significant spectral bands, iii) Linear Discrimination Analysis (LDA) to test the accuracy of the SLDA bands for classifying weeds and sorghum, and iv) analysis of customized multispectral imagery to produce maps which detected weeds in the sorghum crop.Hyperspectral signatures of weeds and sorghum were obtained using a FieldSpec® Handheld2TM spectroradiometer with a spectral range from 325 nm to 1075 nm. Spectra were recorded for different weed species and sorghum plants for three years, 2012 to 2014. Data were collected at four different stages of plant growth each year, from week one to week four after planting.The results show that it is feasible to discriminate spectral profiles of weeds from each other weeds and from sorghum plants. Statistical Analysis Software (SAS) was used to identify the most significant spectral bands (10 nm width) from the hyperspectral reflectance data using SLDA. All weeds and sorghum were correctly classified in 2012 using LDA for week four reflectance data. In 2013, the classification accuracy increased with stage of growth (weeks one to four) from 85% to 90%. In 2014, the classification accuracy also increased with stage of growth (weeks two to four) from 90% to 100%. Combinations of spectral bands were analysed to reduce the number of potential bands identified from the SLDA results. Spectral bands centred on 930, 890, 710, 700, 560and 500 nm were common to the 20 most significant spectral bands identified by SLDA analysis each year. Six spectral bands 850, 720, 710, 680, 560 and 440 nm were subsequently selected for use in multispectral image collection. They were selected based on maximizing the differences and similarities between the 2013 weed and crop reflectance profiles. These bands were used for the band-pass filters in the Tetracam MCA 6 camera used for collecting high spatial resolution terrestrial and aerial imagery.The imagery was analysed using Object-Based Image Analysis (OBIA) and Vegetation Index Analysis (VIA) to classify weeds and sorghum plants. OBIA identified weeds more successfully than VIA. The accuracy of OBIA classification was tested using two methods.Confusion matrices were used to measure the Coefficient of Agreement (Khat), Overall Accuracy iii and Producer's and User Accuracies. Geometry matrices were used to measure under and over segmentation. The Overall accuracy and Khat for all...

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Optimal attributes for the object based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery

Cited by 41 publications

References 59 publications

Evaluation of Green-LiDAR Data for Mapping Extent, Density and Height of Aquatic Reed Beds at Lake Chiemsee, Bavaria—Germany

Evaluation of Green-LiDAR Data for Mapping Extent, Density and Height of Aquatic Reed Beds at Lake Chiemsee, Bavaria—Germany

Testing the discrimination and detection limits of WorldView-2 imagery on a challenging invasive plant target

Site-Specific Weed Management Using Remote Sensing

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