Remote sensing and GIS for detecting changes in the aquatic vegetation of a rehabilitated lake

Valta-Hulkkonen, Kirsi; Kanninen, Antti

doi:10.1080/01431160412331291170

Cited by 28 publications

(7 citation statements)

References 21 publications

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“…UAS-technology is also especially favourable for monitoring purposes with short and/or user-defined repetition intervals. Our automated classification approach for water versus vegetation and at the growth-form level is highly applicable in lake and river management [53] and aquatic plant control, such as in evaluation of rehabilitation measures [54]. Valta-Hulkkonen et al [55] also found that a lake's degree of colonisation by helophytes and nymphaeids detected by remote sensing was positively correlated with the nutrient content in the water.…”

Section: Discussionmentioning

confidence: 91%

Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images

2016

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Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs) provides aerial images with sub-decimetre resolution and offers a potential data source for vegetation mapping. In a manual mapping approach, UAS true-colour images with 5-cm-resolution pixels allowed for the identification of non-submerged aquatic vegetation at the species level. However, manual mapping is labour-intensive, and while automated classification methods are available, they have rarely been evaluated for aquatic vegetation, particularly at the scale of individual vegetation stands. We evaluated classification accuracy and time-efficiency for mapping non-submerged aquatic vegetation at three levels of detail at five test sites (100 m × 100 m) differing in vegetation complexity. We used object-based image analysis and tested two classification methods (threshold classification and Random Forest) using eCognition ® . The automated classification results were compared to results from manual mapping. Using threshold classification, overall accuracy at the five test sites ranged from 93% to 99% for the water-versus-vegetation level and from 62% to 90% for the growth-form level. Using Random Forest classification, overall accuracy ranged from 56% to 94% for the growth-form level and from 52% to 75% for the dominant-taxon level. Overall classification accuracy decreased with increasing vegetation complexity. In test sites with more complex vegetation, automated classification was more time-efficient than manual mapping. This study demonstrated that automated classification of non-submerged aquatic vegetation from true-colour UAS images was feasible, indicating good potential for operative mapping of aquatic vegetation. When choosing the preferred mapping method (manual versus automated) the desired level of thematic detail and the required accuracy for the mapping task needs to be considered.

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Section: Discussionmentioning

confidence: 91%

Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images

2016

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“…Remote sensing can be used to map vegetation patch patterns with extended spatial and temporal coverage and thus has the potential to greatly improve our understanding of vegetation pattern dynamics in arid and semi-arid landscapes [ 21 , 24 ]. Aerial photographs are mostly used to study vegetation patch dynamics because of the higher spatial resolution, little affected by weather and longer historical archiving than satellite images [ 25 ]. Frenkel and Boss [ 26 ] monitored the establishment and exponential spread of distinctive circular Spartina patens patches on Cox Island, Canada, using sequential aerial photographs from 1939 to 1981.…”

Section: Introductionmentioning

confidence: 99%

Comparison of CBERS-04, GF-1, and GF-2 Satellite Panchromatic Images for Mapping Quasi-Circular Vegetation Patches in the Yellow River Delta, China

Liu

Huang

Liu

et al. 2018

Sensors

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Vegetation in arid and semi-arid regions frequently exists in patches, which can be effectively mapped by remote sensing. However, not all satellite images are suitable to detect the decametric-scale vegetation patches because of low spatial resolution. This study compared the capability of the first Gaofen Satellite (GF-1), the second Gaofen Satellite (GF-2), and China-Brazil Earth Resource Satellite 4 (CBERS-04) panchromatic images for mapping quasi-circular vegetation patches (QVPs) with K-Means (KM) and object-based example-based feature extraction with support vector machine classification (OEFE) in the Yellow River Delta, China. Both approaches provide relatively high classification accuracy with GF-2. For all five images, the root mean square errors (RMSEs) for area, perimeter, and perimeter/area ratio were smaller using the KM than the OEFE, indicating that the results from the KM are more similar to ground truth. Although the mapped results of the QVPs from finer-spatial resolution images appeared more accurate, accuracy improvement in terms of QVP area, perimeter, and perimeter/area ratio was limited, and most of the QVPs detected only by finer-spatial resolution imagery had a more than 40% difference with the actual QVPs in these three parameters. Compared with the KM approach, the OEFE approach performed better for vegetation patch shape description. Coupling the CBERS-04 with the OEFE approach could suitably map the QVPs (overall accuracy 75.3%). This is important for ecological protection managers concerned about cost-effectiveness between image spatial resolution and mapping the QVPs.

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“…In Lake Luupuvesi (central Finland) the extent of macrophytes increased from 96 ha in 1953 to 355 ha in 1996 (Valta-Hulkkonen et al 2004). However, the comparison of aerial photographs from the periods of 1947-1963 and 1996-2000 (Brižs 2011).…”

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confidence: 99%

Changes in lake area as a consequence of plant overgrowth in the South Baltic Lakelands (Northern Poland)

Skowron¹,

Jaworski

2017

Bulletin of Geography. Physical Geography Series

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Abstract. The authors carried out the analysis of changes in the plant overgrowth of the lakes based on cartographic materials. Among 6 793 lakes with the area exceeding 1 ha located in the lakelands of Northern Poland, 893 lakes were selected for the analysis. The lakes were selected on the grounds of the existing bathymetric plans and information on their overgrowth and depth relations. Over the last 50 years lake area declined by 1.9% (from 140 975.0 ha do 138 273.7 ha) and so did the lake area covered with emergent plants, i.e. by 0.27% (from 11 219.0 ha down to 10 637.2 ha). Emergent plants cover on average 7.69% of the lake area. In the case of the lakes of smaller areas (below 80 ha) or medium areas (80÷200 ha) the extent of plant overgrowth was 14.3 and 9.6% respectively. The article presents two indicators which determine plant overgrowth of the lakes. These are the coefficient of overgrowing the lakes (%) and the coefficient of overgrowing the shoreline (ha·km -1 ). These coefficients make it possible to study the extent of lake overgrowing in the South Baltic Lakeland, regardless the direction of these changes.Changes in lake area as a consequence of plant overgrowth in the South Baltic Lakelands (Northern Poland)

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Remote sensing and GIS for detecting changes in the aquatic vegetation of a rehabilitated lake

Cited by 28 publications

References 21 publications

Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images

Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images

Comparison of CBERS-04, GF-1, and GF-2 Satellite Panchromatic Images for Mapping Quasi-Circular Vegetation Patches in the Yellow River Delta, China

Changes in lake area as a consequence of plant overgrowth in the South Baltic Lakelands (Northern Poland)

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