Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

Johansen, Kasper; Arroyo, Lara A.; Armston, John; Witte, Christian

doi:10.1016/j.ecolind.2010.01.001

Cited by 62 publications

(48 citation statements)

References 42 publications

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“…Previous studies also show the potential of LiDAR data to describe riparian vegetation; but most of these are focused on forest applications (Farid et al, 2006(Farid et al, , 2008Greenberg et al, 2012;Wasser et al, 2013). Only a few studies can be found on the potential of LiDAR data to describe the ecological attributes of riparian zone (Hall et al, 2009;Johansen et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Small gaps that did not reach the 3 thresholds conditions were merged with riparian forest patches, considering they do not represent a meaningful gap in riparian forest continuity. This approach was adapted from Johansen et al (2010), who used a plant projective cover layer instead of the CHM layer to map riparian forest patches in the context of a dry sub-tropical savannah.…”

Section: Disaggregation and Re-aggregation Processmentioning

confidence: 99%

“…References Johansen et al (2010) tools for mapping stream channels and riparian zones and for measuring key attributes to assess the ecological integrity of riparian zones. We used a 35 km section of the Houille River, a tributary of the Meuse (Belgium), as a model system in the method development.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LiDAR derived ecological integrity indicators for riparian zones: Application to the Houille river in Southern Belgium/Northern France

Michez¹,

Piégay²,

Toromanoff³

et al. 2013

Ecological Indicators

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a b s t r a c tRiparian zones are central landscape features providing several ecosystem services and are exceptionally rich in biodiversity. Despite their relatively low area coverage, riparian zones consequently represent a major concern for land and water resource managers confirmed within several European directives. These directives involve effective multi-scale monitoring to assess their conditions and their ability to carry out their functions. The objective of this research was to develop automated tools to provide from a single aerial LiDAR dataset new mapping tools and keystone riparian zone attributes assessing the ecological integrity of the riparian zone at a network scale (24 km).Different metrics were extracted from the original LiDAR point cloud, notably the Digital Terrain Model and Canopy Height Model rasters, allowing the extraction of riparian zones attributes such as the wetted channel (0.89 m; mean residual) and floodplain extents (6.02 m; mean residual). Different riparian forest characteristics were directly extracted from these layers (patch extent, overhanging character, longitudinal continuity, relative water level, mean and relative standard deviation of tree height). Within the riparian forest, the coniferous stands were distinguished from deciduous and isolated trees, with high accuracy (87.3%, Kappa index).Going further the mapping of the indicators, our study proposed an original approach to study the riparian zone attributes within different buffer width, from local scale (50 m long channel axis reach) to a network scale (ca. 2 km long reaches), using a disaggregation/re-agraggation process. This novel approach, combined to graphical presentations of the results allow natural resource managers to visualise the variation of upstream-downstream attributes and to identify priority action areas.In the case study, results showed a general decrease of the riparian forests when the river crosses built-up areas. They also highlighted the lower flooding frequency of riparian forest patches in habitats areas.Those results showed that LiDAR data can be used to extract indicators of ecological integrity of riparian zones in temperate climate zone. They will enable the assessment of the ecological integrity of riparian zones to be undertaken at the regional scale (13,000 km, completely covered by an aerial LIDAR survey in 2014).

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Section: Introductionmentioning

confidence: 99%

Section: Disaggregation and Re-aggregation Processmentioning

confidence: 99%

See 1 more Smart Citation

LiDAR derived ecological integrity indicators for riparian zones: Application to the Houille river in Southern Belgium/Northern France

Michez¹,

Piégay²,

Toromanoff³

et al. 2013

Ecological Indicators

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“…NDSM height: In typical vegetation mapping studies, the most important LIDAR-derived parameter is the vegetation canopy height [57,94,95]. This is represented by the Normalized Differential Surface Model (NDSM), which is generated by creating both a Digital Terrain Model (DTM) from the LIDAR points reflected from the actual terrain surface, and a Digital Surface Model (DSM) from the points corresponding to the top of the canopy, and then subtracting the DTM height from the DSM height.…”

Section: Airborne Surveymentioning

confidence: 99%

Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types

Zlinszky

Schroiff²,

Kania³

et al. 2014

Remote Sensing

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There is increasing demand for reliable, high-resolution vegetation maps covering large areas. Airborne laser scanning data is available for large areas with high resolution and supports automatic processing, therefore, it is well suited for habitat mapping. Lowland hay meadows are widespread habitat types in European grasslands, and also have one of the highest species richness. The objective of this study was to test the applicability of airborne laser scanning for vegetation mapping of different grasslands, including the Natura 2000 habitat type lowland hay meadows. Full waveform leaf-on and leaf-off point clouds were collected from a Natura 2000 site in Sopron, Hungary, covering OPEN ACCESS Remote Sens. 2014, 6 8057 several grasslands. The LIDAR data were processed to a set of rasters representing point attributes including reflectance, echo width, vegetation height, canopy openness, and surface roughness measures, and these were fused to a multi-band pseudo-image. Random forest machine learning was used for classifying this dataset. Habitat type, dominant plant species and other features of interest were noted in a set of 140 field plots. Two sets of categories were used: five classes focusing on meadow identification and the location of lowland hay meadows, and 10 classes, including eight different grassland vegetation categories. For five classes, an overall accuracy of 75% was reached, for 10 classes, this was 68%. The method delivers unprecedented fine resolution vegetation maps for management and ecological research. We conclude that high-resolution full-waveform LIDAR data can be used to detect grassland vegetation classes relevant for Natura 2000.

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“…Because of the typical linear and narrow shape of the riparian zones, fieldbased monitoring involves sampling, high labor costs, and time-consuming travels (Debruxelles et al 2009;Myers 1989). The continuous improvement of the spatial resolution of remote sensing data combined with more powerful computer capacity and new geomatic procedures to extract information make the remote sensing approach more competitive (Alber and Piégay 2011;Carbonneau and Piégay 2012;Johansen et al 2010;Michez et al 2013;Roux et al 2014). The use of this very-high-resolution (VHR) imagery in a multitemporal approach is an emerging topic (Ardila et al 2012;Champion 2012;Lasaponara et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Michez

Piégay

Lisein

et al. 2016

Environ Monit Assess

198

179

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Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very-high-resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic. The trend is reinforced by the recent and rapid growth of the use of the unmanned aerial system (UAS), which has prompted the development of innovative methodology. Our study proposes a methodological framework to explore how a set of multi-temporal images acquired during a vegetative period can differentiate some of the deciduous riparian forest species and their health conditions. More specifically, the developed approach intends to identify, through a process of variable selection, which variables derived from UAS imagery and which scale of image analysis are the most relevant to our objectives.The methodological framework is applied to two study sites to describe the riparian forest through two fundamental characteristics: the species composition and the health condition. These characteristics were selected not only because of their use as proxies for the riparian zone ecological integrity but also because of their use for river management.The comparison of various scales of image analysis identified the smallest object-based image analysis (OBIA) objects (ca. 1 m 2 ) as the most relevant scale. Variables derived from spectral information (bands ratios) were identified as the most appropriate, followed by variables related to the vertical structure of the forest. Classification results show good overall accuracies for the species composition of the riparian forest (five classes, 79.5 and 84.1 % for site 1 and site 2). The classification scenario regarding the health condition of the black alders of the site 1 performed the best (90.6 %).The quality of the classification models developed with a UAS-based, cost-effective, and semi-automatic approach competes successfully with those developed using more expensive imagery, such as multi-spectral and hyperspectral airborne imagery. The high overall accuracy results obtained by the classification of the diseased alders open the door to applications dedicated to monitoring of the health conditions of riparian forest. Our methodological framework will allow UAS users to manage large imagery metric datasets derived from those dense time series.

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Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

Cited by 62 publications

References 42 publications

LiDAR derived ecological integrity indicators for riparian zones: Application to the Houille river in Southern Belgium/Northern France

LiDAR derived ecological integrity indicators for riparian zones: Application to the Houille river in Southern Belgium/Northern France

Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

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