Monitoring height and greenness of non-woody floodplain vegetation with UAV time series

Iersel, W.K. van; Straatsma, Menno; Addink, E.A.; Middelkoop, H.

doi:10.1016/j.isprsjprs.2018.04.011

Cited by 85 publications

(72 citation statements)

References 39 publications

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“…LiDAR and multispectral imagery (e.g., visible and near‐infrared) sensors can be deployed with UAVs to continuously map species composition, invasive species distributions, diversity, coverage, canopy structure, and vertical plant structure at reach and project scales (Dronova, Spotswood, & Suding, ; van Iersel, Straatsma, Addink, & Middelkoop, , ). Although plant identification requires ground truthing to be paired with UAV data acquisition and rare species are likely to be missed by UAV‐based survey approaches, classification methods are continually improving the accuracy of discriminating communities and species with spectrally similar reflectance profiles (Dronova et al, ; van Iersel et al, ; van Iersel, Straatsma, Addink, & Middelkoop, ). Therefore, UAV‐based remote sensing approaches offer the ability derive many potentially useful metrics for riparian habitats at reach and project scales and will likely become a more frequently utilized tool in monitoring restoration effectiveness.…”

Section: Review Of Methods For Monitoring Floodplain Restorationmentioning

confidence: 99%

Monitoring the effectiveness of floodplain habitat restoration: A review of methods and recommendations for future monitoring

et al. 2019

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Floodplains are some of the most ecologically important and human-impacted habitats throughout the world. Large efforts are underway in North America, Europe, Australia, and elsewhere to restore floodplain habitats, not only to increase fish and aquatic biota but to restore ecological diversity. As the scale, number, and complexity of floodplain restoration projects has increased, so has the need for rigorous monitoring and evaluation to demonstrate effectiveness and guide future floodplain restoration efforts. Moreover, technological advances in remote sensing, genetics, and fish marking have been evolving rapidly and there is need to update guidance on the best methods for monitoring physical and biological response to floodplain restoration. A comprehensive review of the restoration literature located 180 papers that specifically examined the effectiveness of various floodplain restoration techniques. The various methods that were historically and currently used to evaluate the physical (channel and floodplain morphology, sediment, flow, water quality [temperature and nutrients]) and biological (fish, invertebrates, and aquatic and riparian plants) effectiveness of floodplain restoration were reviewed and used to provide recommendations for future monitoring. For each major physical and biological monitoring method, we discuss their importance, how they have historically been used to evaluate floodplain restoration, newer methodologies, and limitations or advantages of different methodologies and approaches. We then discuss monitoring the effectiveness of small (<2 km in main channel length) and large (>2 km of main channel length) floodplain projects, with recommendations for various study designs, parameters, and monitoring methodologies.

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Section: Review Of Methods For Monitoring Floodplain Restorationmentioning

confidence: 99%

Monitoring the effectiveness of floodplain habitat restoration: A review of methods and recommendations for future monitoring

et al. 2019

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“…Several uncertainties on data acquisition and processing might bias our analyses. First, although we conducted regular UAV flights using the same field flight and data processing protocols, a variety of factors, such as UAV positioning errors, varying flight weather conditions (cloud and wind regimes), and imperfect ground control point setup (due to inaccessibility of mudflats and mangrove forests), could result in uncertainties in RGB orthophoto of different flight dates [30,31]. Obvious spatial mismatch across different flight dates was found in some isolated S. alterniflora patches.…”

Section: Discussionmentioning

confidence: 99%

“…The limitations of satellite imagery have been overcome by the application of unmanned airborne vehicle (UAV) technologies [27][28][29][30][31]. With UAV, it is possible to acquire very high resolution (VHR) images to track the growth of S. alterniflora at the patch scale, and the availability of VHR orthophotography merged with UAV imagery provides an opportunity to capture detailed spatio-temporal dynamics of plant community dynamics at the landscape scale.…”

Section: Introductionmentioning

confidence: 99%

Tidal and Meteorological Influences on the Growth of Invasive Spartina alterniflora: Evidence from UAV Remote Sensing

et al. 2019

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Rapid invasion of Spartina alterniflora into Chinese coastal wetlands has attracted much attention. Many field and remote sensing studies have examined the spatio-temporal dynamics of S. alterniflora invasion; however, spatially explicit quantitative analyses of S. alterniflora invasion and its underlying mechanisms at both patch and landscape scales are seldom reported. To fill this knowledge gap, we integrated multi-temporal unmanned aerial vehicle (UAV) imagery, light detection and ranging (LiDAR)-derived elevation data, and tidal and meteorological time series to explore the growth potential (lateral expansion rates and canopy greenness) of S. alterniflora over the intertidal zone in a subtropical coastal wetland (Zhangjiang estuarine wetland, Fujian, China). Our analyses of patch expansion indicated that isolated S. alterniflora patches in this wetland experienced high lateral expansion over the past several years (averaged at 4.28 m/year in patch diameter during 2014–2017), and lateral expansion rates ( y , m/year) showed a statistically significant declining trend with increasing inundation ( x , h/day; 3 ≤ x ≤ 18 ): y = − 0.17 x + 5.91 , R 2 = 0.78 . Our analyses of canopy greenness showed that the seasonality of the growth potential of S. alterniflora was driven by temperature (Pearson correlation coefficient r = 0.76 ) and precipitation ( r = 0.68 ), with the growth potential peaking in early/middle summer with high temperature and adequate precipitation. Together, we concluded that the growth potential of S. alterniflora was co-regulated by tidal and meteorological regimes, in which spatial heterogeneity is controlled by tidal inundation while temporal variation is controlled by both temperature and precipitation. To the best of our knowledge, this is the first spatially explicit quantitative study to examine the influences of tidal and meteorological regimes on both spatial heterogeneity (over the intertidal zone) and temporal variation (intra- and inter-annual) of S. alterniflora at both patch and landscape scales. These findings could serve critical empirical evidence to help answer how coastal salt marshes respond to climate change and assess the vulnerability and resilience of coastal salt marshes to rising sea level. Our UAV-based methodology could be applied to many types of plant community distributions.

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“…Furthermore, a wide area stretching from a few meters to kilometers can be analyzed within a short time [10]. Nevertheless, as most satellite images have a middle or low spatial resolution, they are not sufficient for the precise analysis of plant species or small-scale ecosystems [11][12][13]. To solve this problem, UAVs are actively used for surveys [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Vegetation surveys using UAVs mainly obtain high resolution images and analyze the unique spectral information of plants to classify plant species or identify the distribution of habitats [7,13,16,[21][22][23][24]. For such an analysis, a vegetation index (VI) is usually applied, which is derived from the characteristics of various spectral wavelengths [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index

Song

Park

2020

Remote Sensing

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In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalized Difference Vegetation Index). As for multispectral characteristics, waterside plants showed the highest reflectance in Rnir, while floating plants had a higher reflectance in Rre. During the hottest season (on 25 June), the vegetation indices were the highest, and the habitat expanded near the edge of the reservoir. Among the vegetation indices, NDVI was the highest and NGRDI was the lowest. In particular, NGRDI had a higher value on the water surface and was not useful for detecting aquatic plants. NDVI and GNDVI, which showed the clearest difference between aquatic plants and water surface, were determined to be the most effective vegetation indices for detecting aquatic plants. Accordingly, the vegetation indices using multispectral UAV imagery turned out to be effective for detecting aquatic plants. A further study will be accompanied by a field survey in order to acquire and analyze more accurate imagery information.

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Monitoring height and greenness of non-woody floodplain vegetation with UAV time series

Cited by 85 publications

References 39 publications

Monitoring the effectiveness of floodplain habitat restoration: A review of methods and recommendations for future monitoring

Monitoring the effectiveness of floodplain habitat restoration: A review of methods and recommendations for future monitoring

Tidal and Meteorological Influences on the Growth of Invasive Spartina alterniflora: Evidence from UAV Remote Sensing

Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index

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