Remote Sensing of Coastal Wetland Vegetation and Estuarine Water Properties

Klemas, V.

doi:10.1016/b978-0-12-751802-2.50037-0

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…Raw reflectance bands were more prevalent among the top 25 input variables for the OB classifications than for the PB classifications; nonetheless, all our classifications had a larger representation of raw reflectance bands among the top variables above that among the total number of input variables. The first studies using remote sensing to monitor coastal wetlands typically relied on raw reflectance alone [80] and achieved accuracies ranging from ~70 to 90% [26,31,81,82]. As expected, none of the raw reflectances extracted from the June images was among the top variables in our classifications because above-ground vegetative biomass had just started to emerge and likely showed less variation in reflectance compared to when it was more developed later in the growing season.…”

Section: Similarities and Differences In Variable Importance Between ...mentioning

confidence: 64%

Comparing Pixel- and Object-Based Approaches for Classifying Multispectral Drone Imagery of a Salt Marsh Restoration and Reference Site

Norris,

LaRocque,

Leblon

et al. 2024

Remote Sensing

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Monitoring salt marshes with remote sensing is necessary to evaluate their state and restoration. Determining appropriate techniques for this can be overwhelming. Our study provides insight into whether a pixel- or object-based Random Forest classification approach is best for mapping vegetation in north temperate salt marshes. We used input variables from drone images (raw reflectances, vegetation indices, and textural features) acquired in June, July, and August 2021 of a salt marsh restoration and reference site in Aulac, New Brunswick, Canada. We also investigated the importance of input variables and whether using landcover classes representing areas of change was a practical way to evaluate variation in the monthly images. Our results indicated that (1) the classifiers achieved overall validation accuracies of 91.1–95.2%; (2) pixel-based classifiers outperformed object-based classifiers by 1.3–2.0%; (3) input variables extracted from the August images were more important than those extracted from the June and July images; (4) certain raw reflectances, vegetation indices, and textural features were among the most important variables; and (5) classes that changed temporally were mapped with user’s and producer’s validation accuracies of 86.7–100.0%. Knowledge gained during this study will inform assessments of salt marsh restoration trajectories spanning multiple years.

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Section: Similarities and Differences In Variable Importance Between ...mentioning

confidence: 64%

Comparing Pixel- and Object-Based Approaches for Classifying Multispectral Drone Imagery of a Salt Marsh Restoration and Reference Site

Norris,

LaRocque,

Leblon

et al. 2024

Remote Sensing

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“…Whilst not dismissing the value of these latter methodologies (Klemas 1976;Howard 1991) they are costly and time-consuming, and rely heavily on the availability of high-technology equipment, data processing facilities and advanced interpretation skills. The aerial survey and data analysis of nearly 700 km of coastline reported here took only 4 days and cost less than US $1,800 (excluding the purchase of video camera and staff salaries).…”

Section: Discussionmentioning

confidence: 99%

A new technique for rapid assessment of mangrove degradation: a case study of shrimp farm encroachment in Thailand

Erftemeijer

2001

Trees

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A new technique (Aerial Video Manta Analysis) was developed for the rapid assessment of mangrove degradation due to logging and conversion for shrimp aquaculture. Video recordings were made of mangroves along the Andaman Sea coastline, Thailand, during a 2-day aerial survey (16-17 September 1997) in a small Cessna 206 aircraft at an altitude of 800-1,000 feet and flying speed of 100 knots/h. The video recordings were analysed following a modified Manta-Tow technique, originally developed for coral reef surveys. The percentage cover of good mangrove stands, mangrove areas degraded by logging, and mangrove areas converted into shrimp farms, was estimated for each consecutive 1-min interval of video playing time. The analysis revealed that 9.9±2.2% of the mangroves along the Andaman coastline were severely degraded by logging and 23.3±1.9% had been converted for shrimp farming (a total of 3,255±370 shrimp ponds were counted). The encroachment for shrimp farming was most severe (34%) in Krabi Province, while Phang-nga Province had the most (75%) good mangroves. These results demonstrate that illegal development of new shrimp farms along the Andaman Sea coast has been substantial in recent years. The results obtained by four independent observers using this new method were remarkably close (SD≤25% of mean). The new method allowed cheap, rapid and accurate evaluation of the status of the mangrove resources over a long stretch of coastline within a time-span of a few days. Aerial Video Manta Analysis is a valuable tool to support coastal conservation and management efforts.

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“…In compiling this legend (Aaviksoo 1988), the classification schemes of several aerial photographic studies (Jeglum and Boissoneau 1975;Zoltai et al 1975;Klemas 1976;Anderson et al 1976;Cowardin et al 1979;Vinogradov 1979;Rafstedt and Andersson 1981;Wastenson 1982;Loelkes et al 1983) were investigated and used as an example as required.…”

Section: Methodsmentioning

confidence: 99%

Changes of plant cover and land use types (1950's to 1980's) in three mire reserves and their neighbourhood in Estonia

Aaviksoo

1993

Landscape Ecol

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The dynamics of plant cover and land use types in three study areas -Keava (1192.05 ha), Meenikonna (1513.35 ha) and Nfitsi-V611a (888.61 ha) mire landscapes, each divided into natural (N) and anthropogenous (A) subareas, was investigated by repeated aerial photo (black-and-white panchromatic, 1:10,000) interpretation. Nineteen plant cover and land use (PC&LU) types were differentiated and three contour maps were drawn for each study area (corresponding to 1950's, 1960's and 1980's).The dynamics of mire landscapes were modelled by transition matrices P = [Pij], which contain the transition probabilities between i-th and j-th PC&LU types during the time interval between the aerial photographs. A total of 12 transition matrices were constructed.In A-subareas peat milling was started in the middle of the 60's whereas N-subareas acquired mire reserve status in 1981, which is manifested in a different development. 93% of N-subarea and 69% of the A-subarea remained unchanged from the 50's to the 80's. The increase of anthropogenous land use types in A-subareas of Keava, Meenikonna and Nfitsi-V611a were respectively 0.84%, 0.32% and 1.17% per year.Two different matrices (I and II period) were used to predict the future state of the study areas. The applicability of the transition matrix model has been discussed by comparing matrices of different base periods. Errors arising from photointerpretation, contour input, (transition) area measurement, matrix reduction etc. are evaluated.

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Remote Sensing of Coastal Wetland Vegetation and Estuarine Water Properties

Cited by 6 publications

References 14 publications

Comparing Pixel- and Object-Based Approaches for Classifying Multispectral Drone Imagery of a Salt Marsh Restoration and Reference Site

Comparing Pixel- and Object-Based Approaches for Classifying Multispectral Drone Imagery of a Salt Marsh Restoration and Reference Site

A new technique for rapid assessment of mangrove degradation: a case study of shrimp farm encroachment in Thailand

Changes of plant cover and land use types (1950's to 1980's) in three mire reserves and their neighbourhood in Estonia

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