Delineating wetland areas from the cut-and-fill method using a Digital Elevation Model (DEM)

Lidzhegu, Zwidofhelangani; Ellery, W.N.; Mantel, Sukhmani; Hughes, D.

doi:10.1080/03736245.2019.1638825

Cited by 9 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was further demonstrated when the individual classes were considered; elevation proved the most important feature for delineating water. We concur with Lidzhegu et al [ 38 ] that topographic information derived from the SRTM can better offer different geomorphologic characteristics which reflect the habitats of different vegetation types and can help in their identification. Despite the Sentinel-1 classifications (S1, S1+) proving relatively inaccurate overall, Sentinel-1 features were consistently rated highly in combined datasets.…”

Section: Discussionsupporting

confidence: 92%

“…Lidzhegu et al ([ 38 ], p. 97) defined a wetland as “ an environment where fluvial and/or tectonic processes have shaped the landscape such that topographic conditions become suited for prolonged inundation sufficient for soil oxidation and establishment of hydrophytes ”. Inherent in this definition is the importance of topographic features for wetland delineation.…”

Section: Methodsmentioning

confidence: 99%

“…Ancillary features that help to identify topographic effects on hydrological processes were thus derived from the SRTM digital elevation data at 30 m resolution [ 64 ]. A total of three features were derived at each DEM pixel: slope, aspect and elevation [ 14 , 36 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…It offers detailed information on the often difficult to detect characteristics of vegetation such as moisture, roughness and shape [ 35 ]. Additionally, data from the Shuttle Radar Topographic Mission (SRTM) have been used to successfully identify hydrological landscape units in cloud-persistent areas (e.g., [ 36 , 37 , 38 ]). SRTM data provides an estimate of elevation and is useful for identifying large-scale topographical boundaries within tropical landscapes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland

Amoakoh

Aplin

Awuah

et al. 2021

Sensors

View full text Add to dashboard Cite

Tropical peatlands such as Ghana’s Greater Amanzule peatland are highly valuable ecosystems and under great pressure from anthropogenic land use activities. Accurate measurement of their occurrence and extent is required to facilitate sustainable management. A key challenge, however, is the high cloud cover in the tropics that limits optical remote sensing data acquisition. In this work we combine optical imagery with radar and elevation data to optimise land cover classification for the Greater Amanzule tropical peatland. Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission (SRTM) imagery were acquired and integrated to drive a machine learning land cover classification using a random forest classifier. Recursive feature elimination was used to optimize high-dimensional and correlated feature space and determine the optimal features for the classification. Six datasets were compared, comprising different combinations of optical, radar and elevation features. Results showed that the best overall accuracy (OA) was found for the integrated Sentinel-2, Sentinel-1 and SRTM dataset (S2+S1+DEM), significantly outperforming all the other classifications with an OA of 94%. Assessment of the sensitivity of land cover classes to image features indicated that elevation and the original Sentinel-1 bands contributed the most to separating tropical peatlands from other land cover types. The integration of more features and the removal of redundant features systematically increased classification accuracy. We estimate Ghana’s Greater Amanzule peatland covers 60,187 ha. Our proposed methodological framework contributes a robust workflow for accurate and detailed landscape-scale monitoring of tropical peatlands, while our findings provide timely information critical for the sustainable management of the Greater Amanzule peatland.

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland

Amoakoh

Aplin

Awuah

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, the site investigation is often impractical as the natural condition may not be accessible. Various studies used different approaches for wetland delineation including topographic wetness index [9], terrain indices [10], LiDAR [11], and cut and fill approach [12]. Fortunately, with the evolution and advances in remote sensing technology, it has been recognized as the best alternative for an accurate source of data for large-scale applications and studies such as water body mapping and change detection [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Deterministic Topographic Wetland Index Based on LiDAR-Derived DEM for Delineating Open-Water Wetlands

Bian

Melesse

León

et al. 2021

Water

View full text Add to dashboard Cite

Wetlands play a significant role in flood mitigation. Remote sensing technologies as an efficient and accurate approach have been widely applied to delineate wetlands. Supervised classification is conventionally applied for remote sensing technologies to improve the wetland delineation accuracy. However, performing supervised classification requires preparing the training data, which is also considered time-consuming and prone to human mistakes. This paper presents a deterministic topographic wetland index to delineate wetland inundation areas without performing supervised classification. The classic methods such as Normalized Difference Vegetation Index, Normalized Difference Water Index, and Topographic Wetness Index were chosen to compare with the proposed deterministic topographic method on wetland delineation accuracy. The ground truth sample points validated by Google satellite imageries from four different years were used for the assessment of the delineation overall accuracy. The results show that the proposed deterministic topographic wetland index has the highest overall accuracy (98.90%) and Kappa coefficient (0.641) among the selected approaches in this study. The findings of this paper will provide an alternative approach for delineating wetlands rapidly by using solely the LiDAR-derived Digital Elevation Model.

show abstract

The Okavango Delta Peatlands

Ellery

2022

Landscapes and Landforms of Botswana

View full text Add to dashboard Cite

Delineating wetland areas from the cut-and-fill method using a Digital Elevation Model (DEM)

Cited by 9 publications

References 27 publications

Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland

Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland

A Deterministic Topographic Wetland Index Based on LiDAR-Derived DEM for Delineating Open-Water Wetlands

The Okavango Delta Peatlands

Contact Info

Product

Resources

About