Mapping red edge-based vegetation health indicators using Landsat TM data for Australian native vegetation cover

Shamsoddini, Ali; Raval, Simit

doi:10.1007/s12145-018-0347-5

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…When wildfires damage vegetation and consequently the photosynthetic cells where chlorophyll is present, this affects the chlorophyll content in plants and reduces reflectance in the red spectral region [30,31]. Equally important, the red-edge spectral region is sensitive to subtle changes in chlorophyll [34]. Changes to chlorophyll can either broaden or shorten its reflectance between 670-680nm [59].…”

Section: Discussionmentioning

confidence: 99%

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Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

Ngadze¹,

Mpakairi

Kavhu

et al. 2020

PLoS ONE

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When wildfires are controlled, they are integral to the existence of savannah ecosystems and play an intrinsic role in maintaining their structure and function. Ample studies on wildfire detection and severity mapping are available but what remains a challenge is the accurate mapping of burnt areas in heterogenous landscapes. In this study, we tested which spectral bands contributed most to burnt area detection when using Sentinel-2 and Landsat 8 multispectral sensors in two study sites. Post-fire Sentinel 2A and Landsat 8 images were classified using the Random Forest (RF) classifier. We found out that, the NIR, Red, Red-edge and Blue spectral bands contributed most to burned area detection when using Landsat 8 and Sentinel 2A. We found out that, Landsat 8 had a higher classification accuracy (OA = 0.92, Kappa = 0.85 and TSS = 0.84)) in study site 1 as compared to Sentinel-2 (OA = 0.86, Kappa = 0.74 and TSS = 0.76). In study site 2, Sentinel-2 had a slightly higher classification accuracy (OA = 0.89, Kappa = 0.67 and TSS = 0.64) which was comparable to that of Landsat 8 (OA = 0.85, Kappa = 0.50 and TSS = 0.41). Our study adds rudimentary knowledge on the most reliable sensor allowing reliable estimation of burnt areas and improved post-fire ecological evaluations on ecosystem damage and carbon emission.

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Section: Discussionmentioning

confidence: 99%

“…However, Sentinel-2's high sensitivity to cloud cover [32] has been attributed as its main flaw in burned area detection. However, besides these setbacks, Sentinel-2 and Landsat 8 remain the widely used products in natural resources management [9,20,25,33,34].…”

Section: Introductionmentioning

confidence: 99%

Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

Ngadze¹,

Mpakairi

Kavhu

et al. 2020

PLoS ONE

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“…The outcome resolution encompasses a minimum of 3 × 3 pixels (0.8 ha) necessary in determining a category to map. This resolution has been shown to be effective to identify vegetation (Li et al, 2021; Shamsoddini & Raval, 2018) and, specifically, bofedales (Garcia Dulanto, 2018; García & Lleellish, 2012). The final postprocessing step, (iii) a water mask based on the Normalized Difference Water Index was applied to avoid confusion of bofedales with lakes and other open water bodies.…”

Section: Methodsmentioning

confidence: 99%

Seasonal water storage and release dynamics of bofedal wetlands in the Central Andes

Ross

Mendoza

Drenkhan

et al. 2023

Hydrological Processes

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Tropical high‐Andean wetlands, locally known as ‘bofedales’, are key ecosystems sustaining biodiversity, carbon sequestration, water provision and livestock farming. Bofedales' contribution to dry season baseflows and sustaining water quality is crucial for downstream water security. The sensitivity of bofedales to climatic and anthropogenic disturbances is therefore of growing concern for watershed management. This study aims to understand seasonal water storage and release characteristics of bofedales by combining remote sensing analysis and ground‐based monitoring for the wet and dry seasons of late 2019 to early 2021, using the glacierised Vilcanota‐Urubamba basin (Southern Peru) as a case study. A network of five ultrasound loggers was installed to obtain discharge and water table data from bofedal sites across two headwater catchments. The seasonal extent of bofedales was mapped by applying a supervised machine learning model using Random Forest on imagery from Sentinel‐2 and NASADEM. We identified high seasonal variability in bofedal area with a total of 3.5% and 10.6% of each catchment area, respectively, at the end of the dry season (2020), which increased to 15.1% and 16.9%, respectively, at the end of the following wet season (2021). The hydrological observations and bofedal maps were combined into a hydrological conceptual model to estimate the storage and release characteristics of the bofedales, and their contribution to runoff at the catchment scale. Estimated lag times between 1 and 32 days indicate a prolonged bofedal flow contribution throughout the dry season (about 74% of total flow). Thus, our results suggest that bofedales provide substantial contribution to dry season baseflow, water flow regulation and storage. These findings highlight the importance of including bofedales in local water management strategies and adaptation interventions including nature‐based solutions that seek to support long‐term water security in seasonally dry and rapidly changing Andean catchments.

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“…The red-edge spectral region is highly sensitive to variation in foliar nitrogen, a constituent of LMA [78]. Recent research demonstrates that the red-edge band improves plant trait mapping [91]. Red-edge indices, such as the Red-edge Chlorophyll Index (CIrededge) and Sentinel-2 Red-edge Position (S2REP), have been successfully used to estimate LMA [44].…”

Section: Spaceborne Satellite Platformsmentioning

confidence: 99%

Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook

2021

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Quantitative remote sensing of leaf traits offers an opportunity to track biodiversity changes from space. Augmenting field measurement of leaf traits with remote sensing provides a pathway for monitoring essential biodiversity variables (EBVs) over space and time. Detailed information on key leaf traits such as leaf mass per area (LMA) is critical for understanding ecosystem structure and functioning, and subsequently the provision of ecosystem services. Although studies on remote sensing of LMA and related constituents have been conducted for over three decades, a comprehensive review of remote sensing of LMA—a key driver of leaf and canopy reflectance—has been lacking. This paper reviews the current state and potential approaches, in addition to the challenges associated with LMA estimation/retrieval in forest ecosystems. The physiology and environmental factors that influence the spatial and temporal variation of LMA are presented. The scope of scaling LMA using remote sensing systems at various scales, i.e., near ground (in situ), airborne, and spaceborne platforms is reviewed and discussed. The review explores the advantages and disadvantages of LMA modelling techniques from these platforms. Finally, the research gaps and perspectives for future research are presented. Our review reveals that although progress has been made, scaling LMA to regional and global scales remains a challenge. In addition to seasonal tracking, three-dimensional modeling of LMA is still in its infancy. Over the past decade, the remote sensing scientific community has made efforts to separate LMA constituents in physical modelling at the leaf level. However, upscaling these leaf models to canopy level in forest ecosystems remains untested. We identified future opportunities involving the synergy of multiple sensors, and investigated the utility of hybrid models, particularly at the canopy and landscape levels.

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Mapping red edge-based vegetation health indicators using Landsat TM data for Australian native vegetation cover

Cited by 10 publications

References 37 publications

Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

Seasonal water storage and release dynamics of bofedal wetlands in the Central Andes

Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook

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