Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review

Lawley, V.; Lewis, Mervyn K.; Clarke, Kenneth; Ostendorf, Bertram

doi:10.1016/j.ecolind.2015.03.021

Cited by 170 publications

(132 citation statements)

References 134 publications

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“…In contrast, the first-order derivative spectra, which may be calculated approximately by dividing the difference in reflectance between successive wavebands, can eliminate background noise and resolve overlapping spectral features [58]. This approach is effective in enhancing weak spectral features and extracting critical wavelengths by reducing the influence of trends or low-frequency noise [59].…”

Section: Best Indices For Each Datasetmentioning

confidence: 99%

Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests

Sonobe

Wang

2017

Remote Sensing

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Abstract:The reflectance properties of leaves are influenced by diverse biochemical components including chlorophyll, one of the key indicators related to plant photosynthesis and plant stress. Although a number of hyperspectral indices have been proposed for quantifying leaf chlorophyll concentrations, their applications are largely restricted to where they were developed and can hardly provide satisfactory results in other cases. In this study, universally applicable hyperspectral indices calculated from both original and first-order derivative spectra were identified for quantifying leaf chlorophyll concentrations in deciduous forests. Using the main criteria of the ratio of performance to deviation (RPD) and the widely applicable information criterion (WAIC), new hyperspectral indices were proposed for quantifying chlorophyll concentrations in four independent datasets. The results revealed that the normalized derivative difference between the green peak (520-540 nm) and the end of the red edge (720-740 nm) were effective. The normalized difference type of index using reflectance derivatives at 522 and 728 nm, dND (522, 728), was the most effective index for quantifying chlorophyll concentrations, with an R 2 of 0.807 and a lowest root mean square error of 8.67 µg/cm 2 , n = 816. This index was also validated based on a simulated dataset generated from the model of PROpriétés SPECTrales Version 5 (PROSPECT 5). Its applicability for assessing chlorophyll content in various deciduous forests was hence demonstrated. We foresee its wide application in the future.

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Section: Best Indices For Each Datasetmentioning

confidence: 99%

Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests

Sonobe

Wang

2017

Remote Sensing

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“…Comprehensive analyses of FH have been conducted based on LandTrend and LandSat Time Series (LTS) [392][393][394][395][396]. Comprehensive information and reviews of remote-sensing change detection and ecological monitoring can be found in [397,398].…”

Section: Monitoring Stress On Vegetation In Fes With Rsmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

et al. 2016

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Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH.

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“…Vegetation also provides habitat for many threatened and endangered species. In fact, native vegetation around the world is under threat from historical and on-going clearance, overgrazing, logging, invasive species, increasing soil and water salinity, poor land management and other factors, resulting in a degradation of natural ecosystem (Lawley et al 2016). On the other hand, considering the unprecedented urban population and development growth trend, a plethora of valuable trees are cut annually, which will cause several negative consequences for the environment and ecosystem.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed procedure specifically was applied for vegetation detection in remotely sensed images captured with two sensors (a conventional visible camera and a multispectral camera) mounted on a drone. Lawley et al (2016) reviewed the attributes of vegetation identification as important features for monitoring vegetation. Those indicators that can be monitored using site-based, RS-based and a conflation of both methods were found.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Monitoring of Mashhad Using an Object-Oriented Post Classification Comparison Method

Moghadam

Delavar

Forati

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:By and large, todays mega cities are confronting considerable urban development in which many new buildings are being constructed in fringe areas of these cities. This remarkable urban development will probably end in vegetation reduction even though each mega city requires adequate areas of vegetation, which is considered to be crucial and helpful for these cities from a wide variety of perspectives such as air pollution reduction, soil erosion prevention, and eco system as well as environmental protection. One of the optimum methods for monitoring this vital component of each city is multi-temporal satellite images acquisition and using change detection techniques. In this research, the vegetation and urban changes of Mashhad, Iran, were monitored using an object-oriented (marker-based watershed algorithm) post classification comparison (PCC) method. A Bi-temporal multi-spectral Landsat satellite image was used from the study area to detect the changes of urban and vegetation areas and to find a relation between these changes. The results of this research demonstrate that during 1987-2017, Mashhad urban area has increased about 22525 hectares and the vegetation area has decreased approximately 4903 hectares. These statistics substantiate the close relationship between urban development and vegetation reduction. Moreover, the overall accuracies of 85.5% and 91.2% were achieved for the first and the second image classification, respectively. In addition, the overall accuracy and kappa coefficient of change detection were assessed 84.1% and 70.3%, respectively.

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Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review

Cited by 170 publications

References 134 publications

Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests

Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

Vegetation Monitoring of Mashhad Using an Object-Oriented Post Classification Comparison Method

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