Multi‐Frequency SAR Applications for Land Cover Classification Within Copernicus Downstream Services

Spies, Bernard; Lamb, Alistair; Balzter, Heiko

doi:10.1002/9781118793787.ch5

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…SD (TCB), SD (TCG), and SD (TCW) are the corresponding standard deviations within the mature forest class, and hence nTCB, nTCG, and nTCW indicate the Brightness, Greenness, and Wetness normalized by the statistics of a "mature forest" class, respectively. The "mature forest" class was identified using Corine High-Resolution Layers [45]. After the normalization, the three component indices were combined linearly to acquire DI [24] as follows:…”

Section: Spectral Index Abbreviation Formula Referencesmentioning

confidence: 99%

Assessment of Spectral Vegetation Indices Performance for Post-Fire Monitoring of Different Forest Environments

Avetisyan,

Stankova,

Dimitrov

2023

Fire

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Although wildfires are a common disturbance factor to the environment, some of them can cause significant environmental and socioeconomic losses, affecting ecosystems and people worldwide. The wildfire identification and assessment of their effects on damaged forest areas is of great importance for provision of effective actions on their management and preservation. Forest regrowth after a fire is a continuously evolving and dynamic process, and the accuracy assessment of different remote sensing indices for its evaluation is a complicated task. The implementation of this task cannot rely on the standard procedures. Therefore, we suggested a method involving delineation of dynamic boundaries between conditional categories within burnt forest areas by application of spectral reflectance characteristics (SRC). This study compared the performance of firmly established for fire monitoring differenced vegetation indices—Normalized Difference Vegetation Index (dNDVI) and Normalized Burn Ratio (dNBR) and tested the capabilities of tasseled cap-derived differenced Disturbance Index (dDI) for post-fire monitoring purposes in different forest environments (Boreal Mountain Forest (BMF), Mediterranean Mountain Forest (MMF), Mediterranean Hill Forest (MHF)). The accuracy assessment of the tree indices was performed using Very High Resolution (VHR) aerial and satellite data. The results show that dDI has an optimal performance in monitoring post-fire disturbances in more difficult-to-be-differentiated classes, whereas, for post-fire regrowth, the more appropriate is dNDVI. In the first case, dDI has an overall accuracy of 50%, whereas the accuracy of dNBR and dNDVI is barely 35% and 36%. Moreover, dDI shows better performance in 16 accuracy metrics (from 17). In the second case, dNDVI has an overall accuracy of 59%, whereas those of dNBR and dDI are 55% and 52%, and the accuracy metrics in which dNDVI shows better performance than the other two indices are 11 (from 13). Generally, the studied indices showed higher accuracy in assessment of post-fire disturbance rather than of the post-fire forest regrowth, implicitly at test areas—BMF and MMF, and contrary opposite result in the accuracy at MHF. This indicates the relation of the indices’ accuracy to the heterogeneity of the environment.

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Section: Spectral Index Abbreviation Formula Referencesmentioning

confidence: 99%

Assessment of Spectral Vegetation Indices Performance for Post-Fire Monitoring of Different Forest Environments

Avetisyan,

Stankova,

Dimitrov

2023

Fire

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“…All these enhance the information that was utilized for land cover mapping with ease and accuracy than individual data. (Kanellopoulos et al 1992;Lee and Lathrop 2006;Tan KC et al 2010;Salehi et al 2013;Singh S et al 2018) Hyperspectral (Thenkabail et al 2004;Pal 2006;Tan Q and Wang 2007;Liu and Li 2013;Hegde et al 2014;Vijayan et al 2014;Pandey et al 2018) SAR (Henderson F 1975;Henderson FM and Xia 1997;Saatchi et al 2000;Simard et al 2000;da Costa Freitas et al 2008;Werner et al 2014;Clerici et al 2017;Hagensieker et al 2017;Spies et al 2017) 3 Multi-Temporal datasets Different time-period data (Roberts et al 2002;Engdahl and Hyyppa 2003;Rogan et al 2003;Budreski et al 2007;Pandey et al 2012;Pandey et al 2013;Sexton et al 2013;Campbell et al 2015;Feng et al 2015;Bai et al 2017…”

Section: Spatial Dimension 189mentioning

confidence: 99%

Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

Pandey

Koutsias

Petropoulos

et al. 2019

Geocarto International

124

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Land use/Land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment. Earth Observation (EO) technology provides an informative source of data covering the entire globe in a spatial and spectral resolution appropriate to better and easier classify land cover than traditional or conventional methods. The use of high spatial and spectral resolution imagery from EO sensors has increased remarkably over the past decades, as more and more platforms are placed in orbit and new applications emerge in different disciplines.The aim of the present review work is to provide all-inclusive critical reflection on the state of the art in the use of EO technology in LULC mapping and change detection. The emphasis is placed on providing an overview of the different EO datasets, spatial-spectral-temporal characteristics of satellite data and classification approaches employed in land cover classification. The review concludes providing recommendations and remarks on what should be done in order to overcome hurdle faced using above-mentioned problems in LULC mapping. This also provides information on using classifier algorithms depending upon the data types and dependent on the regional ecosystems.One of the main messages of our review is that in future, there will be a need to assemble techniques specifically used in LULC with their merit and demerits that will enable more comprehensive understanding at regional or global scale and improve understanding between different land cover relationship and variability among them and these remains to be seen.

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Multi‐Frequency SAR Applications for Land Cover Classification Within Copernicus Downstream Services

Cited by 2 publications

References 22 publications

Assessment of Spectral Vegetation Indices Performance for Post-Fire Monitoring of Different Forest Environments

Assessment of Spectral Vegetation Indices Performance for Post-Fire Monitoring of Different Forest Environments

Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

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