Land Vegetation Change Detection Using Remote Sensing and GIS

Aarfin, Samshul; Prakash, Rishi; Mishra, Nitin

doi:10.1007/978-981-15-6463-5_25

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…arid savannas, breakpoint analysis, ecosystem functioning, remote sensing, survival analysis, wetness severity (Aarfin et al, 2021;Hecheltjen et al, 2014;Liang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing is a valuable tool for monitoring vegetation dynamics and detecting change at various spatial and temporal scales (Aarfin et al., 2021; Hecheltjen et al., 2014; Liang et al., 2020). Conventional approaches using time series analysis of vegetation indices like the normalised difference vegetation index (NDVI) are commonly employed (Campo‐Bescós et al., 2013; Chamaille‐Jammes et al., 2007; Cho & Ramoelo, 2019; Mitchard & Flintrop, 2013; Munyati et al., 2011), but may not be optimal for assessing ecosystem functioning in arid and semi‐arid regions such as the savanna biome.…”

Section: Introductionmentioning

confidence: 99%

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Wetness severity increases abrupt shifts in ecosystem functioning in arid savannas

Vermeulen,

Verbist,

Van Meerbeek

et al. 2024

Global Change Biology

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The accelerating pace of climate change has led to unprecedented shifts in surface temperature and precipitation patterns worldwide, with African savannas being among the most vulnerable regions. Understanding the impacts of these extreme changes on ecosystem health, functioning and stability is crucial. This paper focuses on the detection of breakpoints, indicative of shifts in ecosystem functioning, while also determining relevant ecosystem characteristics and climatic drivers that increase susceptibility to these shifts within the semi‐arid to arid savanna biome. Utilising a remote sensing change detection approach and rain use efficiency (RaUE) as a proxy for ecosystem functioning, spatial and temporal patterns of breakpoints in the savanna biome were identified. We then employed a novel combination of survival analysis and remote sensing time series analysis to compare ecosystem characteristics and climatic drivers in areas experiencing breakpoints versus areas with stable ecosystem functioning. Key ecosystem factors increasing savanna breakpoint susceptibility were identified, namely higher soil sand content, flatter terrain and a cooler long‐term mean temperature during the wet summer season. Moreover, the primary driver of changes in ecosystem functioning in arid savannas, as opposed to wetter tropical savannas, was found to be the increased frequency and severity of rainfall events, rather than drought pressures. This research highlights the importance of incorporating wetness severity metrics alongside drought metrics to comprehensively understand climate–ecosystem interactions leading to abrupt shifts in ecosystem functioning in arid biomes. The findings also emphasise the need to consider the underlying ecosystem characteristics, including soil, topography and vegetation composition, in assessing ecosystem responses to climate change. While this research primarily concentrated on the southern African savanna as a case study, the methodological robustness of this approach enables its application to diverse arid and semi‐arid biomes for the assessment of climate–ecosystem interactions that contribute to abrupt shifts.

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“…arid savannas, breakpoint analysis, ecosystem functioning, remote sensing, survival analysis, wetness severity (Aarfin et al, 2021;Hecheltjen et al, 2014;Liang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wetness severity increases abrupt shifts in ecosystem functioning in arid savannas

Vermeulen,

Verbist,

Van Meerbeek

et al. 2024

Global Change Biology

View full text Add to dashboard Cite

show abstract

“…In terms of temporal resolution, an attractive source of remote sensing data for change detection is satellite imagery, such as Sentinel-2 multispectral data. In the past, a lot of research has been devoted to the detection of land cover changes on a large scale from satellite images [2][3][4][5][6][7][8][9][10], where the typical value of a minimum mapping unit (MMU) is on the order of 100 m 2 . The main advantage of using Sentinel-2 data is its low cost.…”

Section: Introductionmentioning

confidence: 99%

Detection and Monitoring of Woody Vegetation Landscape Features Using Periodic Aerial Photography

et al. 2023

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Woody vegetation landscape features, such as hedges, tree patches, and riparian vegetation, are important elements of landscape and biotic diversity. For the reason that biodiversity loss is one of the major ecological problems in the EU, it is necessary to establish efficient workflows for the registration and monitoring of woody vegetation landscape features. In the paper, we propose and evaluate a methodology for automated detection of changes in woody vegetation landscape features from a digital orthophoto (DOP). We demonstrate its ability to capture most of the actual changes in the field and thereby provide valuable support for more efficient maintenance of landscape feature layers, which is important for the shaping of future environmental policies. While the most reliable source for vegetation cover mapping is a combination of LiDAR and high-resolution imagery, it can be prohibitively expensive for continuous updates. The DOP from cyclic aerial photography presents an alternative source of up-to-date information for tracking woody vegetation landscape features in-between LiDAR recordings. The proposed methodology uses a segmentation neural network, which is trained with the latest DOP against the last known ground truth as the target. The output is a layer of detected changes, which are validated by the user before being used to update the woody vegetation landscape feature layer. The methodology was tested using the data of a typical traditional Central European cultural landscape, Goričko, in north-eastern Slovenia. The achieved F1 of per-pixel segmentation was 83.5% and 77.1% for two- and five-year differences between the LiDAR-based reference and the DOP, respectively. The validation of the proposed changes at a minimum area threshold of 100 m2 and a minimum area percentage threshold of 20% showed that the model achieved recall close to 90%.

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Land Vegetation Change Detection Using Remote Sensing and GIS

Cited by 2 publications

References 2 publications

Wetness severity increases abrupt shifts in ecosystem functioning in arid savannas

Wetness severity increases abrupt shifts in ecosystem functioning in arid savannas

Detection and Monitoring of Woody Vegetation Landscape Features Using Periodic Aerial Photography

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