Tracking tree canopy cover changes in space and time in High Nature Value Farmland to prioritize reforestation efforts

Soares, Cristina; Príncipe, Adriana; Köbel, Melanie; Nunes, Alice; Branquinho, Cristina; Pinho, Pedro

doi:10.1080/01431161.2018.1475777

Cited by 17 publications

(14 citation statements)

References 28 publications

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“…The use of remote sensing to monitor temporal trends in ecosystem functioning should be considered to understand the ecological responses to drivers of change in Mediterranean ecosystems. Soares et al (2018) used aerial photography and Landsat satellite imagery to quantify spatial and temporal changes in montado's tree cover. This was done relating Landsat-derived normalized difference vegetation index (NDVI) to aerial photography photointerpretation, over a precipitation gradient in a Mediterranean dryland area.…”

Section: Prefacementioning

confidence: 99%

“…This special issue has contributed in providing new insights in methodological and operational issues associated with SPS mapping, assessment, and monitoring by using remote-sensing and ground-based approaches. It has also demonstrated the wide range of applications able to be explored using different remote-sensing platforms, proximal sensors, and derived products, which include spaceborne/satellite-based applications (Abid, Bargaoui, and Mannaerts 2018;Allen et al 2018;Cárdenas et al, 2018;Demisse et al 2018;Lourenço et al 2018;Soares et al 2018;Filizzola et al, 2018;Godinho, Guiomar, and Gil 2018);…”

Section: Concluding Thoughtsmentioning

confidence: 99%

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Advances in remote-sensing applications in silvo-pastoral systems

Godinho

Surový

Sousa

et al. 2018

International Journal of Remote Sensing

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Section: Prefacementioning

confidence: 99%

Section: Concluding Thoughtsmentioning

confidence: 99%

Advances in remote-sensing applications in silvo-pastoral systems

Godinho

Surový

Sousa

et al. 2018

International Journal of Remote Sensing

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“…One of the most used VIs is the Normalized Difference Vegetation Index (NDVI), measuring the "greenness" of vegetation, which is positively correlated with canopy density [20]. It is widely used at different spatial and temporal scales, assessing largescale vegetation trends [21,22]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Based Monitoring of Climate Effects on the Inter-Annual Variability of Different Plant Functional Types in Mediterranean Cork Oak Woodlands

et al. 2022

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Using remotely sensed data to estimate the biophysical properties of vegetation in woodlands is a challenging task due to their heterogeneous nature. The objective of this study was to assess the biophysical parameters of different vegetation types (cork oak trees, shrubs and herbaceous vegetation) in cork oak woodland through the analysis of temporal trends in spectral vegetation indices (VIs). A seven-year database (from 2011 until 2017) of in situ observations collected with a field spectroradiometer with a monthly basis was used and four VIs were derived, considered as proxies for several biophysical properties of vegetation such as biomass (Normalized Difference Vegetation Index—NDVI); chlorophyll content (MERIS Terrestrial Chlorophyll Index-MTCI), tissue water content (Normalized Difference Water Index—NDWI) and the carotenoid/chlorophyll ratio (Photochemical Reflectance Index—PRI). During the analyzed period, some key meteorological data (precipitation, temperature, relative air humidity and global radiation) were collected for the study site, aggregated at three different time-lags (short period (30 d), medium period (90 d) and hydrological period (HIDR)), and their relationship with VIs was analyzed. The results showed different trends for each vegetation index and vegetation type. In NDVI and NDWI, herbaceous vegetation showed a highly marked seasonal trend, whereas for MTCI, it was the cork oak and Cistus salvifolius, and for PRI, it was Ulex airensis that showed the marked seasonal trend. Shrubs have large differences depending on the species: the shallow-rooted Cistus salvifolius showed a higher seasonal variability than the deep-rooted Ulex airensis. Our results revealed the importance of temperature and precipitation as the main climatic variables influencing VI variability in the four studied vegetation types. This study sets up the relationships between climate and vegetation indices for each vegetation type. Spectral vegetation indices are useful tools for assessing the impact of climate on vegetation, because using these makes it easier to monitor the amount of “greenness”, biomass and water stress of vegetation than assessing the photosynthetic efficiency. Proximal remote sensing measurements are fundamental for the correct use of remote sensing in monitoring complex agroforest ecosystems, largely used to inform policies to improve resilience to drought, particularly in the Mediterranean region.

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“…Presently, in the assessment of TCC detection there is a dichotomy occurring between research examining small-scale spatial changes over short time periods (< 50 years) [ 8 , 9 ], and large-scale spatial change examining timescales within 1973 –present [ 27 – 29 ]. However, there are limited studies that have tried to combine canopy data types to allow for longer period (> 50 years) examinations of TCC on small scales.…”

Section: Introductionmentioning

confidence: 99%

Combining aerial photos and LiDAR data to detect canopy cover change in urban forests

2022

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The advancement and accessibility of high-resolution remotely sensed data has made it feasible to detect tree canopy cover (TCC) changes over small spatial scales. However, the short history of these high-resolution collection techniques presents challenges when assessing canopy changes over longer time scales (> 50 years). This research shows how using high-resolution LiDAR data in conjunction with historical aerial photos can overcome this limitation. We used the University of British Columbia’s Point Grey campus in Vancouver, Canada, as a case study, using both historical aerial photographs from 1949 and 2015 LiDAR data. TCC was summed in 0.05 ha analysis polygons for both the LiDAR and aerial photo data, allowing for TCC comparison across the two different data types. Methods were validated using 2015 aerial photos, the means (Δ 0.24) and a TOST test indicated that the methods were statistically equivalent (±5.38% TCC). This research concludes the methods outlined is suitable for small scale TCC change detection over long time frames when inconsistent data types are available between the two time periods.

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Tracking tree canopy cover changes in space and time in High Nature Value Farmland to prioritize reforestation efforts

Cited by 17 publications

References 28 publications

Advances in remote-sensing applications in silvo-pastoral systems

Advances in remote-sensing applications in silvo-pastoral systems

Spectral-Based Monitoring of Climate Effects on the Inter-Annual Variability of Different Plant Functional Types in Mediterranean Cork Oak Woodlands

Combining aerial photos and LiDAR data to detect canopy cover change in urban forests

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