Albedo estimated from remote sensing correlates with ecosystem multifunctionality in global drylands

Zhao, Yanchuang; Wang, Xinyuan; Novillo, Carlos J.; Arrogante-Funes, Patricia; Vázquez-Jiménez, René; Maestre, Fernando T.

doi:10.1016/j.jaridenv.2018.05.010

Cited by 30 publications

(26 citation statements)

References 75 publications

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“…Temperatures (LST) ranged from −25 • C to 45 • C. (3) Albedo (ALB; MCD43A3.006). ALB is surrogate for surface properties such as the extent and nature of the vegetation cover, and it is affected by the change of the land-surface bio-physical factors such as vegetation, LST and soil moisture [35]. ALB values ranged from 0 to 1 (fresh snow and bare soil usually fall around 0.9).…”

Section: Satellite-derived Products and Efasmentioning

confidence: 99%

Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

et al. 2020

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Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning attributes (EFAs) to predict the distribution of 73 bird species with different life-history traits. We run ensemble species distribution models (SDMs) trained with bird atlas data and 12 EFAs describing different dimensions of carbon cycle and surface energy balance. Our ensemble SDMs—exclusively based on EFAs—hold a high predictive capacity across 71 target species (up to 0.94 and 0.79 of Area Under the ROC curve and true skill statistic (TSS)). Our results showed the life-history traits did not significantly affect SDM performance. Overall, minimum Enhanced Vegetation Index (EVI) and maximum Albedo values (descriptors of primary productivity and energy balance) were the most important predictors across our bird community. Our approach leverages the existing atlas data and provides an alternative method to monitor inter-annual bird habitat dynamics from space in the absence of long-term biodiversity monitoring schemes. This study illustrates the great potential that satellite remote sensing can contribute to the Aichi Biodiversity Targets and to the Essential Biodiversity Variables framework (EBV class “Species distribution”).

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Section: Satellite-derived Products and Efasmentioning

confidence: 99%

Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

et al. 2020

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“…2). SA exhibited a greater sensitivity to changes involving characteristics other than the greenness of leaves because this index covers other bands (SWIR 1 and SWIR 2) of the electromagnetic spectrum (Lui et al, 2017;Zhao et al, 2018), which are not used by indices that use VIS-NIR. When a soil-plant-atmosphere system is altered by an action of deforestation, the leafless woody biomass, which represents ca.…”

Section: Discussionmentioning

confidence: 99%

“…The detection of LCC in seasonally dry forests by using VIS-NIR, such as EVI and NDVI, is limited due to difficulties distinguishing deciduous vegetation from the underlying ground during the dry period (Daughtry, 2001;Jacques et al, 2014;Mayes et al, 2015;Nagler et al, 2000;Xu et al, 2014). Zhao et al (2018) highlight that while vegetation indices are routinely used to monitor ecosystem attributes and functions such as vegetation cover and primary productivity, the remote sensing-measured surface albedo (SA) can be used to assess ecosystem status in drylands. SA is more sensitive to changes in biomass (Rodríguez-Caballero et al, 2015); it has been used to monitor changes in dryland ecosystems, and it is positively correlated with exposed soils (Yu et al, 2017), which are the outcome of the LCC process (Lamchin et al, 2016;Liu et al, 2016;;Karnieli et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Surface albedo as a proxy for land-cover clearing in seasonally dry forests: evidence from the Brazilian Caatinga

Cunha¹,

Nóbrega²,

Rufino³

et al. 2018

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Ongoing increase in human and climate pressures, in addition to the lack of monitoring initiatives, makes the Caatinga one of the most vulnerable forests in the world. The Caatinga is located in the semi-arid region of Brazil and its vegetation phenology is highly dependent on precipitation, which has a high spatial and temporal variability. Under these circumstances, satellite image-based methods are valued due to their ability to uncover human-induced changes from climate effects on land cover. In this study, a time series stack of 670 Landsat images over a period of 31 years (1985–2015) was used to investigate spatial and temporal patterns of land-cover clearing (LCC) due to vegetation removal in an area of the Caatinga. We compared the LCC detection accuracy of three spectral indices, i.e., the surface albedo (SA), the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We applied a residual trend analysis (TSS-RESTREND) to attenuate seasonal climate effects on the vegetation time series signal and to detect only significant structural changes (breakpoints) from monthly Landsat time series. Our results show that SA was able to identify the general occurrence of LCC and the year that it occurred with a higher accuracy (89 and 62%, respectively) compared to EVI (44 and 22%) and NDVI (46 and 22%). The overall outcome of the study shows the benefits of using Landsat time series and a spectral index that incorporates the short-wave infrared range, such as the SA, compared to visible and near-infrared vegetation indices for monitoring LCC in seasonally dry forests such as the Caatinga.

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“…However, the white‐sky albedo (WSA) is related purely to the properties of the land surface and is not affected by atmospheric conditions (Strahler et al, ). Zhao et al () have reported that WSA correlates with ecosystem multifunctionality, which has been found to exhibit two states and abrupt and discontinuous changes along aridity gradients in global drylands (Berdugo, Kéfi, et al, ). WSA (bihemispherical reflectance) is defined as albedo under the condition that the direct component is absent and the diffuse component is isotropic and thus is independent of solar zenith angle.…”

Section: Methodsmentioning

confidence: 99%

Remotely sensed albedo allows the identification of two ecosystem states along aridity gradients in Africa

et al. 2019

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Empirical verification of multiple states in drylands is scarce, impeding the design of indicators to anticipate the onset of desertification. Remote sensing‐derived indicators of ecosystem states are gaining new ground due to the possibilities they bring to be applied inexpensively over large areas. Remotely sensed albedo has been often used to monitor drylands due to its close relationship with ecosystem status and climate. Here, we used a space‐for‐time‐substitution approach to evaluate whether albedo (averaged from 2000 to 2016) can identify multiple ecosystem states in African drylands spanning from the Saharan desert to tropical Africa. By using latent class analysis, we found that albedo showed two states (low and high; the cut‐off level was 0.22 at the shortwave band). Potential analysis revealed that albedo exhibited an abrupt and discontinuous increase with increased aridity (1 − [precipitation/potential evapotranspiration]). The two albedo states co‐occurred along aridity values ranging from 0.72 to 0.78, during which vegetation cover exhibited a rapid, continuous decrease from ~90% to ~50%. At aridity values of 0.75, the low albedo state started to exhibit less attraction than the high albedo state. Low albedo areas beyond this aridity value were considered as vulnerable regions where abrupt shifts in albedo may occur if aridity increases, as forecasted by current climate change models. Our findings indicate that remotely sensed albedo can identify two ecosystem states in African drylands. They support the suitability of albedo indices to inform us about discontinuous responses to aridity experienced by drylands, which can be linked to the onset of land degradation.

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Albedo estimated from remote sensing correlates with ecosystem multifunctionality in global drylands

Cited by 30 publications

References 75 publications

Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

Surface albedo as a proxy for land-cover clearing in seasonally dry forests: evidence from the Brazilian Caatinga

Remotely sensed albedo allows the identification of two ecosystem states along aridity gradients in Africa

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