Remote Sensing of Biological Soil Crusts at Different Scales

Weber, Bettina; Hill, Joachim

doi:10.1007/978-3-319-30214-0_12

Cited by 23 publications

(37 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, contrary to previous studies [2,76], we found that the use of single band reflectance values at this wavelength were not able to accurately estimate Chla of biocrust communities, as shown by the lower coefficient of determination and higher RMSE values obtained compared to those obtained with the , CR, ND, and standard spectral indexes (Table 1). This may be due to the fact that our dataset was composed of different biocrust communities dominated by a variety of phototrophic organisms with different spectral traits [2,50] that, as previously reported by Escribano et al, 2017 [77], interact with the soil reflectance in multiple ways, modifying the final reflectance values independently of the biocrust chlorophyll concentration. For example, whereas dark cyanobacteria-and green moss-dominated biocrusts reduced the overall soil surface reflectance when compared to the bare soil spectra, white lichens that occur in the experimental zones caused the opposite effect, especially in the dark-colored soils from Las Amoladeras (Figure 3).…”

Section: Discussioncontrasting

confidence: 99%

“…The spectral analysis of our experimental dataset corroborated that, according to several studies analyzing different biocrust communities around the world [2,[48][49][50][51][52], biocrusts modify soil surface reflectance with a main absorption detected in the reflectance red peak at about 680 nm, and along the red edge ( Figure 3) that corresponds to the well-known chlorophyll a spectral absorption [72][73][74][75]. However, contrary to previous studies [2,76], we found that the use of single band reflectance values at this wavelength were not able to accurately estimate Chla of biocrust communities, as shown by the lower coefficient of determination and higher RMSE values obtained compared to those obtained with the , CR, ND, and standard spectral indexes (Table 1). This may be due to the fact that our dataset was composed of different biocrust communities dominated by a variety of phototrophic organisms with different spectral traits [2,50] that, as previously reported by Escribano et al, 2017 [77], interact with the soil reflectance in multiple ways, modifying the final reflectance values independently of the biocrust chlorophyll concentration.…”

Section: Discussionsupporting

confidence: 88%

“…Under these hostile conditions, other life forms, such as biological soil crusts (biocrusts) have been described to play a key role in ecosystem primary productivity [1]. Biocrusts are inconspicuous communities, mainly dominated by photosynthesizing cyanobacteria, algae, lichens, or bryophytes that can survive during long drought periods in a dormant state and rapidly become active after small water pulses [2]. These traits allow biocrusts to fix a large amount of atmospheric carbon and nitrogen [3,4], which becomes incorporated within the upper layer of the soil to be used by heterotrophic fungi, bacteria, archaea [5], and other soil surface inhabitants [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Vastly different methodologies and indexes have been developed for these analyses using both multi-and hyper-spectral information [42][43][44][45][46][47]. Contrastingly, though there are numerous studies demonstrating that biocrusts have spectral traits related to the presence of photosynthetic pigments [2,[48][49][50][51][52], remote sensing applications for biocrust chlorophyll a quantification are mostly limited to a few local analyses using the normalized difference vegetation index (NDVI; e.g., Karnieli et al, 2001 [53]) or surface reflectance at 680 nm [2]. This occurs for two main reasons.…”

Section: Introductionmentioning

confidence: 99%

“…This occurs for two main reasons. First, monitoring drylands and biocrusts from remote sensing platforms has been quite challenging because the spectral signal of these sites is often the results of the interaction between different components with different spectral properties and life cycles such as soil, biocrusts, and vascular plants [2,52,53]. Second, when dry, the biocrust spectral signal is very subtle [52], which increases the difficulty in detecting chlorophyll-related absorption peaks, and therefore, the biocrust's chlorophyll quantification.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

et al. 2019

View full text Add to dashboard Cite

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms' status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R 2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 µg/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

show abstract

Section: Discussioncontrasting

confidence: 99%

Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

et al. 2019

View full text Add to dashboard Cite

show abstract

UAV RGB, thermal infrared and multispectral imagery used to investigate the control of terrain on the spatial distribution of dryland biocrust

Blanco‐Sacristán

Panigada

Gentili

et al. 2021

Earth Surf Processes Landf

View full text Add to dashboard Cite

Biocrusts (topsoil communities formed by mosses, lichens, bacteria, fungi, algae, and cyanobacteria) are a key biotic component of dryland ecosystems. Whilst climate patterns control the distribution of biocrusts in drylands worldwide, terrain and soil attributes can influence biocrust distribution at landscape scale. Multi-source unmanned aerial vehicle (UAV) imagery was used to map and study biocrust ecology in a typical dryland ecosystem in central Spain. Red, green and blue (RGB) imagery was processed using structure-from-motion techniques to map terrain attributes related to microclimate and terrain stability. Multispectral imagery was used to produce accurate maps (accuracy > 80%) of dryland ecosystem components (vegetation, bare soil and biocrust composition). Finally, thermal infrared (TIR) and multispectral imagery was used to calculate the apparent thermal inertia (ATI) of soil and to evaluate how ATI was related to soil moisture (r 2 = 0.83). The relationship between soil properties and UAV-derived variables was first evaluated at the field plot level. Then, the maps obtained were used to explore the relationship between biocrusts and terrain attributes at ecosystem level through a redundancy analysis. The most significant variables that explain biocrust distribution are: ATI (34.4% of variance, F = 130.75; p < 0.001), Elevation (25.8%, F = 97.6; p < 0.001), and potential solar incoming radiation (PSIR) (52.9%, F = 200.1; p < 0.001). Differences were found between areas dominated by lichens and mosses. Lichen-dominated biocrusts were associated with areas with high slopes and low values of ATI, with soil characterized by a higher amount of soluble salts, and lower amount of organic carbon, total phosphorus (P tot ) and total nitrogen (N tot ). Biocrust-forming mosses dominated lower and moister areas, characterized by gentler slopes and higher values of ATI with soils with higher contents of organic carbon, P tot and N tot . This study shows the potential to use UAVs to improve our understanding of drylands and to evaluate the control that the terrain has on biocrust distribution.Twitter: Multi-source UAV imagery was used to study biocrust distribution in a typical dryland ecosystem in central Spain and how it is affected by terrain and soil properties.

show abstract

Ultra‐high‐resolution mapping of biocrusts with Unmanned Aerial Systems

Havrilla

Villarreal

DiBiase

et al. 2020

Remote Sens Ecol Conserv

View full text Add to dashboard Cite

Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground-based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite-based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high-resolution imagery from Unmanned Aerial Systems (UAS) to map fine-scale patterns of biocrusts. We collected sub-centimeter, true color 3-band imagery at 10 plots in sagebrush and pinyon-juniper woodland communities in a semiarid ecosystem in the southwestern US and used objectbased image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon-juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon-juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon-juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon-juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type.

show abstract

Remote Sensing of Biological Soil Crusts at Different Scales

Cited by 23 publications

References 39 publications

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

UAV RGB, thermal infrared and multispectral imagery used to investigate the control of terrain on the spatial distribution of dryland biocrust

Ultra‐high‐resolution mapping of biocrusts with Unmanned Aerial Systems

Contact Info

Product

Resources

About