scholarly journals New Biomass Estimates for Chaparral-Dominated Southern California Landscapes

2021 ◽  
Author(s):  
Charlie Schrader-Patton ◽  
Emma C. Underwood
Keyword(s):  
Southern California ◽  
Vegetation Type ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Management Actions ◽  
Biomass Estimate ◽  
Resource Managers ◽  
Dynamic Landscape ◽  
The Mean ◽  
Field Plots

Chaparral shrublands are the dominant wildland vegetation type in southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrub-lands to sequester carbon. Despite this importance, most national and regional scale estimates do not account for shrubland biomass. Employing plot data from several sources, we built a random forest model to predict above ground live biomass in southern California using remote sensing data (Landsat NDVI) and a suite of geophysical variables. By substituting the NDVI and precipi-tation predictors for any given year we were able to apply the model to each year from 2000-2019. Using a total of 980 field plots, our model had a k-fold cross validation R2 of 0.51 and a RMSE of 3.9. Validation by vegetation type ranged from R2 = 0.17 (RMSE=9.7) for Sierran mixed conifer to R2 = 0.91 (RMSE = 2.3) for sagebrush. Our estimates showed an improvement in accuracy over a two other biomass estimates that included shrublands, with an R2 = 0.82 (RMSE = 4.7) compared to R2 = 0.068 (RMSE = 6.7) for a global biomass estimate and R2 = 0.29 (RMSE = 5.9) for a regional biomass estimate. Given the importance of accurate biomass estimates for resource managers we calculated the mean year 2010 shrubland biomass for the four national forests which ranged from 3.5 kg/m2 (Los Padres) to 2.3 kg/m2 (Angeles and Cleveland). Finally, we compared our estimates to field-measured biomass from the literature summarized by shrubland vegetation type and age class. Our model provides a transparent and repeatable method to generate biomass measure-ments in any year, thereby providing data to track biomass recovery after management actions or disturbances such as fire.

scholarly journals New Biomass Estimates for Chaparral-Dominated Southern California Landscapes

2021 ◽  
Vol 13 (8) ◽  
pp. 1581
Author(s):  
Charlie C. Schrader-Patton ◽  
Emma C. Underwood
Keyword(s):  
Southern California ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Type ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Management Actions ◽  
Biomass Estimate ◽  
Resource Managers ◽  
Dynamic Landscape

Chaparral shrublands are the dominant wildland vegetation type in Southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrublands to sequester carbon. Despite this importance, most national and regional scale estimates do not account for shrubland biomass. Employing plot data from several sources, we built a random forest model to predict aboveground live biomass in Southern California using remote sensing data (Landsat Normalized Difference Vegetation Index (NDVI)) and a suite of geophysical variables. By substituting the NDVI and precipitation predictors for any given year, we were able to apply the model to each year from 2000 to 2019. Using a total of 980 field plots, our model had a k-fold cross-validation R2 of 0.51 and an RMSE of 3.9. Validation by vegetation type ranged from R2 = 0.17 (RMSE = 9.7) for Sierran mixed-conifer to R2 = 0.91 (RMSE = 2.3) for sagebrush. Our estimates showed an improvement in accuracy over two other biomass estimates that included shrublands, with an R2 = 0.82 (RMSE = 4.7) compared to R2 = 0.068 (RMSE = 6.7) for a global biomass estimate and R2 = 0.29 (RMSE = 5.9) for a regional biomass estimate. Given the importance of accurate biomass estimates for resource managers, we calculated the mean year 2010 shrubland biomasses for the four national forests that ranged from 3.5 kg/m2 (Los Padres) to 2.3 kg/m2 (Angeles and Cleveland). Finally, we compared our estimates to field-measured biomasses from the literature summarized by shrubland vegetation type and age class. Our model provides a transparent and repeatable method to generate biomass measurements in any year, thereby providing data to track biomass recovery after management actions or disturbances such as fire.

Predicting spatial patterns of fire on a southern California landscape

2008 ◽  
Vol 17 (5) ◽  
pp. 602 ◽  
Author(s):  
Alexandra D. Syphard ◽  
Volker C. Radeloff ◽  
Nicholas S. Keuler ◽  
Robert S. Taylor ◽  
Todd J. Hawbaker ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Southern California ◽  
Vegetation Type ◽  
Fire Hazard ◽  
Fire Regimes ◽  
Fire Frequency ◽  
Fuel Loading ◽  
Landscape Characteristics ◽  
Management Actions ◽  
Fire Ignitions

Humans influence the frequency and spatial pattern of fire and contribute to altered fire regimes, but fuel loading is often the only factor considered when planning management activities to reduce fire hazard. Understanding both the human and biophysical landscape characteristics that explain how fire patterns vary should help to identify where fire is most likely to threaten values at risk. We used human and biophysical explanatory variables to model and map the spatial patterns of both fire ignitions and fire frequency in the Santa Monica Mountains, a human-dominated southern California landscape. Most fires in the study area are caused by humans, and our results showed that fire ignition patterns were strongly influenced by human variables. In particular, ignitions were most likely to occur close to roads, trails, and housing development but were also related to vegetation type. In contrast, biophysical variables related to climate and terrain (January temperature, transformed aspect, elevation, and slope) explained most of the variation in fire frequency. Although most ignitions occur close to human infrastructure, fires were more likely to spread when located farther from urban development. How far fires spread was ultimately related to biophysical variables, and the largest fires in southern California occurred as a function of wind speed, topography, and vegetation type. Overlaying predictive maps of fire ignitions and fire frequency may be useful for identifying high-risk areas that can be targeted for fire management actions.

scholarly journals New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

2021 ◽  
Vol 13 (15) ◽  
pp. 2892
Author(s):  
Zhongbing Chang ◽  
Sanaa Hobeichi ◽  
Ying-Ping Wang ◽  
Xuli Tang ◽  
Gab Abramowitz ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Aboveground Biomass ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Random Forest Regression ◽  
Multiple Datasets ◽  
Optimal Weighting ◽  
Two Hybrid ◽  
Forest Aboveground Biomass

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales.

The effects of development, vegetation-type conversion, and fire on low-elevation Southern California spider assemblages

2017 ◽  
Author(s):  
Dakota M. Spear ◽  
Tessa A. Adams ◽  
Elise S. Boyd ◽  
Madison M. Dipman ◽  
Weston J. Staubus ◽  
...  
Keyword(s):  
Southern California ◽  
Vegetation Type ◽  
Type Conversion

scholarly journals Traffic-related air pollution and alveolar nitric oxide in southern California children

2016 ◽  
Vol 47 (5) ◽  
pp. 1348-1356 ◽  
Author(s):  
Sandrah P. Eckel ◽  
Zilu Zhang ◽  
Rima Habre ◽  
Edward B. Rappaport ◽  
William S. Linn ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Air Pollution ◽  
Southern California ◽  
Airway Wall ◽  
Acute Effects ◽  
Estimated Parameters ◽  
Free Air ◽  
Distal Airway ◽  
Airway Response ◽  
The Mean

Mechanisms for the adverse respiratory effects of traffic-related air pollution (TRAP) have yet to be established. We evaluated the acute effects of TRAP exposure on proximal and distal airway inflammation by relating indoor nitric oxide (NO), a marker of TRAP exposure in the indoor microenvironment, to airway and alveolar sources of exhaled nitric oxide (FeNO).FeNO was collected online at four flow rates in 1635 schoolchildren (aged 12–15 years) in southern California (USA) breathing NO-free air. Indoor NO was sampled hourly and linearly interpolated to the time of the FeNO test. Estimated parameters quantifying airway wall diffusivity (DawNO) and flux (J′awNO) and alveolar concentration (CANO) sources of FeNO were related to exposure using linear regression to adjust for potential confounders.We found that TRAP exposure indoors was associated with elevated alveolar NO. A 10 ppb higher indoor NO concentration at the time of the FeNO test was associated with 0.10 ppb higher average CANO (95% CI 0.04–0.16) (equivalent to a 7.1% increase from the mean), 4.0% higher J′awNO (95% CI −2.8–11.3) and 0.2% lower DawNO (95% CI −4.8–4.6).These findings are consistent with an airway response to TRAP exposure that was most marked in the distal airways.

scholarly journals Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia

2020 ◽  
Vol 12 (1) ◽  
pp. 1666-1678
Author(s):  
Mohammed H. Aljahdali ◽  
Mohamed Elhag
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Wind Speed ◽  
Red Sea ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Optical Data ◽  
Mean Values ◽  
Sensing Data ◽  
The Mean

AbstractRabigh is a thriving coastal city located at the eastern bank of the Red Sea, Saudi Arabia. The city has suffered from shoreline destruction because of the invasive tidal action powered principally by the wind speed and direction over shallow waters. This study was carried out to calibrate the water column depth in the vicinity of Rabigh. Optical and microwave remote sensing data from the European Space Agency were collected over 2 years (2017–2018) along with the analog daily monitoring of tidal data collected from the marine station of Rabigh. Depth invariant index (DII) was implemented utilizing the optical data, while the Wind Field Estimation algorithm was implemented utilizing the microwave data. The findings of the current research emphasis on the oscillation behavior of the depth invariant mean values and the mean astronomical tides resulted in R2 of 0.75 and 0.79, respectively. Robust linear regression was established between the astronomical tide and the mean values of the normalized DII (R2 = 0.81). The findings also indicated that January had the strongest wind speed solidly correlated with the depth invariant values (R2 = 0.92). Therefore, decision-makers can depend on remote sensing data as an efficient tool to monitor natural phenomena and also to regulate human activities in fragile ecosystems.

Combining static and dynamic environmental factors at various scales to predict shallow landsliding in South Tyrol, Italy – The Proslide project

2021 ◽  
Author(s):  
Robin Kohrs ◽  
 Lotte de Vugt ◽  
Thomas Zieher ◽  
Alice Crespi ◽  
Mattia Rossi ◽  
...  
Keyword(s):  
Regional Scale ◽  
Remote Sensing Data ◽  
Predisposing Factors ◽  
Physical Models ◽  
Data Driven ◽  
Catchment Scale ◽  
Heterogeneous Model ◽  
South Tyrol ◽  
Daily Precipitation Data ◽  
Spatio Temporal

<p>Shallow landslides in alpine environments can constitute a serious threat to the exposed elements. The spatio-temporal occurrence of such slope movements is controlled by a combination of predisposing factors (e.g. topography), preparatory factors (e.g. wet periods, snow melting) and landslide triggers (e.g. heavy precipitation events).  </p><p>For large study areas, landslide assessments frequently focus either on the static predisposing factors to estimate landslide susceptibility using data-driven procedures, or exclusively on the triggering events to derive empirical rainfall thresholds. For smaller areas, dynamic physical models can reasonably be parameterized to simultaneously account for static and dynamic landslide controls.  </p><p>The recently accepted Proslide project aims to develop and test methods with the potential to improve the predictability of landslides for the Italian province of South Tyrol. It is envisaged to account for a variety of innovative input data at multiple spatio-temporal scales. In this context, we seek to exploit remote sensing data for the spatio-temporal description of landslide controlling factors (e.g. precipitation RADAR; satellite soil moisture) and to develop models that allow an integration of heterogeneous model inputs using both, data-driven approaches (regional scale) and physically-based models (catchment scale). This contribution presents the core ideas and methodical framework behind the Proslide project and its very first results (e.g. relationships between landslide observations and gridded daily precipitation data at regional scale). </p>

Soil organic carbon stocks under different páramo vegetation covers in Ecuador’s northern Andes

2021 ◽  
Author(s):  
Marlon Calispa ◽  
Raphaël van Ypersele ◽  
Benoît Pereira ◽  
Sebastián Páez-Bimos ◽  
Veerle Vanacker ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Vegetation Type ◽  
Regional Scale ◽  
Soil Samples ◽  
Related Factors ◽  
Organic C ◽  
Soil C ◽  
C Storage ◽  
Soc Stocks ◽  
Volcanic Ash Soils

<p>The Ecuadorian páramo, a neotropical ecosystem located in the upper Andes, acts as a constant source of high-quality water. It also stores significant amounts of C at the regional scale. In this region, volcanic ash soils sustain most of the paramo, and C storage results partly from their propensity to accumulate organic matter. Vegetation type is known to influence the balance between plant C inputs and soil C losses, ultimately affecting the soil organic C (SOC) content and stock. Tussock-forming grass (spp. Calamagrostis Intermedia; TU), cushion-like plants (spp. Azorella pedunculata; CU) and shrubs and trees (Polylepis stands) are commonly found in the páramo. Our understanding of SOC stocks and dynamics in the páramo remains limited, despite mounting concerns that human activities are increasingly affecting vegetation and potentially, the capacity of these ecosystems to store C.</p><p>Here, we compare the organic C content and stock in soils under tussock-forming grass (spp. Calamagrostis Intermedia; TU) and soils under cushion-like plants (spp. Azorella pedunculata; CU). The study took place at Jatunhuayco, a watershed on the western slopes of Antisana volcano in the northern Ecuadorian Andes. Two areas of similar size (~0.35 km<sup>2</sup>) were surveyed. Fourty soil samples were collected randomly in each area to depths varying from 10 to 30 cm (A horizon) and from 30 to 75 cm (2Ab horizon). The soils are Vitric Andosols and the 2Ab horizon corresponds to a soil buried by the tephra fall from the Quilotoa eruption about 800 yr. BP. Sixteen intact soil samples were collected in Kopecky's cylinders for bulk density (BD) determination of each horizon.</p><p>The average SOC content in the A horizon of the CU sites (9.4±0.5%) is significantly higher (Mann-Whitney U test, p<0.05) than that of the TU sites (8.0±0.4%), probably reflecting a larger input of root biomass from the cushion-forming plants. The 2Ab horizon contains less organic C (i.e. TU: 4.3±0.3% and CU: 4.0±0.4%) than the A horizon, but the SOC contents are undistinguishable between the two vegetation types. This suggests that the influence of vegetation type on SOC is limited to the A horizon. The average SOC stocks (in the first 30 cm from the soil) for TU and CU are 20.04±1.1 and 18.23±1.0 kg/m<sup>2</sup>,<sup></sup>respectively. These values are almost two times greater than the global average reported for Vitric Andosols (~8.2 kg/m<sup>2</sup> ), but are lower than the estimates obtained for some wetter Andean páramos (22.5±5 kg/m<sup>2</sup>, 270% higher rainfall) from Ecuador. Our stock values further indicate that vegetation type has a limited effect on C storage in the young volcanic ash soils found at Jatunhuyaco. Despite a higher SOC content, the CU soils store a stock of organic C similar to that estimated for the TU soils. This likely reflects the comparatively lower BD of the former soils (650±100 vs. 840±30 kg/m<sup>3</sup>). Additional studies are needed in order to establish the vegetation-related factors driving the SOC content and stability in the TU and CU soils.</p>

scholarly journals Use of Global Reanalysis Data in the Study of the Aridity Index in the Magdalena-Cauca Macro-Basin, Colombia

10.29007/92l9 ◽  
2018 ◽  
Author(s):  
Carolina Vega-Viviescas ◽  
David A. Zamora ◽  
Erasmo A. Rodríguez
Keyword(s):  
Southern Oscillation ◽  
Pearson Correlation ◽  
Regional Scale ◽  
Correlation Coefficients ◽  
Aridity Index ◽  
Remote Sensing Data ◽  
Reanalysis Data ◽  
In Situ Data ◽  
Global Reanalysis

The Magdalena-Cauca macro-basin (MCMB) in Colombia, by its tropical location, annually experiences the effects of movement of the Intertropical Convergence Zone, and it is highly affected by interannual macro-climatic phenomena, such as El Niño– Southern Oscillation (ENSO). With the aim of increasing the use of global reanalysis and remote sensing data for supporting water management decisions at the watershed scale and within the framework of the eartH2Observe research project, the aridity index (AI) was calculated with three different data sources. Precipitation products and AI results were compared with their corresponding in-situ national official data. The comparison shows high correlations between the AI derived from observed data and AI obtained from the reanalysis, with Pearson correlation coefficients above 0.8 for two of the products investigated. This shows the importance of using global reanalysis data in water availability studies on a regional scale for the MCMB and the potential of this information in others macrobasins in Colombia including the Orinoquia and Amazon regions, where in-situ data is scarce.

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