Showing papers by "Yufang Jin published in 2020"

Intensified burn severity in California’s northern coastal mountains by drier climatic condition

Abstract: The severity of wildfire burns in interior lands of western US ecosystems has been increasing. However, less is known about its coastal mountain ecosystems, especially under extreme weather conditions, raising concerns about the vulnerability of these populated areas to catastrophic fires. Here we examine the fine-scale association between burn severity and a suite of environmental drivers including explicit fuel information, weather, climate, and topography, for diverse ecosystems in California's northern coastal mountains. Burn severity was quantified using Relative difference Normalized Burn Ratio from Landsat multispectral imagery during 1984-2017. We found a significant increasing trend in burned areas and severity. During low-precipitation years, areas that burned had much lower fuel moisture and higher climatic water deficit than in wetter years, but the percentage of high-severity areas doubled, especially during the most recent 2012-2016 drought. The Random Forest (RF) machine learning model achieved overall accuracy of 79% in classifying categories of burn severity. Aspect, slope, fuel type and availability, and temperature were the most important drivers, based on both classification and regression RF models. We further examined the importance of drivers under four climatic conditions: dry vs. wet years, and during two extended drought periods (the 2012-2016 warmer drought vs. the 1987-1992 drought). During warm and dry years, the spatial variability of burn severity was a mixed effect of slope, long-term minimum temperature, fuel amount, and fuel moisture. In contrast, climatic water deficit and short-term weather became dominant factors for fires during wetter years. These results suggest that relative importance of drivers for burn severity in the broader domain of California's northern coastal mountains varied with weather scenarios, especially when exacerbated by warm and extended drought. Our findings highlight the importance of targeting areas with high burn severity risk for fire adaptation and mitigation strategies in a changing climate and intensifying extremes.

Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California's Almond Orchards.

Abstract: Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California's almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19°C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPDmax), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPDmax beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches.

Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California

Abstract: Satellite-based active fire (AF) products provide opportunities for constructing continuous fire progression maps, a critical dataset needed for improved fire behavior modeling and fire management. This study aims to investigate the geospatial interpolation techniques in mapping the daily fire progression and assess the accuracy of the derived maps from multisensor AF products. We focused on 42 large wildfires greater than 5000 acres in Northern California from 2017 to 2018, where the USDA Forest Service National Infrared Operations (NIROPS) daily fire perimeters were available for the comparison. The standard AF products from the moderate resolution imaging spectroradiometer (MODIS), the visible infrared imaging radiometer suite (VIIRS), and the combined products were used as inputs. We found that the estimated fire progression areas generated by the natural neighbor method with the combined MODIS and VIIRS AF input layers performed the best, with R 2 of 0.7 ± 0.31 and RMSE of 1.25 ± 1.21 (103 acres) at a daily time scale; the accuracy was higher when assessed at a two-day rolling window, e.g., R 2 of 0.83 ± 0.20 and RMSE of 0.74 ± 0.94 (103 acres). A relatively higher spatial accuracy was found using the 375 m VIIRS AF product as inputs, with a kappa score of 0.55 and an overall accuracy score of 0.59, when interpolated with the natural neighbor method. Furthermore, the locational pixel-based comparison showed 61% matched to a single day and an additional 25% explained within ±1 day of the estimation, revealing greater confidence in fire progression estimation at a two-day moving time interval. This study demonstrated the efficacy and potential improvements of daily fire progression mapping at local and regional scales.

ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California

Abstract: The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides remotely-sensed estimates of evapotranspiration at 70 m spatial resolution every 1–5 days, sampling across the diurnal cycle. This study, in partnership with an operational water management organization, the Eastern Municipal Water District (EMWD) in Southern California, was conducted to evaluate estimates of evapotranspiration under ideal conditions where water is not limited. EMWD regularly uses a ground-based network of reference evapotranspiration (ETo) from the California Irrigation Management Information System (CIMIS); yet, there are gaps in spatial coverage and questions of spatial representativeness and consistency. Space-based potential evapotranspiration (PET) estimates, such as those from ECOSTRESS, provide consistent spatial coverage. We compared ECOSTRESS ETo (estimated from PET) to CIMIS ETo at five CIMIS sites in Riverside County, California from July 2018–June 2020. We found strong correlations between CIMIS ETo and ECOSTRESS ETo across all five sites (R2 = 0.89, root mean square error (RMSE) = 0.11 mm hr−1). Both CIMIS and ECOSTRESS ETo captured similar seasonal patterns through the study period as well as diurnal variability. There were site-specific differences in the relationship between ECOSTRESS AND CIMIS, in part due to spatial heterogeneity around the station site. Consequently, careful examination of landscapes surrounding CIMIS sites must be considered in future comparisons. These results indicate that ECOSTRESS successfully retrieves PET that is comparable to ground-based reference ET, highlighting the potential for providing observation-driven guidance for irrigation management across spatial scales.

Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce

Abstract: Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences comprised of multiple florets that open only once. Different accessions were observed to flower at different times of day. An F6 recombinant inbred line population (RIL) had been derived from accessions of L. serriola x L. sativa that originated from different environments and differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping on two occasions, one week apart. Floral pixels were identified from the images using a support vector machine (SVM) machine learning algorithm with an accuracy above 99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent QTLs, qDFO2.1 (Daily Floral Opening 2.1) and qDFO8.1, were discovered. Together, they explained more than 30% of the phenotypic variation in floral opening time. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote imaging, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes such as floral opening time.