Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine

Google Earth Engine (GEE) has been widely used to process geospatial data in recent years. Although the current GEE platform includes functions for fitting linear regression models, it does not have the function to fit nonlinear models, limiting the GEE platform’s capacity and application. To circumvent this limitation, this work proposes a general adaptation of the Levenberg–Marquardt (LM) method for fitting nonlinear models to a parallel processing framework and its integration into GEE. We compared two commonly used nonlinear fitting methods, the LM and nonlinear least square (NLS) methods. We found that the LM method was superior to the NLS method when we compared the convergence speed, initial value stability, and the accuracy of fitted parameters; therefore, we then applied the LM method to develop a nonlinear fitting function for the GEE platform. We further tested this function by fitting a double-logistic equation with the global leaf area index (LAI), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data to the GEE platform. We concluded that the nonlinear fitting function we developed for the GEE platform was fast, stable, and accurate in fitting double-logistic models with remote sensing data. Given the generality of the LM algorithm, we believe that the nonlinear function can also be used to fit other types of nonlinear equations with other sorts of datasets on the GEE platform

Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine

Google Earth Engine (GEE) has been widely used to process geospatial data in recent years. Although the current GEE platform includes functions for fitting linear regression models, it does not have the function to fit nonlinear models, limiting the GEE platform’s capacity and application. To circumvent this limitation, this work proposes a general adaptation of the Levenberg–Marquardt (LM) method for fitting nonlinear models to a parallel processing framework and its integration into GEE. We compared two commonly used nonlinear fitting methods, the LM and nonlinear least square (NLS) methods. We found that the LM method was superior to the NLS method when we compared the convergence speed, initial value stability, and the accuracy of fitted parameters; therefore, we then applied the LM method to develop a nonlinear fitting function for the GEE platform. We further tested this function by fitting a double-logistic equation with the global leaf area index (LAI), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data to the GEE platform. We concluded that the nonlinear fitting function we developed for the GEE platform was fast, stable, and accurate in fitting double-logistic models with remote sensing data. Given the generality of the LM algorithm, we believe that the nonlinear function can also be used to fit other types of nonlinear equations with other sorts of datasets on the GEE platform

Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China

Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation to reduce greenhouse gas emissions. Consequently, many countries have been attempting to generate electricity through PV power plants over the last decade. Monitoring PV power plants through satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid and precise locating with current status on a regional basis are crucial for environmental impact assessment and policy formulation. The effect of fusion of the spectral, textural with different neighbor sizes, and topographic features that may improve machine learning accuracy has not been evaluated yet in PV power plants’ mapping. This study mapped PV power plants using a random forest (RF) model on the Google Earth Engine (GEE) platform. We combined textural features calculated from the Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) from Landsat-8 imagery and elevation, slope, and aspect from Shuttle Radar Topography Mission (SRTM) as input variables. We found that the textural features from GLCM prominent enhance the accuracy of the random forest model in identifying PV power plants where a neighbor size of 30 pixels showed the best model performance. The addition of texture features can improve model accuracy from a Kappa statistic of 0.904 ± 0.05 to 0.938 ± 0.04 and overall accuracy of 97.45 ± 0.14% to 98.32 ± 0.11%. The topographic and thermal features contribute a slight improvement in modeling. This study extends the knowledge of the effect of various variables in identifying PV power plants from remote sensing data. The texture characteristics of PV power plants at different spatial resolutions deserve attention. The findings of our study have great significance for collecting the geographic information of PV power plants and evaluating their environmental impact

Genetic diversity and population structure of indigenous chicken breeds in South China

A total of 587 individuals from 12 indigenous chicken breeds from South China and two commercial breeds were genotyped for 26 microsatellites to investigate the genetic diversity and population structure. All microsatellites were found to be polymorphic. The number of alleles per locus ranged from 5 to 36, with an average of 12.10 ± 7.00 (SE). All breeds, except White Recessive Rock, had high allelic polymorphism (>0.5). Higher genetic diversity was revealed in the indigenous chicken breeds rather than in the commercial breeds. Potential introgression from the commercial breeds into the indigenous chickens was also detected. The population structure of these indigenous chicken breeds could be explained by their geographical distribution, which suggested the presence of independent history of breed formation. Data generated in this study will provide valuable information to the conservation for indigenous chicken breeds in future

Mitochondrial DNA diversity and demographic history of Black-boned chickens in China

Black-boned chickens (Gallus domesticus, herein abbreviated BBCs) are well known for their unique appearance and medicinal properties and have a long breeding history in China. However, the genetic diversity and demographic history of BBCs remain unclear. In this study, we analyzed 844 mitochondrial DNA D-loop sequences, including 346 de novo sequences and 498 previously published sequences from 20 BBC breeds. We detected a generally high level of genetic diversity among the BBCs, with average haplotype and nucleotide diversities of 0.917 ± 0.0049 and 0.01422, respectively. Nucleotide diversity was highest in populations from Southwest China (0.01549 ± 0.00026), particularly in Yunnan Province (0.01624 ± 0.00025). Significant genetic divergence was detected between most breeds, particularly between Yunnan chickens and those from all other provinces. Haplogroups F and G had the highest levels of genetic diversity and were restricted to Southwest China, particularly Yunnan Province. Based on neutrality tests and mismatch distribution analyses, we did not obtain evidence for rapid population expansions and observed similar demographic histories in BBCs and local non-BBCs. Our results suggest that Chinese BBCs have complex breeding histories and may be selected in situ from local domestic chickens. These results improve our understanding of the genetic heritage and breeding histories of these desirable chickens

Genomic variations and signatures of selection in Wuhua yellow chicken.

Wuhua yellow chicken (WHYC) is an important traditional yellow-feathered chicken from China, which is characterized by its white tail feathers, white flight feathers, and strong disease resistance. However, the genomic basis of these unique traits associated with WHYC is poorly understood. In this study, whole-genome resequencing was performed with an average coverage of 20.77-fold to investigate heritable variation and identify selection signals in WHYC. Reads were mapped onto the chicken reference genome (Galgal5) with a coverage of 85.95%. After quality control, 11,953,471 single nucleotide polymorphisms and 1,069,574 insertion/deletions were obtained. In addition, 41,408 structural variants and 33,278 copy number variants were found. Comparative genomic analysis of WHYC and other yellow-feathered chicken breeds showed that selected regions were enriched in genes involved in transport and catabolism, immune system, infectious diseases, signal transduction, and signaling molecules and interactions. Several genes associated with disease resistance were also identified, including IFNA, IFNB, CD86, IL18, IL11RA, VEGFC, and ATG10. Furthermore, our results suggest that PMEL and TYRP1 may contribute to the white feather coloring in WHYC. These findings can improve our understanding of the genetic characteristics of WHYC and may contribute to future breed improvement