A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

Simulation of urban expansion using geographical similarity transition rules and neighbourhood sizes

Most existing cellular automata(CA)models impose strict requirements on the number and spatial distribution of samples.This makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling needs of large and complex geographic areas.This paper presents a CA model based on geographically optimal similarity(GOS)transition rules and similarly sized neighborhoods(SSN).By comparing the similarity in geographical configuration between samples and predicted points,the model enables a comprehensive characterization of the driving mechanism behind urban expansion and its self-organizing scope.This helps to mitigate the impact of sample selection and assumptions about spatial stationarity on simulation results.The performance of GOS-SSN-CA simulation was tested by taking the urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration in China as an example.The results show that GOS can derive more accurate and reliable urban transition rules with fewer samples,thereby significantly reducing spatial prediction errors compared with logistic regression.Moreover,SSN selects different neighborhood sizes to represent the difference between the local self-organizing range and surrounding cells,thus further improving the simulation accuracy and restricting urban expansion morphology.Overall,GOS-SSN-CA effectively characterizes the geographical similarity of urban expansion,improves simulation accuracy while constraining the urban expansion form,and enhances the practical application value of CA.

Genetic analysis of selected almond genotypes and cultivars grown in Turkey using peroxidase-gene-based markers

Peroxidase plays important roles in many stress-related interactions and catalyzes important reactions in various physiological processes. Since peroxidase played critical roles in the evolution of almond（Prunus dulcis Miller（D.A Webb） syn P. amygdalus Batsch）, peroxidase-gene-based analyses may increase the understanding of evolution of this species. Peroxidase gene polymorphism（POGP） markers were used to determine genetic diversity and relationships among 69 Turkish genotypes/cultivars and 27 foreign almond cultivars by using unweighted pair group method with arithmetic mean（UPGMA） analysis. This study is the first evaluation of the use of POGP markers for diversity analysis in almond.Totally, 83 fragments were obtained from eight peroxidase primer pairs, and polymorphism was identified as 94 %.Similarity level among the genotypes ranged between 0.63 and 0.93, and all materials were distinguished. In general,Turkish and foreign genotypes were mixed in clusters since they share a common genetic background and gene migration among the sites. Clusters were not based on geographic regions except for some minor groupings. This study indicated that peroxidase gene markers can be reliably used to determine genetic relationships in almonds.