Forests play a crucial role in regulating the carbon balance and maintaining global climate stability.Remote sensing has provided new perspectives for regional monitoring of vegetation phenology.However,an accurate me...Forests play a crucial role in regulating the carbon balance and maintaining global climate stability.Remote sensing has provided new perspectives for regional monitoring of vegetation phenology.However,an accurate method for extracting the photosynthetic phenology of forests remains challenging.This study proposes an innovative method,the change point estimation of forest photosynthetic phenology method based on the maximum perpendicular distance(CBPD).CBPD extracted the dates of the start of the season(SOS)and the end of the season(EOS)for forests in North America from solar-induced chlorophyll fluorescence and daily flux tower observations.The validation results of CBPD indicated that compared to those of the double-logistic,first-order derivative,and dynamic threshold methods,the root mean square error of CBPD decreased by 0.04 to 14.04 d,while Pearson’s correlation coefficient and agreement index increased by 0.03 to 0.30 and by 0.34 to 21.52,respectively.Furthermore,CBPD demonstrated substantial consistency(P<0.01)with cross-validation based on remote sensing of photosynthetic phenology.In addition,SOS exhibited greater interannual variability compared to EOS.SOS was dominated by air temperature in 93.89% of the forest area.EOS was dominated by radiation in 48.70% of the forest area.In summary,CBPD has a great potential for tracking forest photosynthetic phenology,offering crucial insights into phenological responses to climate variations.展开更多
We propose a nonparametric change point estimator in the distributions of a sequence of independent observations in terms of the test statistics given by Huˇskov′a and Meintanis(2006) that are based on weighted empi...We propose a nonparametric change point estimator in the distributions of a sequence of independent observations in terms of the test statistics given by Huˇskov′a and Meintanis(2006) that are based on weighted empirical characteristic functions. The weight function ω(t; a) under consideration includes the two weight functions from Huˇskov′a and Meintanis(2006) plus the weight function used by Matteson and James(2014),where a is a tuning parameter. Under the local alternative hypothesis, we establish the consistency, convergence rate, and asymptotic distribution of this change point estimator which is the maxima of a two-side Brownian motion with a drift. Since the performance of the change point estimator depends on a in use, we thus propose an algorithm for choosing an appropriate value of a, denoted by a_s which is also justified. Our simulation study shows that the change point estimate obtained by using a_s has a satisfactory performance. We also apply our method to a real dataset.展开更多
基金supported in part by the Postdoctor Project of Hubei Province under Grant Number 2024HBBHCXA064the Natural Resources Science and Technology Innovation Projects in Fujian Province under Grant Number KY-030000-04-2024-033+4 种基金the Open Fund of the Key Laboratory of JiangHuai Arable Land Resources Protection and Ecorestoration under Grant Number ARPE-2024-KF01the National Natural Science Foundation of China under Grant Number 42090012the Sichuan Science and Technology Program under Grant Numbers 2022YFN0031,2023YFS0381,and 2023YFN0022the Inte rgovernmental International Science and Technology Inno vation Cooperation Project under Grant Number 2023YFE0110400the Key Technology and Application Demonstration for Integ rated Remote Sensing Monitoring of Safety in Key Projects under Grant Number 2023YFB3906100.
文摘Forests play a crucial role in regulating the carbon balance and maintaining global climate stability.Remote sensing has provided new perspectives for regional monitoring of vegetation phenology.However,an accurate method for extracting the photosynthetic phenology of forests remains challenging.This study proposes an innovative method,the change point estimation of forest photosynthetic phenology method based on the maximum perpendicular distance(CBPD).CBPD extracted the dates of the start of the season(SOS)and the end of the season(EOS)for forests in North America from solar-induced chlorophyll fluorescence and daily flux tower observations.The validation results of CBPD indicated that compared to those of the double-logistic,first-order derivative,and dynamic threshold methods,the root mean square error of CBPD decreased by 0.04 to 14.04 d,while Pearson’s correlation coefficient and agreement index increased by 0.03 to 0.30 and by 0.34 to 21.52,respectively.Furthermore,CBPD demonstrated substantial consistency(P<0.01)with cross-validation based on remote sensing of photosynthetic phenology.In addition,SOS exhibited greater interannual variability compared to EOS.SOS was dominated by air temperature in 93.89% of the forest area.EOS was dominated by radiation in 48.70% of the forest area.In summary,CBPD has a great potential for tracking forest photosynthetic phenology,offering crucial insights into phenological responses to climate variations.
基金supported by Natural Sciences and the Engineering Research Council of Canada (Grant No. 105557-2012)National Natural Science Foundation for Young Scientists of China (Grant No. 11201108)+1 种基金the National Statistical Research Plan Project (Grant No. 2012LZ009)the Humanities and Social Sciences Project from Ministry of Education of China (Grant No. 12YJC910007)
文摘We propose a nonparametric change point estimator in the distributions of a sequence of independent observations in terms of the test statistics given by Huˇskov′a and Meintanis(2006) that are based on weighted empirical characteristic functions. The weight function ω(t; a) under consideration includes the two weight functions from Huˇskov′a and Meintanis(2006) plus the weight function used by Matteson and James(2014),where a is a tuning parameter. Under the local alternative hypothesis, we establish the consistency, convergence rate, and asymptotic distribution of this change point estimator which is the maxima of a two-side Brownian motion with a drift. Since the performance of the change point estimator depends on a in use, we thus propose an algorithm for choosing an appropriate value of a, denoted by a_s which is also justified. Our simulation study shows that the change point estimate obtained by using a_s has a satisfactory performance. We also apply our method to a real dataset.
