RESUMEN
Forest biomass is an important index in forest development planning and forest resource monitoring. In order to provide a more efficient and low-biased method for estimating individual tree biomass, we introduced artificial neural network here. We used the data of aboveground biomass of 101 Larix olgensis trees harvested from the Dongzhelenghe Forest Farm in Heilongjiang Province to develop four aggregation model systems (AMS), based on different combination of the variables (diameter at breast height, tree height, crown width). The weighted functions were used to eliminate heteroscedasticity. Then, we trained artificial neural network (ANN) biomass model based on the optimal combination. The models were tested by the leave-one-out cross-validation method to compare the accuracy of the two biomass estimation methods. The results showed that biomass model based on only one variable, diameter at breast height, could accurately estimate the biomass of L. olgensis. Adding two indices, tree height and crown width, could improve the fitting performance of models, with AMS4 performing the best among the four addictive model systems. The biomass models developed by the two methods both could estimate biomass at tree level accurately, with the coefficient of determination (R2) of each component was higher than 0.87. Compared with the AMS4, R2 of leaf biomass model was about 0.05 higher, and that of other organs were also about 0.01 higher in artificial neural network model system. In addition, the root mean square error (RMSE) and other indicators were also significantly smaller. For example, the RMSE of tree stem and aboveground biomass were smaller by 2.135 kg and 3.908 kg, respectively. The model's validation statistics mean relative error (MRE) performed better. In general, ANN was a flexible and reliable biomass estimation method, which was worthy consideration when predicting tree component biomass or aboveground biomass.
Asunto(s)
Larix , Árboles , Biomasa , Bosques , Redes Neurales de la ComputaciónRESUMEN
Following the distribution characteristics of Larix gmelinii in Daxing'anling Mountains, nine sampling sites along a latitude gradient were set up to analyze the spatial difference and temporal dynamic in the responses of radial growth of L. gmelinii to climate. Overall, the radial growth of L. gmelinii was positively correlated with the standardized precipitation evapotranspiration index (SPEI) in summer (June to August), summer precipitation, February SPEI, and February preci-pitation, but was negatively correlated with the March temperature. Spatially, in the southern area of the region with higher annual average temperature, the radial growth of L. gmelinii had a significant positive correlation with February SPEI. In the northern area with lower annual average tempera-ture, the radial growth of L. gmelinii was negatively correlated with the temperature in March. Temporally, the growth-climate relationship for L. gmelinii was unstable. In the area with higher annual average temperature, the positive effects of SPEI and precipitation, as well as the negative effects of temperature in summer on growth significantly enhanced with climate warming. In the area with lower annual average temperature, the negative response of growth to March temperature enhanced more obviously. Such a result indicated that climate change would alter growth-climate relationship, with great spatial variations. Our results suggested that radial growth of L. gmelinii would be limited in the future climate of warm and dry in the Daxing'anling Mountains. The growth of L. gmelinii might obviously decline in south due to summer water deficit and winter drought, and might be inhibited in north because of warm and dry winter.
Asunto(s)
Larix , China , Cambio Climático , Temperatura , ÁrbolesRESUMEN
OBJECTIVE: To analyze the discipline of intraocular pressure (IOP) variation, through circadian intraocular pressure monitoring in primary open-angle glaucoma (POAG) patients and normal controls, with a view to provide basis for individualized treatment of glaucoma. METHODS: Subjects were enrolled from the outpatients of Shanghai Beizhan Hospital and Eye and ENT Hospital of Fudan University, which were diagnosed as primary open angle glaucoma, from April 2006 to April 2009. Totally there were 102 cases of patients and 83 cases of normal volunteers. All the subjects accepted 24-hour IOP measurements using non-contact tonometer every two hours starting from 8:00 am. And the IOP between 00:00 to 06:00 am was measured in sitting position immediately after wake up. RESULTS: The differences of peak IOP [(16.0 ± 2.7) mm Hg of right eye and (16.2 ± 2.7) mm Hg of left eye in normal group; (25.3 ± 5.6) mm Hg of right eye and (24.8 ± 5.1) mm Hg of left eye in POAG group], valley IOP (11.1 ± 2.5) mm Hg of right eye and (11.0 ± 2.3) mm Hg of left eye in normal group; (16.3 ± 3.7) mm Hg of right eye and (16.2 ± 3.3) mm Hg of left eye in POAG group, average IOP (13.4 ± 2.5) mm Hg of right eye and (13.4 ± 2.5) mm Hg of left eye in normal group; (19.9 ± 4.3) mm Hg of right eye and (19.8 ± 3.8) mm Hg of left eye in POAG group), and IOP fluctuations (5.0 ± 1.6) mm Hg of right eye and (5.2 ± 1.7) mm Hg of left eye in normal group; (9.1 ± 3.6) mm Hg of right eye and (8.6 ± 3.8) mm Hg of left eye in POAG group between two groups were all of statistically significance (P < 0.01). Notably, the peak IOP of 59.6% in normal control group and 73.5% in POAG group were outside working hours, especially in the time period from 00:00 to 06:00 am. The peak value of 50% in normal group and 64.7% in POAG group located between 00:00 to 06:00 in the morning. CONCLUSIONS: By comparison and analysis, 24-hour intraocular pressure measurement could provide us pre-treatment basic state, so as to provide detailed information for individualized treatment. If possible, it is suggested that 24-hour IOP monitoring should be added as a routine examination of primary open angle glaucoma.
