Dataset Information

Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region.

ABSTRACT: Background:Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends. The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance. Methods:We used climate, topographic, and spectral data as the input variables for four machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and maximum likelihood classification (MLC)) on three vegetation classification units (vegetation group (I), vegetation type (II), and formation and subformation (III)) in Jing-Jin-Ji, one of China's most developed regions. We used a total of 2,789 vegetation points for model training and 974 vegetation points for model assessment. Results:Our results showed that the RF method was the best of the four models, as it could effectively simulate vegetation distribution in all three classification units. The DT method could only simulate vegetation distribution in units I and II, while the other two models could not simulate vegetation distribution in any of the units. Kappa coefficients indicated that the DT and RF methods had more accurate predictions for units I and II than for unit III. The three vegetation classification units were most affected by six variables: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Mid-infrared ratio of winter vegetation index and brightness index of summer vegetation index). Variables Combination 7, including annual mean temperature, annual precipitation, mean diurnal range and precipitation of driest month, produced the highest simulation accuracy. Conclusions:We determined that the RF model was the most effective for simulating vegetation distribution in all classification units present in the Jing-Jin-Ji region. The RF model produced high accuracy vegetation distributions in classification units I and II, but relatively low accuracy in classification unit III. Four climate variables were sufficient for vegetation distribution simulation in such region.

SUBMITTER: Yi S

PROVIDER: S-EPMC7474518 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region.

Yi Sangui S Zhou Jihua J Lai Liming L Du Hui H Sun Qinglin Q Yang Liu L Liu Xin X Liu Benben B Zheng Yuanrun Y

PeerJ 20200902

<h4>Background</h4>Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends. The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance.<h4>Methods</h4>We used climate, topographic, and spectral data as the input variables for four machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and maximum likelihood ...[more]

PMID: 32953272

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Project description:BackgroundPolycystic ovary syndrome (PCOS) is the most common heterogeneous endocrine disease among women of childbearing age, characterized by androgen excess and ovarian dysfunction. Aerobic exercise is an important solution used to manage PCOS, due to its multiple benefits. Yi Jin Jing is an important component of traditional Chinese exercise (TCE), based on the root of traditional Chinese medicine theory (TCM), which focuses on keeping the body as a whole in a harmonious state. However, to date there is no literature on the benign effects of Yi Jin Jing on PCOS. The primary purpose of this protocol is to assess the effectiveness of aerobic exercise versus Yi Jin Jing, on the management of ovarian function in young overweight/obese women with PCOS.MethodsThe study will conduct a controlled randomized, superiority trial with three-arm parallel groups, recruiting 90 women diagnosed with PCOS, ages between 18 and 35 years, with a body mass index (BMI) ≥ 23 kg/m2. Women will be randomly assigned to either control group (combined oral contraceptives) or one of the intervention groups (Yi Jin Jing group or aerobic exercise group) with an allocation rate of 1:1:1. After randomization, the intervention will be conducted within a 12-week period. The primary outcome would be anti-Mullerian hormone (AMH) level; the secondary outcomes would be biochemical profiles, ovarian volume, antral follicle count, BMI, menstrual frequency, and homeostasis model assessment of insulin resistance (HOMA-IR). Outcome measures would be collected during baseline and end of treatment. Reporting of the study will follow the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) statement.DiscussionThis proposed study will be the first randomized clinical trial to evaluate the comparative effectiveness of aerobic exercise versus Yi Jin Jing on the management of ovarian function in young overweight/obese women with PCOS. The results may provide a new evidence-based management strategy for young women with PCOS.Trial registrationChinese Clinical Trial Registry ChiCTR1900022385 . Registered on 9 April 2019.

Dataset Information

Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region.

Publications

Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region.

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