The Effects of Forest Area Changes on Extreme Temperature Indexes between the 1900s and 2010s in Heilongjiang Province, China

Zhang, Lijuan; Pan, Tao; Zhang, Hongwen; Li, Xiaxiang; Jiang, Lili

doi:10.3390/rs9121280

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…We extracted CHD based on different sampling height intervals and tested the effect of the extracted CHDs at these height intervals (0.1 m-1.0 m) on the performance of the AGB model. The 0.4 m sampling interval identified was appropriate in this study (see Figure A1), similar to previous studies' findings [53,54].…”

Section: Chd Vs Lidar Metricssupporting

confidence: 90%

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Ma,

Zhang,

et al. 2023

Remote Sensing

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Identifying important factors (e.g., features and prediction models) for forest aboveground biomass (AGB) estimation can provide a vital reference for accurate AGB estimation. This study proposed a novel feature of the canopy height distribution (CHD), a function of canopy height, that is useful for describing canopy structure for AGB estimation of natural secondary forests (NSFs) by fitting a bimodal Gaussian function. Three machine learning models (Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (Xgboost)) and three deep learning models (One-dimensional Convolutional Neural Network (1D-CNN4), 1D Visual Geometry Group Network (1D-VGG16), and 1D Residual Network (1D-Resnet34)) were applied. A completely randomized design was utilized to investigate the effects of four feature sets (original CHD features, original LiDAR features, the proposed CHD features fitted by the bimodal Gaussian function, and the LiDAR features selected by the recursive feature elimination algorithm) and models on estimating the AGB of NSFs. Results revealed that the models were the most important factor for AGB estimation, followed by the features. The fitted CHD features significantly outperformed the other three feature sets in most cases. When employing the fitted CHD features, the 1D-Renset34 model demonstrates optimal performance (R2 = 0.80, RMSE = 9.58 Mg/ha, rRMSE = 0.09), surpassing not only other deep learning models (e.g.,1D-VGG16: R2 = 0.65, RMSE = 18.55 Mg/ha, rRMSE = 0.17) but also the best machine learning model (RF: R2 = 0.50, RMSE = 19.42 Mg/ha, rRMSE = 0.16). This study highlights the significant role of the new CHD features fitting a bimodal Gaussian function and the effects between the models and the CHD features, which provide the sound foundations for effective estimation of AGB in NSFs.

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Section: Chd Vs Lidar Metricssupporting

confidence: 90%

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Ma,

Zhang,

et al. 2023

Remote Sensing

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“…In recent years, climate change has increased the frequency of extreme weather events [20,21] and, correspondingly, the occurrence probability of fires [22,23]. Climate change affects the occurrence and dynamics of fire by changing meteorological factors such as air temperature (abbreviated as temperature), precipitation, humidity, evaporation and wind speed [24,25].…”

Section: Introductionmentioning

confidence: 99%

Satellite-Derived Variation in Burned Area in China from 2001 to 2018 and Its Response to Climatic Factors

Wang

et al. 2021

Remote Sensing

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Fire is one of the most widespread and destructive disasters, which causes property losses, casualties, and disruption of the balance of ecosystems. Therefore, it is highly necessary for firefighting to study the variations in fire and its climatic attributions. This study analyzed the characteristics of fire-burned area (BA) and its response to climatic factors in seven subregions of China from 2001 to 2018 using satellite remote sensing BA products. The results show that the BA in China and most of its subregions shows a decreasing trend. In general, it is negatively correlated with precipitation and positively correlated with air temperature and wind speed based on the regression and correlation analyses. Based on Pearson correlation and random forest methods, it is also found that the temperature is commonly an important factor contributing to BA in China, except for R2 (Inner Mongolia region), where wind speed is more important, and R5 (South China), where precipitation is more important, which coexists at annual and seasonal scales. Besides temperature, precipitation in spring and summer is the main driving factor, such as in R1 (Northeast China), R5, R6 (Northwest China) and R7 (Qinghai–Tibet Plateau) in spring and R4 (Central China), R5 and R7 in summer; and wind speed in autumn and winter is the main driving factor, such as in R2 and R4 in autumn and R2, R3, R5, R6 and R7 in winter. Finally, the distributions of BA with respect to each climatic factor were also analyzed to quantify the range of climatic factors with maximum BA occurrence.

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“…The province includes wet, semi‐humid and semi‐arid rainfall zones. The terrain is complex, and the total area of mountains and hills account for approximately 60.5% of the total area (Zhang et al ., 2017). Heilongjiang Province is surrounded by vast forests and is an important forest and forestry area in China (Zhang et al ., 2015).…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

Zhang

Liu

et al. 2020

Quart J Royal Meteoro Soc

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Sample entropy can be used to investigate the complexity of precipitation series. However, the randomness of similar tolerance selection may lead to inaccurate results. To solve this problem, the distinction degree theory was introduced to optimize the similar tolerance of sample entropy, and a specific reference frame for the optimization process was provided. The optimized sample entropy was used to study the spatial differences in precipitation complexity and the possible causes from the perspective of the monthly precipitation (MP) and extreme daily precipitation (EDP) in Heilongjiang Province, China. The results demonstrated that the MP and EDP in Heilongjiang Province both had unsteady complex fluctuation characteristics and the optimal similar tolerances of the sample entropy were 0.24 and 0.13 times the standard deviation, respectively. The complexity of the EDP was higher than that of the MP, and their fluctuation ranges were 1.641–2.268 and 0.433–0.870, respectively. The complexity of the MP in the study area could be spatially represented as a basic pattern, which gradually decreased from east to west. The spatial distribution pattern of the EDP complexity indicated that the northern part was higher than the southern part. Altitude was the common influencing factor of the MP and EDP complexities, and it had a greater impact on the spatial pattern of the EDP complexity. Potential factors influencing the spatial difference in MP complexity also included changes in precipitation intensity, water area and residential and industrial land areas. Except for altitude, industrial economic development level may also affect the complexity of the EDP. The results may improve the application of sample entropy in climate system complexity measurements and could provide guiding significance for revealing the spatial variation of precipitation complexity.

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The Effects of Forest Area Changes on Extreme Temperature Indexes between the 1900s and 2010s in Heilongjiang Province, China

Cited by 9 publications

References 31 publications

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests

Satellite-Derived Variation in Burned Area in China from 2001 to 2018 and Its Response to Climatic Factors

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

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