Spatiotemporal variations in remote sensing phenology of vegetation and its responses to temperature change of boreal forest in tundra-taiga transitional zone in the Eastern Siberia

Li, Cheng; Zhuang, Dafang; He, Jianfeng; Wen, Kege

doi:10.1007/s11442-023-2092-z

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…The ecological mechanism of this interaction and its specific impact on regional climate feedback have not been fully explored. The feedback mechanism between temperature and vegetation phenological factors needs to be further studied in detail [35]. Table 4 shows the comparison of advantages and disadvantages between different methods.…”

Section: Discussionmentioning

confidence: 99%

Detection of the Pine Wilt Disease Using a Joint Deep Object Detection Model Based on Drone Remote Sensing Data

Wu,

Yang,

Mao

2024

Forests

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Disease and detection is crucial for the protection of forest growth, reproduction, and biodiversity. Traditional detection methods face challenges such as limited coverage, excessive time and resource consumption, and poor accuracy, diminishing the effectiveness of forest disease prevention and control. By addressing these challenges, this study leverages drone remote sensing data combined with deep object detection models, specifically employing the YOLO-v3 algorithm based on loss function optimization, for the efficient and accurate detection of tree diseases and pests. Utilizing drone-mounted cameras, the study captures insect pest image information in pine forest areas, followed by segmentation, merging, and feature extraction processing. The computing system of airborne embedded devices is designed to ensure detection efficiency and accuracy. The improved YOLO-v3 algorithm combined with the CIoU loss function was used to detect forest pests and diseases. Compared to the traditional IoU loss function, CIoU takes into account the overlap area, the distance between the center of the predicted frame and the actual frame, and the consistency of the aspect ratio. The experimental results demonstrate the proposed model’s capability to process pest and disease images at a slightly faster speed, with an average processing time of less than 0.5 s per image, while achieving an accuracy surpassing 95%. The model’s effectiveness in identifying tree pests and diseases with high accuracy and comprehensiveness offers significant potential for developing forest inspection protection and prevention plans. However, limitations exist in the model’s performance in complex forest environments, necessitating further research to improve model universality and adaptability across diverse forest regions. Future directions include exploring advanced deep object detection models to minimize computing resource demands and enhance practical application support for forest protection and pest control.

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Section: Discussionmentioning

confidence: 99%

Detection of the Pine Wilt Disease Using a Joint Deep Object Detection Model Based on Drone Remote Sensing Data

Wu,

Yang,

Mao

2024

Forests

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“…The formula is given by Equation ( 9), where N X1 and N X2 , respectively, represent the sample sizes of the two factors X 1 and X 2 , and SSW X 1 and SSW X 2 , respectively, denote the sum of within-layer variances of the stratification formed by X 1 and X 2 . The calculation formula is given by Equation (10), where L 1 and L 2 , respectively, represent the numbers of layers of variables X 1 and X 2 .…”

Section: Interaction Types Judgement Criteriamentioning

confidence: 99%

“…According to the reconstructed time series, phenological extraction methods such as the threshold methods (e.g., dynamic threshold method [8]) and the VI change monitoring methods (e.g., moving-average method [9]) are used to obtain the characteristic nodes of key phenological stages such as the start of the growth season (SOS), the end of the growth season (EOS), the length of the growth season (LOS), and the date-position of peak value (POP). Recent phenological studies based on remote-sensing observation have mainly concentrated on middle and high latitudes [10,11], temperate-zone regions [12,13], and regions vulnerable to climate change [3,14]; in contrast, relatively few studies have focused on subtropical and low latitudes with complex stand structures and diverse phenological patterns. East Asian subtropical forests account for approximately 8% of the global net ecosystem productivity and have gradually become an important ecological-function area for investigations into the mitigation of global warming [15,16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Factors Driving Subtropical Forest Phenology Differentiation, Considering Temperature and Precipitation Time-Lag Effects: A Case Study of Fujian Province

Ma,

Zhang,

Qin

et al. 2024

Forests

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Subtropical forest phenology differentiation is affected by temperature, precipitation, and topography. Understanding the primary contributing elements and their interactions with forest phenology can help people better comprehend the subtropical forest growth process and its response to climate. Meanwhile, the temporal and spatial variations of phenological rhythms are important indicators of climatic impacts on forests. Therefore, this study aimed to analyze both a total area and different forest growth environments within the whole (i.e., coastal site areas (II, IV) and inland site areas (I, III)) as to spatiotemporal patterns associated with subtropical forests in Fujian Province, which is located at the boundary between the middle and south subtropical zones. Considering the asymmetric effects of climate and forest growth, this study chose pre-seasonal and cumulative temperature and precipitation factors and utilized the GeoDetector model to analyze the dominant drivers and interactions within phenology differentiation in Fujian Province. The results show the following: (1) All of the phenological parameters were advanced or shortened over the 19-year observation period; those of shrubland and deciduous broadleaf forests fluctuated greatly, and their stability was poor. (2) The phenological parameters were more distinct at the borders of the site areas. Additionally, the dates associated with the end of the growth season (EOS) and the date-position of peak value (POP) in coastal areas (i.e., II and IV) were later than those in inland areas (i.e., I and III). Among the parameters, the length of the growth season (LOS) was most sensitive to altitude. (3) Precipitation was the main driving factor affecting the spatial heterogeneity of the start of the growth season (SOS) and the EOS. The relatively strong effects of preseason and current-month temperatures on the SOS may be influenced by the temperature threshold required to break bud dormancy, and the relationship between the SOS and temperature was related to the lag time and the length of accumulation. The EOS was susceptible to the hydrothermal conditions of the preseason accumulation, and the variation trend was negatively correlated with temperature and precipitation. Spatial attribution was used to analyze the attribution of phenology differentiation from the perspectives of different regions, thus revealing the relationships between forest phenology and meteorological time-lag effects, the result which can contribute to targeted guidance and support for scientific forest management.

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“…In addition, under the influence of natural and human factors, land cover variables are highly prone to change in space, which in turn directly or indirectly affect the spatial distribution of landslides, and ultimately lead to significant spatial differences in landslide susceptibility (Reichenbach et al, 2014). With the advantages of wide coverage and high resolution, remote sensing technology provides an important and effective means for determining the spatial change characteristics of land cover variables (Liu et al, 2010;Zhang, 2016;Li et al, 2021). The analysis of the literature database revealed that although remote sensing means account for a high proportion in the acquisition methods of land cover variables (60% and 70% for land use and vegetation variables, respectively), researchers essentially began using remote sensing representation…”

Section: Land Covermentioning

confidence: 99%

Application of environmental variables in statistically-based landslide susceptibility mapping: A review

Zhao

Huang

et al. 2023

Front. Earth Sci.

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Environmental variables are crucial factors affecting the development and distribution of landslides, and they also provide vitally important information for statistically-based landslide susceptibility mapping (SLSM). The acquisition and utilization of appropriate and the most influential environmental variables and their combinations are crucial for improving the quality of SLSM results. However, compared with the construction of SLSM models based on machine learning, the acquisition and utilization of high-quality environmental variables have received very little attention. In order to further clarify the research status of the application of environmental variables and possible development directions in future research, this study systematically analyzed the application of environmental variables in SLSM. To this end, a literature database was constructed by collecting 261 peer-reviewed articles (from 2002 to 2021) on SLSM from the Web of Science and CNKI platform (www.cnki.net) based on the keywords of “landslide susceptibility” and “environmental variable.” We found that existing methods for determining environmental variables do not consider the regional representativeness and geomorphological significance of the variables. We also found that at present, environmental variables are utilized generally without the realization and understanding of their spatial heterogeneity. Accordingly, this study raises two major scientific issues: 1) Effective identification of important environmental variables required in SLSM. 2) Effective representation of the spatial heterogeneity of environmental variables in SLSM modeling. From the perspective of the identification of dominant variables and their geospatial pattern of heterogeneity, targeted solutions for future research are also preliminarily discussed, including the method for identifying dominant variables from qualitative and quantitative perspectives and SLSM model construction considering the specific geospatial patterns. In addition, the applicability and limitation of the mentioned methods are discussed.

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Spatiotemporal variations in remote sensing phenology of vegetation and its responses to temperature change of boreal forest in tundra-taiga transitional zone in the Eastern Siberia

Cited by 10 publications

References 24 publications

Detection of the Pine Wilt Disease Using a Joint Deep Object Detection Model Based on Drone Remote Sensing Data

Detection of the Pine Wilt Disease Using a Joint Deep Object Detection Model Based on Drone Remote Sensing Data

Analysis of Factors Driving Subtropical Forest Phenology Differentiation, Considering Temperature and Precipitation Time-Lag Effects: A Case Study of Fujian Province

Application of environmental variables in statistically-based landslide susceptibility mapping: A review

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