Spatiotemporal variation and driving factors of vegetation net primary productivity in a typical karst area in China from 2000 to 2010

Song, Liyi; Li, Mingyang; Hai, Xu; Guo, Ying; Wang, Zi; Li, Yingchang; Wu, Xuejuan; Feng, Luchun; Chen, Jun; Lü, Xin; Xu, Yabei; Li, Tao

doi:10.1016/j.ecolind.2021.108280

Cited by 20 publications

(12 citation statements)

References 59 publications

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“…Aerial photography provides high-resolution remote sensing images ( Song et al, 2021 ) and is often used to map, manage, and analyze tree distributions ( Xu et al, 2019 ). However, the captured tree crowns consistently exhibit considerable differences in appearance due to varying capture positions between UAV-loaded cameras and the target trees.…”

Section: Discussionmentioning

confidence: 99%

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

et al. 2022

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Deriving individual tree crown (ITC) information from light detection and ranging (LiDAR) data is of great significance to forest resource assessment and smart management. After proof-of-concept studies, advanced deep learning methods have been shown to have high efficiency and accuracy in remote sensing data analysis and geoscience problem solving. This study proposes a novel concept for synergetic use of the YOLO-v4 deep learning network based on heightmaps directly generated from airborne LiDAR data for ITC segmentation and a computer graphics algorithm for refinement of the segmentation results involving overlapping tree crowns. This concept overcomes the limitations experienced by existing ITC segmentation methods that use aerial photographs to obtain texture and crown appearance information and commonly encounter interference due to heterogeneous solar illumination intensities or interlacing branches and leaves. Three generative adversarial networks (WGAN, CycleGAN, and SinGAN) were employed to generate synthetic images. These images were coupled with manually labeled training samples to train the network. Three forest plots, namely, a tree nursery, forest landscape and mixed tree plantation, were used to verify the effectiveness of our approach. The results showed that the overall recall of our method for detecting ITCs in the three forest plot types reached 83.6%, with an overall precision of 81.4%. Compared with reference field measurement data, the coefficient of determination R2 was ≥ 79.93% for tree crown width estimation, and the accuracy of our deep learning method was not influenced by the values of key parameters, yielding 3.9% greater accuracy than the traditional watershed method. The results demonstrate an enhancement of tree crown segmentation in the form of a heightmap for different forest plot types using the concept of deep learning, and our method bypasses the visual complications arising from aerial images featuring diverse textures and unordered scanned points with irregular geometrical properties.

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Section: Discussionmentioning

confidence: 99%

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

et al. 2022

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“…The alluvial plain of the Sushui River in the northwestern part of the Yuncheng Basin is characterized by flat terrain, fertile land, adequate irrigation, and the occurrence of significant human activities. These features have greater effects on crop growth (L. Song et al., 2021), as they facilitate the growth and development of food crops with high NPP accumulations, reaching 900 gC m −2 and summer maize yields of 8,000 kg ha −1 . The high degree of topographic fragmentation in the northeastern mountainous and northern plateau areas makes erosion more likely, as it prevents NPP accumulation.…”

Section: Discussionmentioning

confidence: 99%

Yield estimation of summer maize based on multi‐source remote‐sensing data

Wang

Liu

et al. 2022

Agronomy Journal

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Accurately estimating regional‐scale crop yields is substantial in determining current agricultural production performance and effective agricultural land management. The Yuncheng Basin is an important grain‐producing area in the Shanxi Province. This paper used Sentinel 2A with a spatial resolution of 10 m and MODIS with a temporal resolution of 1 d in 2020. The spatial and temporal nonlocal filter‐based fusion model (STNLFFM) was used to obtain fused data with a spatial resolution of 10 m and a temporal resolution of 1 d, combined with the Carnegie–Ames–Stanford Approach (CASA) and light‐use efficiency model to achieve summer maize (Zea mays L.) yield estimation. The results showed that the fused normalized difference vegetation index (NDVI) could inherit the spatial Sentinel‐2A NDVI details and express the spatial differences between smaller features more effectively. The STNLFFM NDVI curve was consistent with the actual summer maize growth condition, which accurately reflects the NDVI trend and local abrupt change information during the summer maize growth period. Moreover, the fused NDVI was influenced by topographic differences and artificial irrigation factors, whereas the summer maize yield in mountainous and plateau areas of the Yuncheng Basin was <5,000 kg ha−1 and those in the alluvial plain of the Sushui River reached 8,000 kg ha−1. The accuracy of the yield estimation model constructed based on STNLFFM NDVI (mean absolute percentage error [MAPE] = 5.47%, −13.74% ≤ relative error [RE] ≤0.12%) was significantly higher than that of the model based on MODIS NDVI (MAPE = 15.65%, −19.67% ≤ RE ≤ 20.88%), indicating that the use of spatio‐temporal fusion technology can effectively improve the summer maize yield estimation accuracy.

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“…The advantages of CiSL-NPP were verified through comparing it to linear regression [92], [93], deep belief network(DBN) [94] and MultiLayer Perceptron (MLP) [95]. As shown in Fig.…”

Section: ) Comparison With Machine Learning and Deep Learning Algorithmsmentioning

confidence: 95%

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

et al. 2023

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Carbon storage capacity can be estimated to establish evaluation standards and statistics for carbon neutrality. Existing estimation methods including machine learning system have weakness modeling ability, and they are unable to deal with the complex topographies and temporal changes in vegetation in urban zones. However, a deep neural network has the potential ability to face such complex scenes because of its nonlinear fitting properties which has been widely used in industry. In this study, a novel and powerful neural network learning system named CiSL-NPP is proposed, to integrate multi-source data as an accurate, efficient, simple, long-term, city-scale carbon storage capacity measurement method. We use an unsupervised generation model spatio-temporal Masked AutoEncoder (st-MAE) and lightweight RNN model to efficiently obtain high-quality data that contain time-series information such as seasons; this minimizes any undue impact of meteorological, temporal, and spatial factors on the measurement owing to the multi-source, multi-modal nature of the data. In the MAE, we add seasonal coding to make it time-series sensitive; we also embed road network information to accurately perceive the complex topography of the city. Results for 16 cities in China and Europe revealed that the proposed method shows: 1) Higher-quality generated data (MSE is 0.13-0.29); 2) Accurate coverage of time series and complex geographical features; 3) Satisfaction of estimation demands with only 316 RMB cost; 4) Capability to evolve a long-term trend of urban vegetation carbon storage capacity in 4.2 days; and 5) Easily interpretable results which could, in practice, provide sound guidance for urban planning and decision-making processes. INDEX TERMSNeural network Learning System, Unsupervised generation model, Spatio-temporal Masked AutoEncoder, Carbon Storage Capacity Measurement I. INTRODUCTION M ORE than 90% of human economic activity takes place in cities [1], which directly contribute at least 85% of global carbon emissions [2] and are regarded as the world's main battlefields of carbon neutrality [3]. Currently, carbon mitigation and carbon sink increment are the primary approaches to achieving urban carbon neutrality. The carbon mitigation approach, a complex systematic undertaking, is difficult to implement [4]. Further, strict policies towards emissions may have consequences such as carbon leakage [5], [6]. The carbon sink increment approach shows more potential to achieve urban carbon neutrality. For example, studies indicate that the carbon sink of urban vegetation can offset more than 22.45% of carbon emissions [7], already achieving carbon neutrality in some areas [8]. In city, limited to a fixed-size space, the effective way to increase carbon sink is to use green resources (e.g., municipal parks) rationally according to their vegetation carbon storage capacity. Net Primary Productivity (NPP), a crucial metric reflecting the storage effect of vegetation , is the key role for increasing carbon storage capacity in the city ...

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Spatiotemporal variation and driving factors of vegetation net primary productivity in a typical karst area in China from 2000 to 2010

Cited by 20 publications

References 59 publications

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

Yield estimation of summer maize based on multi‐source remote‐sensing data

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

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