Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning

Yu, Tao; Pang, Yong; Sun, Rui; Niu, Xiaodong

doi:10.1109/access.2022.3210218

Cited by 2 publications

(2 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) Neural network system Some scholars have argued the feasibility and broad application prospects of deep learning models to estimate NPP in large-scale forest scenarios [74], but no empirical research on urban application has been conducted. The application of deep learning for urban NPP estimation is limited by remote sensing data availability, time series perception ability, and climate perception ability.…”

Section: ) Npp Functionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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 ...

show abstract

Section: ) Npp Functionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The potential for deep learning applications in environmental modelling is growing [28]. For example, Yu et al used four deep learning models, including deep neural network, convolutional neural network, back propagation neural network, and recurrent neural network to downscale GLASS GPP and NPP data to a higher resolution [29].…”

Section: Introductionmentioning

confidence: 99%

A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning

Shangguan

Xiong

Nourani

et al. 2023

Forests

View full text Add to dashboard Cite

Global carbon fluxes describe the carbon exchange between land and atmosphere. However, already available global carbon fluxes datasets have not been adjusted by the available site data and deep learning tools. In this work, a global carbon fluxes dataset (named as GCFD) of gross primary productivity (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) has been developed via a deep learning based convolutional neural network (CNN) model. The dataset has a spatial resolution of 1 km at three time steps per month from January 1999 to June 2020. Flux measurements were used as a training target while remote sensing of vegetation conditions and meteorological data were used as predictors. The results showed that CNN could outperform other commonly used machine learning methods such as random forest (RF) and artificial neural network (ANN) by leading to satisfactory performance with R2 values of the validation stage as 0.82, 0.72 and 0.62 for GPP, RECO, and NEE modelling, respectively. Thus, CNN trained using reanalysis meteorological data and remote sensing data was chosen to produce the global dataset. GCFD showed higher accuracy and more spatial details than some other global carbon flux datasets with reasonable spatial pattern and temporal variation. GCFD is also in accordance with vegetation conditions detected by remote sensing. Owing to the obtained results, GCFD can be a useful reference for various meteorological and ecological analyses and modelling, especially when high resolution carbon flux maps are required.

show abstract

Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning

Cited by 2 publications

References 56 publications

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

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

A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning

Contact Info

Product

Resources

About