Emerging academic trends at the ForestSAT 2024 Conference

HOU Zhengyang, HUANG Huaguo

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (6) : 1-4.

PDF(1539 KB)
PDF(1539 KB)
JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (6) : 1-4. DOI: 10.12302/j.issn.1000-2006.202410001

Emerging academic trends at the ForestSAT 2024 Conference

Author information +
History +

Abstract

The Forest Spatial Analysis Technology (ForestSAT) Conference is a pivotal assembly in the global domain of forestry remote sensing, functioning as a vital gauge for advancements in this area of research. The ForestSAT 2024 was convented from September 9 to 13 in Rotorua, New Zealand, drawing academics from more than 40 nations to deliberate on 38 subjects, including statistical remote sensing, forest inventory, forest ecology, remote sensing algorithms, remote sensing products, and future trends. The conference underscored five significant aspects: firstly, the prominence of statistical remote sensing; secondly, the robust demand for remote sensing applications within forest ecology and inventory; thirdly, the accentuation on data fusion to capitalize on the strengths of multi-source and multi-modal data; fourthly, the vital support that remote sensing products contribute to the global “double carbon” strategy; lastly, the inquiry into future developmental trajectories for forestry remote sensing. During the event, two keynote speeches were presented, with additional avant-garde topics such as UAV remote sensing, upscaling, small-domain estimation, and error propagation. As an integration of sampling surveys, statistical models, and remote sensing technology, statistical remote sensing is instrumental in addressing the challenges of forestry remote sensing, elucidating the emerging developmental directions in this discipline.

Key words

ForestSAT / statistical remote sensing / upscaling / small-area estimation / error propagation

Cite this article

Download Citations
HOU Zhengyang , HUANG Huaguo. Emerging academic trends at the ForestSAT 2024 Conference[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2024, 48(6): 1-4 https://doi.org/10.12302/j.issn.1000-2006.202410001

References

[1]
MCROBERTS R E, COHEN W B, PFLUGMACHER D. Preface, 2012 ForestSAT special issue[J]. Remote Sens Environ, 2014, 151:1-2.DOI: 10.1016/j.rse.2014.02.010.
[2]
PFLUGMACHER D, MCROBERTS R E, MORSDORF F. Preface:ForestSAT 2014 special issue[J]. Remote Sens Environ, 2016, 173:211-213.DOI: 10.1016/j.rse.2015.11.025.
[3]
LIU Y, WANG Z S, SUN Q S, et al. Evaluation of the VIIRS BRDF,Albedo and NBAR products suite and an assessment of continuity with the long term MODIS record[J]. Remote Sens Environ, 2017, 201:256-274.DOI: 10.1016/j.rse.2017.09.020.
[4]
CAMPAGNOLO M L, SUN Q S, LIU Y, et al. Estimating the effective spatial resolution of the operational BRDF,albedo,and nadir reflectance products from MODIS and VIIRS[J]. Remote Sens Environ, 2016, 175:52-64.DOI: 10.1016/j.rse.2015.12.033.
[5]
MOHAMMED G H, COLOMBO R, MIDDLETON E M, et al. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation:50-years of progress[J]. Remote Sens Environ, 2019,231:111177.DOI: 10.1016/j.rse.2019.04.030.
[6]
SUOMALAINEN J, OLIVEIRA R A, HAKALA T, et al. Direct reflectance transformation methodology for drone-based hyperspectral imaging[J]. Remote Sens Environ, 2021,266:112691.DOI: 10.1016/j.rse.2021.112691.
[7]
ZHENG Y, HOU Z Y, STÅHL G, et al. Nexus of certain model-based estimators in remote sensing forest inventory[J]. For Ecosyst, 2024,11:100245.DOI: 10.1016/j.fecs.2024.100245.
[8]
STÅHL G, SAARELA S, SCHNELL S, et al. Use of models in large-area forest surveys:comparing model-assisted,model-based and hybrid estimation[J]. For Ecosyst, 2016, 3(1):5.DOI: 10.1186/s40663-016-0064-9.
[9]
HOU Z Y, DOMKE G M, RUSSELL M B, et al. Updating annual state- and county-level forest inventory estimates with data assimilation and FIA data[J]. For Ecol Manag, 2021,483:118777.DOI: 10.1016/j.foreco.2020.118777.
[10]
GOERNDT M E, MONLEON V J, TEMESGEN H. Small-area estimation of county-level forest attributes using ground data and remote sensed auxiliary information[J]. For Sci, 2013, 59(5):536-548.DOI: 10.5849/forsci.12-073.
[11]
HOU Z Y, MCROBERTS R E, ZHANG C Y, et al. Cross-classes domain inference with network sampling for natural resource inventory[J]. For Ecosyst, 2022,9:100029.DOI: 10.1016/j.fecs.2022.100029.
[12]
STÅHL G, GOBAKKEN T, SAARELA S, et al. Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time-and how this affects applications[J]. For Ecosyst, 2024,11:100164.DOI: 10.1016/j.fecs.2023.100164.
[13]
HOU Z Y, YUAN K Y, STÅHL G, et al. Conjugating remotely sensed data assimilation and model-assisted estimation for efficient multivariate forest inventory[J]. Remote Sens Environ, 2023,299:113854.DOI: 10.1016/j.rse.2023.113854.
[14]
CHEN F T, HOU Z Y, SAARELA S, et al. Leveraging remotely sensed non-wall-to-wall data for wall-to-wall upscaling in forest inventory[J]. Int J Appl Earth Obs Geoinf, 2023,119:103314.DOI: 10.1016/j.jag.2023.103314.
[15]
SAARELA S, HOLM S, HEALEY S P, et al. Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation[J]. Remote Sens Environ, 2022,278:113074.DOI: 10.1016/j.rse.2022.113074.
[16]
DUNCANSON L, KELLNER J R, ARMSTON J, et al. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission[J]. Remote Sens Environ, 2022, 270: 112845.
PDF(1539 KB)

Accesses

Citation

Detail

Sections
Recommended
The full text is translated into English by AI, aiming to facilitate reading and comprehension. The core content is subject to the explanation in Chinese.

/