New Insights into Remote Sensing of Vegetation Structural Parameters
1
Innovation Research Center of Satellite Application (IRCSA), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Department of Geographic Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(9), 1555; https://doi.org/10.3390/f15091555
Submission received: 25 July 2024
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Revised: 30 July 2024
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Accepted: 12 August 2024
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Published: 4 September 2024
(This article belongs to the Special Issue New Insights into Remote Sensing of Vegetation Structural Parameters)
The accurate and efficient estimation of vegetation structural parameters from remote sensing is a pivotal subject within the field of remote sensing. In recent years, there has been a surge in research dedicated to developing innovative techniques in regard to sensors and data analysis methodologies in order to refine the retrieval process and the utilization of the retrieved parameters.
However, future developments are still necessary to optimize the retrieval of vegetation structural parameters. The Special Issue “New Insights into Remote Sensing of Vegetation Structural Parameters” presents several examples of technological advancements that can enhance the retrieval and utilization of these parameters. Focusing on Lidar, multispectral/hyperspectral sensors, and the Bidirectional Reflectance Distribution Function (BRDF), these studies explored advancements in various aspects such as data acquisition, parameter inversion modeling, and spatio-temporal change analysis. The remote sensing advancements discussed in these studies have significant implications for improving our understanding and management of terrestrial ecosystems. They provide valuable insights into various environmental research, monitoring, and decision-making applications.
On the topic of estimating forest biomass, Li et al. [1] used Airborne Laser Scanning (ALS) data collected from the National Park of Hainan Tropical Rainforest to develop a model for estimating Above Ground Biomass (AGB). They introduced several new metrics retrieved from the alpha-shape of point clouds to improve the forest biomass estimation. Sun et al. [2] discussed the accuracy of NASA’s GEDI Lidar observations in estimating biomass in temperate and tropical forests. Their findings indicated that the GEDI-derived forest structure and ground elevation data were effective for estimating AGB in these forest types. The advanced Lidar system can provide valuable information about forest structures and biomass that can be used for various applications, such as carbon monitoring and forest management.
Post-fire vegetation recovery provides a scientific basis for the sustainable management of forest ecosystems. Hao et al. [3] utilized multi-source remote sensing data and the Google Earth Engine (GEE) platform to analyze vegetation recovery after forest fires in China. They employed various vegetation indices (NDVI, EVI, NBR, and NDMI) to quantify the severity of burned areas (low, moderate, and high) and used the ridge regression model to examine the factors that can affect vegetation regeneration processes.
Remote sensing data fusion and vegetation index modeling are crucial for the remote sensing of vegetation structural parameters. Teresneu et al. [4] assessed the reliability of using a laser caliper to measure the upper-stem diameter of trees by multiple users. The results demonstrated the effectiveness of using a laser caliper for upper-stem diameter measurements, making it a valuable tool for researchers and forestry professionals in assessing tree growth and health. Song et al. [5] presented a method for estimating Fractional Vegetation Cover (FVC) using a Vegetation Index (VI)-based mixture model and multiple satellite data sources. The research involved the joint utilization of multiple satellite datasets, derived from different sensors or platforms, with the objective of enhancing FVC estimation. Li et al. [6] revisited the performance of the kernel-driven BRDF (Bidirectional Reflectance Distribution Function) model using filtered high-quality observations from the POLDER-3 sensor. Ding et al. [7] analyzed the spatio-temporal variation in the start of the spring season (SOS) of subtropical forests across the Nanling Mountains in southern China from 1999 to 2019. They found significant spatio-temporal variations in the SOS of subtropical forests across the Nanling Mountains. Both earlier and later periods of the SOS were observed during the study.
In general, these studies explore new methods and new findings in regard to using remote sensing technology to monitor and evaluate vegetation structural parameters from different perspectives. They provide valuable insights for further improving the accuracy and application of vegetation structure remote sensing.
Funding
This research received no external funding.
Acknowledgments
We thank for Xiaohan Sun for revising the manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
References
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MDPI and ACS Style
Yan, K.; Ding, C.; Qi, J. New Insights into Remote Sensing of Vegetation Structural Parameters. Forests 2024, 15, 1555. https://doi.org/10.3390/f15091555
AMA Style
Yan K, Ding C, Qi J. New Insights into Remote Sensing of Vegetation Structural Parameters. Forests. 2024; 15(9):1555. https://doi.org/10.3390/f15091555
Chicago/Turabian StyleYan, Kai, Chao Ding, and Jianbo Qi. 2024. "New Insights into Remote Sensing of Vegetation Structural Parameters" Forests 15, no. 9: 1555. https://doi.org/10.3390/f15091555
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