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Analysis of Forest Structure Based on Landsat and Sentinel Data

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Dr. Mariusz Ciesielski

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Department of Geomatics, Forest Research Institute, Sękocin Stary, Braci Leśnej 3, 05-090 Raszyn, Poland
Interests: urban forestry; remote sensing; ecosystem services; urban planning; spatial planning; trees
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Dr. Aneta Modzelewska

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MGGP Aero Sp. z o. o., Aleje Jerozolimskie 81, 02-001 Warsaw, Poland
Interests: hyperspectral imaging; vegetation classification; multitemporal classification; remote sensing

Dr. Paweł Hawryło

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Department of Forest Resources Management, Faculty of Forestry, University of Agriculture in Krakow, Al. 29 Listopada 46, 31‐425 Krakow, Poland
Interests: airborne laser scanning; deep learning; national forest inventory; forest growth modeling

Special Issue Information

Dear Colleagues,

The sustainable management of forest resources through their inventory, analyzing growth and monitoring threats (biotic, abiotic, anthropogenic) is now extensively supported by satellite imageries.

Satellite imageries are acquired for large areas practically and on a continuous basis, and they have been the subject of scientific research for many years because of their highly informational nature. Among their many applications in forestry, the following are of note: global forest monitoring based on time series analysis; the monitoring of deforestation and reforestation; the identification of threats and their consequences (e.g., insect gradation); the determination of forests and trees parameters (e.g., forest type classification, species classification); and biomass estimation.

Of particular importance to the use of satellite imagery in forestry are two optical satellite constellations that provide continuous data at medium spatial resolution globally and free of charge, namely Landsat (led by the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA)) and Sentinel-2 (led by the European Space Agency (ESA)).

In this Special Issue, we encourage colleagues from around the world to submit papers on the use of Landsat or Sentinel-2 satellite imagery in forestry. We expect original and innovative papers that address the methodological issues of data processing and analysis, as well as practical applications in forest resource management at different spatial scales. We are also open to review papers and meta-analyses that demonstrate the potential and application nature of satellite imagery.

Potential topics include, but are not limited to:

Mapping forest type and tree species;
Mapping of forest harvesting;
Monitoring forest biomass;
Forest disturbance monitoring;
Deforestation and reforestation;
Review and meta-analyses.

Dr. Mariusz Ciesielski
Dr. Aneta Modzelewska
Dr. Paweł Hawryło
Guest Editors

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Keywords

forest structure
vegetation mapping
forest ecosystem
satellite imagery
image classification
forest inventory
remote sensing
Landsat
Sentinel
ecosystem services

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Research

21 pages, 17010 KiB

Open AccessArticle

Estimation of Forest Stock Volume Using Sentinel-2 MSI, Landsat 8 OLI Imagery and Forest Inventory Data

by Yangyang Zhou and Zhongke Feng

Forests 2023, 14(7), 1345; https://doi.org/10.3390/f14071345 - 29 Jun 2023

Cited by 4 | Viewed by 1758

Abstract

Forest stock volume (FSV) is a key indicator for measuring forest quality, evaluating forest management capabilities, and the main factor for evaluating forest carbon sequestration levels. In this study, to achieve an accurate estimation of FSV, we used Ninth Beijing Forest Inventory data (FID), and Landsat 8 OLI and Sentinel-2 MSI imagery to establish FSV models. The performance of Landsat 8 and Sentinel-2 imagery data in estimating forest volume in Huairou District, Beijing, China was compared. The combination of Landsat 8 and Sentinel-2 satellite data was employed to create a new data source. Two variable selection methods, linear stepwise regression (LSR) and recursive feature elimination (RFE), were used to extract feature variables. The multiple linear regression(MLR) models, Back Propagation (BP) neural network models, and Random Forest (RF) models were employed to estimate forest volume in the study area based on the feature variables obtained from both data sources. The research results indicate (1) the Sentinel-2-based model achieved higher accuracy compared to the same model based on the Landsat 8 factor set. The correlation between the red-edge band of Sentinel-2 imagery and FSV is more significant than that of other characteristic variables used. Variables derived from the red-edge band have the potential to reduce model errors; (2) the estimation accuracy of the model can be significantly improved by using the RFE (Recursive Feature Elimination) method to select remote sensing feature variables. RFE is based on the importance ranking of all feature variables and selects the feature variables that contribute the most to the model. In the variable group selected by RFE, the texture features and the derived features from the red-edge band, such as

S e n_{B 5}

S e n_{R V I}

S e n_{m N D V I r e}

, and

S e n_{B 5 M e a n}

, contribute the most to the improvement of model accuracy. Furthermore, in the optimal Landsat 8–Sentinel-2 RFE-RF model, where texture features are involved, the rRMSE is greatly reduced by 3.7% compared to the joint remote sensing RFE-RF model without texture features; (3) the MLR, BP, and RF models based on the modeling factor set established on Sentinel-2 have accuracy superior to the model accuracy established based on the modeling factor set of Landsat 8. Among them, the Random Forest (RF) method inverted by the recursive feature elimination (RFE) method using Sentinel-2A image has the best inversion accuracy effect (

R^{2}

= 0.831,

R M S E

= 12.604 m

^{3}

^{- 1}

r R M S E

= 36.411%,

M A E

= 9.366 m

^{3}

^{- 1}

). Comparing the performance of the models on the test set, the ranking is as follows, Random Forest (RF) model > Back Propagation (BP) neural network model > multiple linear regression (MLR) model. The feature variable screening based on the Random Forest’s recursive feature elimination (RFE) method is better than the linear stepwise regression (LSR). Therefore, the RFE-RF method based on the joint variables from Landsat 8 and Sentinel-2 satellite data to establish a new remote sensing data source provides the possibility to improve the estimation accuracy of FSV and provides reference for forest dynamic monitoring. Full article

(This article belongs to the Special Issue Analysis of Forest Structure Based on Landsat and Sentinel Data)

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