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Multi-Sensor Forest Monitoring: Lidar, Multi-and Hyperspectral, Polarimetric Interferometric SAR

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 9592

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Special Issue Editors

Dr. Fabio Gonçalves

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Guest Editor

Canopy Remote Sensing Solutions, Florianópolis, SC 88032, Brazil
Interests: forest inventory; multi-sensor forest monitoring; 3D remote sensing; carbon dynamics

Dr. Robert Treuhaft

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Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Interests: remote-sensing measurement of biophysical attributes of tropical forests by combining biological and electromagnetic modeling
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Dr. Eben N. Broadbent

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Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32603, USA
Interests: remote sensing; forest ecology; conservation biology; airborne sensors; GatorEye; landscape simulation models; ecosystem and canopy ecology
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Dr. André Almeida

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Departamento de Engenharia Agrícola, Universidade Federal de Sergipe, SE 49100, Brazil
Interests: unmanned aerial vehicles (UAVs); enhanced forest inventories (EFI); tropical forests
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Special Issue Information

Dear Colleagues,

Remote sensing technologies required for accurate, consistent, and comprehensive forest assessment and monitoring have advanced substantially over the past few years, creating new opportunities in a diverse range of forest ecology and management applications.

New spaceborne lidar missions (GEDI and ICESat-2) now offer detailed measurements of vegetation vertical structure, sampled over nearly all tropical and temperate forests. An interferometric SAR mission in space (TanDEM-X), and two soon-to-be launched missions with interferometric components (BIOMASS and NISAR), demonstrate the capability of vertical structure and biomass measurements with contiguous, global coverage. Optical or microwave, these missions open up the possibility of structure and biomass monitoring in space and time (dynamics).

The ongoing deployment of several small satellite constellations by a fast-growing commercial imagery industry has been changing the paradigm in Earth observation. Multi-platform sensing enables near real-time, high spatial resolution, multispectral, hyperspectral and polarimetric interferometric SAR (PolInSAR) observations of forests across the globe. Multi-platform remote sensing also enables tomographic SAR (TomoSAR) profiling contiguously and globally.

At local scales, advancements in the miniaturization of lidar and hyperspectral sensors have recently enabled their application on unmanned aerial vehicles (UAVs), providing researchers with additional tools to produce remarkably detailed datasets. These new technologies are becoming increasingly more accessible and affordable and are expected to play a key role in calibration and validation of large-scale forest monitoring approaches.

At the same time, cloud-based platforms now provide simplified computational access to several public, analysis-ready datasets, including the full Landsat and Sentinel archives, providing the remote sensing community with unprecedented planetary-scale processing and analysis capabilities.

While some of these technological advances are already having a significant impact on forest monitoring research, further strategies for effective combination of technologies are needed to tap the full potential of multi-sensor data. In this context, in this Special Issue of Remote Sensing, we invite original research manuscripts that address, but are not limited to:

GEDI and ICESat-2 performance for 3D structure and biomass
PolInSAR/TomoSAR performance for structure and biomass
Multispectral and hyperspectral performance for composition, disturbance and recovery
Enhanced lidar structure/biomass using fusion with multispectral and/or PolInSAR
2D and 3D remote sensing combined for changes in forest extent and carbon stocks
Remotely sensed structure and its relationship to ecosystem function and biodiversity
Novel approaches to biomass estimation (e.g., combining lidar data and mechanistic models)
New calibration methods based on direct estimates of tree-level attributes from UAV
Uncertainty in multi-sensor forest monitoring

Dr. Fabio Gonçalves
Dr. Robert Treuhaft
Dr. Eben Broadbent
Dr. André Almeida
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Forest monitoring
Forest ecosystem structure, composition, and dynamics
Aboveground biomass
Multi-sensor fusion
Lidar
Polarimetric Interferometric SAR
Hyperspectral imagery
Digital Aerial Photogrammetry
SfM
Cloud computing

Published Papers (3 papers)

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Research

15 pages, 2440 KiB

Open AccessArticle

Tree Species Classification Based on Fusion Images by GF-5 and Sentinel-2A

by Weihua Chen, Jie Pan and Yulin Sun

Remote Sens. 2022, 14(20), 5088; https://doi.org/10.3390/rs14205088 - 12 Oct 2022

Cited by 3 | Viewed by 2030

Abstract

Forest ecosystem detection and assessment usually requires accurate spatial distribution information of forest tree species. Remote sensing technology has been confirmed as the most important method for tree species acquisition, and space-borne hyperspectral imagery, with the advantages of high spectral resolution, provides a better possibility for tree species classification. However, the present in-orbit hyperspectral imager has proved to be too low in spatial resolution to meet the accuracy needs of tree species classification. In this study, we firstly explored and evaluated the effectiveness of the Gram-Schmidt (GS) Harmonic analysis fusion (HAF) method for image fusion of GaoFen-5 (GF-5) and Sentinel-2A. Then, the Integrated Forest Z-Score (IFZ) was used to extract forest information from the fused image. Next, the spectral and textural features of the fused image, and topographic features extracted from DEM were selected according to random forest importance ranking (Mean Decreasing Gini (MDG) and Mean Decreasing Accuracy (MDA)), and imported into the random forest classifier to complete tree species classification. The results showed that: comparing some evaluation factors such as information entropy, average gradient and standard deviation of the fused images, the GS fusion image was proven to have a higher degree of spatial integration and spectral fidelity. The random forest importance ranking showed that WBI, Aspect, NDNI, ARI2, FRI were more important for tree species classification. Both the classification accuracy and kappa coefficients of the fused images were significantly greatly improved when compared to those of original GF-5 images. The overall classification accuracy ranged from 61.17% to 86.93% for different feature combination scenarios, and accuracy of the selected method based on MDA achieved higher results (OA = 86.93%, Kappa = 0.85). This study demonstrated the feasibility of fusion of GF-5 and Sentinel-2A images for tree species classification, which further provides good reference for application of in-orbit hyperspectral images. Full article

(This article belongs to the Special Issue Multi-Sensor Forest Monitoring: Lidar, Multi-and Hyperspectral, Polarimetric Interferometric SAR)

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20 pages, 5145 KiB

Open AccessArticle

Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images

by Yuping Tian, Zechuan Wu, Mingze Li, Bin Wang and Xiaodi Zhang

Remote Sens. 2022, 14(18), 4431; https://doi.org/10.3390/rs14184431 - 06 Sep 2022

Cited by 24 | Viewed by 3886

Abstract

With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study’s results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses. Full article

(This article belongs to the Special Issue Multi-Sensor Forest Monitoring: Lidar, Multi-and Hyperspectral, Polarimetric Interferometric SAR)

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15 pages, 2594 KiB

Open AccessArticle

Combining Sentinel-1 and Landsat 8 Does Not Improve Classification Accuracy of Tropical Selective Logging

by Matthew G. Hethcoat, João M. B. Carreiras, Robert G. Bryant, Shaun Quegan and David P. Edwards

Remote Sens. 2022, 14(1), 179; https://doi.org/10.3390/rs14010179 - 01 Jan 2022

Cited by 3 | Viewed by 2440

Abstract

Tropical forests play a key role in the global carbon and hydrological cycles, maintaining biological diversity, slowing climate change, and supporting the global economy and local livelihoods. Yet, rapidly growing populations are driving continued degradation of tropical forests to supply wood products. The United Nations (UN) has developed the Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme to mitigate climate impacts and biodiversity losses through improved forest management. Consistent and reliable systems are still needed to monitor tropical forests at large scales, however, degradation has largely been left out of most REDD+ reporting given the lack of effective monitoring and countries mainly focus on deforestation. Recent advances in combining optical data and Synthetic Aperture Radar (SAR) data have shown promise for improved ability to monitor forest losses, but it remains unclear if similar improvements could be made in detecting and mapping forest degradation. We used detailed selective logging records from three lowland tropical forest regions in the Brazilian Amazon to test the effectiveness of combining Landsat 8 and Sentinel-1 for selective logging detection. We built Random Forest models to classify pixel-based differences in logged and unlogged regions to understand if combining optical and SAR improved the detection capabilities over optical data alone. We found that the classification accuracy of models utilizing optical data from Landsat 8 alone were slightly higher than models that combined Sentinel-1 and Landsat 8. In general, detection of selective logging was high with both optical only and optical-SAR combined models, but our results show that the optical data was dominating the predictive performance and adding SAR data introduced noise, lowering the detection of selective logging. While we have shown limited capabilities with C-band SAR, the anticipated opening of the ALOS-PALSAR archives and the anticipated launch of NISAR and BIOMASS in 2023 should stimulate research investigating similar methods to understand if longer wavelength SAR might improve classification of areas affected by selective logging when combined with optical data. Full article

(This article belongs to the Special Issue Multi-Sensor Forest Monitoring: Lidar, Multi-and Hyperspectral, Polarimetric Interferometric SAR)

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