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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 January 2023) | Viewed by 26584

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Special Issue Editors

Dr. Michele Martone

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Guest Editor

Microwaves and Radar Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Interests: forest mapping with SAR interferometry (InSAR); forest change detection; SAR raw data quantization; data volume reduction methods for future SAR systems
Special Issues, Collections and Topics in MDPI journals

Dr. Armando Marino

E-Mail Website
Guest Editor

Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK
Interests: remote sensing; synthetic aperture radar (SAR); polarimetric SAR; forest aboveground biomass; polarimetric target detector
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a vital natural resource, forests are of extreme importance for all living beings on our planet. They play a key role in controlling climate change, represent an essential source of energy (e.g., biomass), food, jobs, and livelihoods, and serve as natural habitat to a large variety of animal species, which is essential for biodiversity preservation.

Forest ecosystems are constantly shaped and changed by physical and biological disturbances and eventual regeneration processes. Today, forest degradation is occurring at an alarming rate, often due to illegal anthropogenic activities such as logging and fires, such that sensitive environments have been irreversibly damaged, with critical environmental and economic consequences at regional as well as at global scale. A precise and efficient assessment and monitoring of the forest resources, treatments, and recreational opportunities is therefore of crucial importance in order to develop early warning systems. In this scenario, synthetic aperture radar (SAR) remote sensing represents a unique technique for providing high-resolution images independently of daylight and almost any weather conditions. In the last few decades, SAR imaging has demonstrated its suitability for forest mapping applications. The combination of the polarimetric, interferometric, and/or tomographic information further increases its capabilities and the achievable product accuracy.

As Guest Editors, we would like to dedicate this Special Issue to documenting SAR-based methods for forest mapping. Well-prepared, unpublished submissions that address one or more of the following topics are solicited:

New methods and concepts for the quantitative assessment of forest biomass;
Combination of complementary SAR imaging methods (tomography, polarimetry, interferometry) to define novel approaches, concepts, and applications for forest mapping and monitoring;
Feasibility studies with new sensors, ranging from drones to spaceborne SAR systems, and their applications to forestry;
Combined use of multifrequency SAR imaging for forest applications;
Comparison and benchmarking studies using various sensors and/or processing methods for forestry;
New approaches for the detection of forest changes;
Potential of artificial intelligence-based methods for forest information retrieval;
Novel methodologies considering the fusion of SAR data with data from other sources.

Dr. Michele Martone
Dr. Armando Marino
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.

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Keywords

Drone/airborne/spaceborne synthetic aperture radar (SAR)
Biomass estimation
Forest mapping
Change detection
SAR polarimetry, interferometry, tomography
Artificial intelligence for forest applications
Data fusion of SAR with other sensors

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Related Special Issue

SAR for Forest Mapping III in Remote Sensing (5 articles)

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Editorial

Jump to: Research

4 pages, 183 KiB

Open AccessEditorial

Editorial for the Special Issue “SAR for Forest Mapping II”

by Michele Martone and Armando Marino

Remote Sens. 2023, 15(18), 4376; https://doi.org/10.3390/rs15184376 - 6 Sep 2023

Cited by 1 | Viewed by 1015

Abstract

As vital natural resources, forests are of extreme importance for all living beings on our planet [...] Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

Research

Jump to: Editorial

18 pages, 5660 KiB

Open AccessArticle

Mapping Growing Stem Volume Using Dual-Polarization GaoFen-3 SAR Images in Evergreen Coniferous Forests

by Zilin Ye, Jiangping Long, Huanna Zheng, Zhaohua Liu, Tingchen Zhang and Qingyang Wang

Remote Sens. 2023, 15(9), 2253; https://doi.org/10.3390/rs15092253 - 24 Apr 2023

Cited by 3 | Viewed by 1375

Abstract

Unaffected by cloud cover and solar illumination, synthetic aperture radar (SAR) images have great capability to map forest growing stem volume (GSV) in complex biophysical environments. Up to now, c-band dual-polarization Gaofen-3 (GF-3) SAR images, acquired by the first Chinese civilian satellite equipped with multi-polarized modes, are rarely applied in mapping forest GSV. To evaluate the capability of dual-polarization GF-3 SAR images in mapping forest GSV, several proposed derived features were initially extracted by mathematical operations and applied to obtain optimal feature sets by different feature sorting methods and feature selection methods. Then, the maps of GSV in an evergreen coniferous forest were inverted by various machine learning algorithms and stacking ensemble learning methods with different strategies. The results implied that backscattering coefficients and partially proposed derived features showed high sensitivity to the forest GSV, and the saturation phenomenon also obviously occurred once the forest GSV was larger than 300 m³/ha. Furthermore, the results showed that the accuracy of the mapped GSV was significantly improved using the stacking ensemble learning methods. Using various optimal feature sets and base models (MLR, KNN, SVM, and RF), the rRMSE values mainly ranged from 30% to 40%. After using the stacking ensemble learning methods, the values of rRMSE ranged from 16.71% to 20.51%. This confirmed that dual-polarization GF-3 images have great potential to map forest GSV in evergreen coniferous forests. Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

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20 pages, 5376 KiB

Open AccessArticle

Evaluating the Sensitivity of Polarimetric Features Related to Rotation Domain and Mapping Chinese Fir AGB Using Quad-Polarimetric SAR Images

by Tingchen Zhang, Hui Lin, Jiangping Long, Huanna Zheng, Zilin Ye and Zhaohua Liu

Remote Sens. 2023, 15(6), 1519; https://doi.org/10.3390/rs15061519 - 10 Mar 2023

Cited by 5 | Viewed by 1688

Abstract

Unaffected by cloud cover and solar illumination, synthetic aperture radar (SAR) images coupled with quad-polarimetric techniques have significant potential for mapping forest aboveground biomass (AGB) in the mountains of southern China. To improve the accuracy of mapping forest AGB, it is necessary to accurately interpret and evaluate the sensitivity of polarimetric features related to polarimetric response in complex forests. In this study, several rotated polarimetric features were extracted from L-band quad-polarimetric ALOS PALSAR-2 images based on uniform polarimetric matrix rotation theory. In addition, the sensitivity of rotated polarimetric features with forest parameters was evaluated by the Pearson correlation coefficient, sensitivity index (SI), and saturation levels. Ultimately, the forest AGB was mapped with various combinatorial feature sets by a proposed feature selection method based on the sensitivity index. The results illustrated that rotated polarimetric features extracted from the rotational domain have higher sensitivity with various forest parameters and higher saturation levels for mapping forests than other traditional features. After using the proposed feature selection method and combinatorial feature sets, the rRMSE of mapped forest AGB ranged from 22.5% to 33.9% for two acquired images, and the best result was obtained from the combination of three types of polarimetric features (BC + C4 + Ro). It is also confirmed that different types of features extracted from quad-polarimetric SAR images have better compensation effects and the accuracy of mapped forest AGB is significantly improved. Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

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22 pages, 13752 KiB

Open AccessArticle

Potential of Sentinel-1 SAR to Assess Damage in Drought-Affected Temperate Deciduous Broadleaf Forests

by Konstantin Schellenberg, Thomas Jagdhuber, Markus Zehner, Sören Hese, Marcel Urban, Mikhail Urbazaev, Henrik Hartmann, Christiane Schmullius and Clémence Dubois

Remote Sens. 2023, 15(4), 1004; https://doi.org/10.3390/rs15041004 - 11 Feb 2023

Cited by 10 | Viewed by 2949

Abstract

Understanding forest decline under drought pressure is receiving research attention due to the increasing frequency of large-scale heat waves and massive tree mortality events. However, since assessing mortality on the ground is challenging and costly, this study explores the capability of satellite-borne Copernicus Sentinel-1 (S-1) C-band radar data for monitoring drought-induced tree canopy damage. As droughts cause water deficits in trees and eventually lead to early foliage loss, the S-1 radiometric signal and polarimetric indices are tested regarding their sensitivities to these effects, exemplified in a deciduous broadleaf forest. Due to the scattered nature of mortality in the study site, we employed a temporal-only time series filtering scheme that provides very high spatial resolution (10 m

\times

10 m) for measuring at the scale of single trees. Finally, the anomaly between heavily damaged and non-damaged tree canopy samples (n = 146 per class) was used to quantify the level of damage. With a maximum anomaly of −0.50 dB ± 1.38 for S-1 Span (VV+VH), a significant decline in hydrostructural scattering (moisture and geometry of scatterers as seen by SAR) was found in the second year after drought onset. By contrast, S-1 polarimetric indices (cross-ratio, RVI,

H α

) showed limited capability in detecting drought effects. From our time series evaluation, we infer that damaged canopies exhibit both lower leaf-on and leaf-off backscatters compared to unaffected canopies. We further introduce an NDVI/Span hysteresis showing a lagged signal anomaly of Span behind NDVI (by ca. one year). This time-lagged correlation implies that SAR is able to add complementary information to optical remote sensing data for detecting drought damage due to its sensitivity to physiological and hydraulic tree canopy damage. Our study lays out the promising potential of SAR remote sensing information for drought impact assessment in deciduous broadleaf forests. Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

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17 pages, 2398 KiB

Open AccessArticle

First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity

by Maryam Poorazimy, Shaban Shataee, Hossein Aghababaei, Erkki Tomppo and Jaan Praks

Remote Sens. 2023, 15(3), 555; https://doi.org/10.3390/rs15030555 - 17 Jan 2023

Cited by 2 | Viewed by 2068

Abstract

Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (

σ_{d b h}

) and the number of trees (

N

), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height

h_{v}

and ground phase

φ_{0}

. The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of

σ_{d b h}

and

N

, thirteen geometrical and physical parameters were defined (

P 1 - P 13

). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting

σ_{d b h}

and

N

. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R²* of 0.25 for

σ_{d b h}

, whereas only 22% of the variation in

N

was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies. Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

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25 pages, 7595 KiB

Open AccessArticle

Forest Height Inversion Based on Time–Frequency RVoG Model Using Single-Baseline L-Band Sublook-InSAR Data

by Lei Wang, Yushan Zhou, Gaoyun Shen, Junnan Xiong and Hongtao Shi

Remote Sens. 2023, 15(1), 166; https://doi.org/10.3390/rs15010166 - 28 Dec 2022

Cited by 4 | Viewed by 4943

Abstract

The interferometric synthetic aperture radar (InSAR) technique based on time–frequency (TF) analysis has great potential for mapping the forest canopy height model (CHM) at regional and global scales, as it benefits from the additional InSAR observations provided by the sublook decomposition. Meanwhile, due to the wider swath and higher spatial resolution of single-polarization data, InSAR has a higher observation efficiency in comparison with PolInSAR. However, the accuracy of the CHM inversion obtained by the TF-InSAR method is attenuated by its inaccurate coherent scattering modeling and uncertain parameter calculation. Hence, a new approach for CHM estimation based on single-baseline InSAR data and sublook decomposition is proposed in this study. With its derivation of the coherent scattering modeling based on the scattering matrix of sublook observations, a time–frequency based random volume over ground (TF-RVoG) model is proposed to describe the relationship between the sublook coherence and the forest biophysical parameters. Then, a modified three-stage method based on the TF-RVoG model is used for CHM retrieval. Finally, the two-dimensional (2-D) ambiguous error of pure volume coherence caused by residual ground scattering and temporal decorrelation is alleviated in the complex unit circle. The performance of the proposed method was tested with airborne L-band E-SAR data at the Krycklan test site in Northern Sweden. Results show that the modified three-stage method provides a root-mean-square error (RMSE) of 5.61 m using InSAR and 14.3% improvement over the PolInSAR technique with respect to the classical three-stage inversion result. An inversion accuracy of RMSE = 2.54 m is obtained when the spatial heterogeneity of CHM is considered using the proposed method, demonstrating a noticeable improvement of 32.8% compared with results from the existing method which introduces the fixed temporal decorrelation factor. Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

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22 pages, 15539 KiB

Open AccessArticle

Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data

by Jose-Luis Bueso-Bello, Daniel Carcereri, Michele Martone, Carolina González, Philipp Posovszky and Paola Rizzoli

Remote Sens. 2022, 14(16), 3981; https://doi.org/10.3390/rs14163981 - 16 Aug 2022

Cited by 8 | Viewed by 1900

Abstract

The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average F-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation. Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

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26 pages, 3562 KiB

Open AccessArticle

A Selection of Experiments for Understanding the Strengths of Time Series SAR Data Analysis for Finding the Drivers Causing Phenological Changes in Paphos Forest, Cyprus

by Milto Miltiadou, Vassilia Karathanassi, Athos Agapiou, Christos Theocharidis, Polychronis Kolokousis and Chris Danezis

Remote Sens. 2022, 14(15), 3581; https://doi.org/10.3390/rs14153581 - 26 Jul 2022

Cited by 3 | Viewed by 2821

Abstract

Observing phenological changes are important for evaluating the natural regeneration process of forests, especially in Mediterranean areas where the regeneration of coniferous forests depends on seeds and the changes in blossoming time are influenced by climate change. The high temporal resolution of Sentinel-1 data allows the time series analysis of synthetic aperture radar (SAR) data, but it is still unknown how these data could be utilised for better understanding forest phenology and climate-related alternations. This study investigates the phenological cycle of Paphos forest, Cyprus using SAR data from 1992 to 2021, acquired by ERS-1/2, Envisat and Sentinel-1. An average phenological diagram was created for each space mission and a more detailed analysis was performed from October 2014 to November 2021, using the higher temporal resolution of Sentinel-1 data. Meteorological data were used to better understand the drivers of blooming alternations. Using the interquartile range (IQR), outliers were detected and replaced using the Kalman filter imputation. Forecasting trend lines were used to estimate the amplitude of the summer peaks and the annual mean. The observation of the average phenology from each satellite mission showed that there were two main blooming peaks each year: the winter and the summer peak. We argue that the winter peak relates to increased foliage, water content and/or increased soil moisture. The winter peak was followed by a fall in February reaching the lower point around March, due to the act of pine processionary (Thaumetopoea pityocampa). The summer peak should relate to the annual regeneration of needles and the drop of the old ones. A delay in the summer peak—in August 2018—was associated with increased high temperatures in May 2018. Simultaneously, the appearance of one peak instead of two in the

σ_{V H}

time series during the period November 2014–October 2015 may be linked to a reduced act of the pine processionary associated with low November temperatures. Furthermore, there was an outlier in February 2016 with very low backscattering coefficients and it was associated with a drought year. Finally, predicting the amplitude of July 2020 returned high relevant Root Mean Square Error (rRMSE). Seven years of time series data are limiting for predicting using trend lines and many parameters need to be taken into consideration, including the increased rainfall between November 2018 and March 2020. Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

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21 pages, 11848 KiB

Open AccessArticle

Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series

by Ricardo Dal Molin, Jr. and Paola Rizzoli

Remote Sens. 2022, 14(6), 1381; https://doi.org/10.3390/rs14061381 - 12 Mar 2022

Cited by 10 | Viewed by 3039

Abstract

The monitoring of land cover and land use patterns is pivotal in the joint effort to fight deforestation in the Amazon and study its relation to climate change effects with respect to anthropogenic activities. Most of the region, typically monitored with optical sensors, is hidden by a persistent cloud cover for most of the wet season. The necessity for a consistent and reliable deforestation warning system based on cloud-independent radar data is therefore of particular interest. In this paper, we investigated the potential of combining deep learning with Sentinel-1 (S-1) Interferometric Synthetic Aperture Radar (InSAR) short time series (STS), covering only 24 d of acquisitions, to map endangered areas in the Amazon Basin. To this end, we implemented a U-Net-like convolutional neural network (CNN) for multi-layer semantic segmentation, trained from scratch with different sets of input features to evaluate the viability of the proposed approach for different operating conditions. As input features, we relied on both multi-temporal backscatter and interferometric coherences at different temporal baselines. We provide a detailed performance benchmark of the different configurations, also considering the current state-of-the-art approaches based on S-1 STS and shallow learners. Our findings showed that, by exploiting the powerful learning capabilities of CNNs, we outperformed the STS-based approaches published in the literature and significantly reduced the computational load. Indeed, when considering the entire stack of Sentinel-1 data at a 6 d revisit time, we were able to maintain the overall accuracy and F₁-score well above 90% and reduce the computational time by more than 50% with respect to state-of-the-art approaches, by avoiding the generation of handcrafted feature maps. Moreover, we achieved satisfactory results even when only S-1 InSAR acquisitions with a revisit time of 12 d or more were used, setting the groundwork for an effective and fast monitoring of tropical forests at a global scale. Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

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25 pages, 9396 KiB

Open AccessArticle

Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping

by Ignacio Borlaf-Mena, Ovidiu Badea and Mihai Andrei Tanase

Remote Sens. 2021, 13(23), 4814; https://doi.org/10.3390/rs13234814 - 27 Nov 2021

Cited by 10 | Viewed by 2799

Abstract

This study tested the ability of Sentinel-1 C-band to separate forest from other common land use classes (i.e., urban, low vegetation and water) at two different sites. The first site is characterized by temperate forests and rough terrain while the second by tropical forest and near-flat terrain. We trained a support vector machine classifier using increasing feature sets starting from annual backscatter statistics (average, standard deviation) and adding long-term coherence (i.e., coherence estimate for two acquisitions with a large time difference), as well as short-term (six to twelve days) coherence statistics from annual time series. Classification accuracies using all feature sets was high (>92% overall accuracy). For temperate forests the overall accuracy improved by up to 5% when coherence features were added: long-term coherence reduced misclassification of forest as urban, whereas short-term coherence statistics reduced the misclassification of low vegetation as forest. Classification accuracy for tropical forests showed little differences across feature sets, as the annual backscatter statistics sufficed to separate forest from low vegetation, the other dominant land cover. Our results show the importance of coherence for forest classification over rough terrain, where forest omission error was reduced up to 11%. Full article

(This article belongs to the Special Issue SAR for Forest Mapping II)

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