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Vegetation Dynamics and Forest Structure Monitoring Based on Multisensor Approaches

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 34781

Special Issue Editors


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Guest Editor
1. Centre for Landscape and Climate Research (CLCR), University of Leicester, & National Centre for Earth Observation (NCEO), Leicester, UK
2. School of Geography, Geology and the Environment, University of Leicester, Leicester, UK
Interests: quantitative understanding of climate change; land use change impacts on ecosystem services; spatial-temporal patterns and processes; forest aboveground biomass; logging detection; fire monitoring; mapping tree disease outbreaks
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Rd, Manchester M13 9PL, UK
Interests: tropical forests; forest ecology; forest structure; vegetation dynamics; geomorphometry; aboveground biomass estimations; multisensory approach; SAR; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Centre for Landscape and Climate Research (CLCR), University of Leicester, Leicester, UK
Interests: forest structure and ecology; forest monitoring; forest degradation and disturbance; earth observation particularly using multi-frequency SAR and LiDAR data

Special Issue Information

Dear Colleagues,

The recent increase in the availability of data from different optical and Synthetic Aperture Radar (SAR) sensors has led to the capability of monitoring vegetation dynamics and forest structure in almost near-real-time. Recent approaches have demonstrated that the combination of multiple sensors can considerably improve the accuracy of forest change detection. SAR sensors provide data that can fill in any gaps in a time series of optical data related to cloud cover. Despite the increasing evidence of the benefits of combining data from optical and SAR sensors and even combining different SAR frequencies and polarisations, there is a paucity of studies using such methods on larger scales or in a time series approach. This special issue focuses on the combination of available long-term datasets and newly released datasets for improving the understanding of forest dynamics and increases the accuracy for monitoring their changes. We invite papers describing approaches that combine data from different SAR, optical and LiDAR sensors to tackle the current environmental challenges.

Prof. Dr. Heiko Balzter
Dr. Polyanna da Conceição Bispo
Dr. Ana Maria Pacheco-Pascagaza
Guest Editors

Manuscript Submission Information

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Keywords

  • Vegetation dynamics
  • Multi-sensor fusion
  • Machine Learning
  • Forest structure
  • Environmental monitoring
  • Forest Modelling

Published Papers (8 papers)

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Research

22 pages, 4150 KiB  
Article
Assessing Drought Response in the Southwestern Amazon Forest by Remote Sensing and In Situ Measurements
by Ranieli Dos Anjos De Souza, Valdir Moura, Rennan Andres Paloschi, Renata Gonçalves Aguiar, Alberto Dresch Webler and Laura De Simone Borma
Remote Sens. 2022, 14(7), 1733; https://doi.org/10.3390/rs14071733 - 04 Apr 2022
Cited by 5 | Viewed by 2445
Abstract
Long-term meteorological analyzes suggest an increase in air temperature and a decrease in rainfall over the Amazon biome. The effect of these climate changes on the forest remains unresolved, because field observations on functional traits are sparse in time and space, and the [...] Read more.
Long-term meteorological analyzes suggest an increase in air temperature and a decrease in rainfall over the Amazon biome. The effect of these climate changes on the forest remains unresolved, because field observations on functional traits are sparse in time and space, and the results from remote sensing analyses are divergent. Then, we analyzed the drought response in a ‘terra firme’ forest fragment in the southwestern Amazonia, during an extreme drought event influenced by ENSO episode (2015/2017), focusing on stem growth, litter production, functional traits and forest canopy dynamics. We use the Moderate Resolution Imaging Spectroradiometer (MODIS), corrected by Multi-Angle Implementation of Atmospheric Correction (MAIAC) to generate the enhanced vegetation index (EVI) and green chromatic coordinate (Gcc) vegetation indices. We monitor stem growth and measure the functional traits of trees in situ, such as the potential at which the plant loses 50% of hydraulic conductivity (P50), turgor loss point (πTLP), hydraulic safety margin (HSM) and isohydricity. Our results suggest that: (a) during the dry season, there is a smooth reduction in EVI values (browning) and an increase in the wet season (greening); (b) in the dry season, leaf flush occurs, when the water table still has a quota at the limit of the root zone; (c) the forest showed moderate resistance to drought, with water as the primary limiting factor, and the thickest trees were the most resistant; and (d) a decline in stem growth post-El-Niño 2015/2016 was observed, suggesting that the persistence of negative rainfall anomalies may be as critical to the forest as the drought episode itself. Full article
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20 pages, 7098 KiB  
Article
Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data
by Aline Pontes-Lopes, Ricardo Dalagnol, Andeise Cerqueira Dutra, Camila Valéria de Jesus Silva, Paulo Maurício Lima de Alencastro Graça and Luiz Eduardo de Oliveira e Cruz de Aragão
Remote Sens. 2022, 14(7), 1545; https://doi.org/10.3390/rs14071545 - 23 Mar 2022
Cited by 10 | Viewed by 3026
Abstract
Fire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal [...] Read more.
Fire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal changes in forest aboveground biomass (AGB), measured in permanent plots, and in traditional spectral indices derived from Landsat-8 images. We tested if the spectral indices can improve Random Forest (RF) models of post-fire AGB losses based on pre-fire AGB, proxied by AGB data from immediately after a fire. The delta normalized burned ratio, non-photosynthetic vegetation, and green vegetation (ΔNBR, ΔNPV, and ΔGV, respectively), relative to pre-fire data, were good proxies of canopy damage through tree mortality, even though small and medium trees were the most affected tree size. Among all tested predictors, pre-fire AGB had the highest RF model importance to predicting AGB within one year after fire. However, spectral indices significantly improved AGB loss estimates by 24% and model accuracy by 16% within two years after a fire, with ΔGV as the most important predictor, followed by ΔNBR and ΔNPV. Up to two years after a fire, this study indicates the potential of structural and spectral-based spatial data for integrating complex post-fire ecological processes and improving carbon emission estimates by forest fires in the Amazon. Full article
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21 pages, 6251 KiB  
Article
Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests
by Ana María Pacheco-Pascagaza, Yaqing Gou, Valentin Louis, John F. Roberts, Pedro Rodríguez-Veiga, Polyanna da Conceição Bispo, Fernando D. B. Espírito-Santo, Ciaran Robb, Caroline Upton, Gustavo Galindo, Edersson Cabrera, Indira Paola Pachón Cendales, Miguel Angel Castillo Santiago, Oswaldo Carrillo Negrete, Carmen Meneses, Marco Iñiguez and Heiko Balzter
Remote Sens. 2022, 14(3), 707; https://doi.org/10.3390/rs14030707 - 02 Feb 2022
Cited by 14 | Viewed by 7544
Abstract
The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cover loss enables forest landowners, government [...] Read more.
The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cover loss enables forest landowners, government agencies and local communities to monitor natural and anthropogenic disturbances in a much timelier fashion than the thematic maps that are released every year. NRT deforestation alerts enable the establishment of more up-to-date forest inventories and rapid responses to unlicensed logging. The Copernicus Sentinel-2 satellites provide operational Earth observation (EO) data from multi-spectral optical/near-infrared wavelengths every five days at a global scale and at 10 m resolution. The amount of acquired data requires cloud computing or high-performance computing for ongoing monitoring systems and an automated system for processing, analyzing and delivering the information promptly. Here, we present a Sentinel-2-based NRT change detection system, assess its performance over two study sites, Manantlán in Mexico and Cartagena del Chairá in Colombia, and evaluate the forest changes that occurred in 2018. An independent validation with very high-resolution PlanetScope (~3 m) and RapidEye (~5 m) data suggests that the proposed NRT change detection system can accurately detect forest cover loss (> 87%), other vegetation loss (> 76%) and other vegetation gain (> 71%). Furthermore, the proposed NRT change detection system is designed to be attuned using in situ data. Therefore, it is scalable to larger regions, entire countries and even continents. Full article
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22 pages, 6181 KiB  
Article
A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images
by Tahisa Neitzel Kuck, Edson Eyji Sano, Polyanna da Conceição Bispo, Elcio Hideiti Shiguemori, Paulo Fernando Ferreira Silva Filho and Eraldo Aparecido Trondoli Matricardi
Remote Sens. 2021, 13(17), 3341; https://doi.org/10.3390/rs13173341 - 24 Aug 2021
Cited by 10 | Viewed by 3241
Abstract
The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover [...] Read more.
The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon. Full article
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32 pages, 26239 KiB  
Article
Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau
by Jessica da Silva Costa, Veraldo Liesenberg, Marcos Benedito Schimalski, Raquel Valério de Sousa, Leonardo Josoé Biffi, Alessandra Rodrigues Gomes, Sílvio Luís Rafaeli Neto, Edson Mitishita and Polyanna da Conceição Bispo
Remote Sens. 2021, 13(2), 229; https://doi.org/10.3390/rs13020229 - 11 Jan 2021
Cited by 9 | Viewed by 3689
Abstract
The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This [...] Read more.
The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This study’s main goal was to characterize the polarimetric attributes of the experimental quad-polarization acquisition mode of the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR-2) for mapping seven land cover classes. The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm. The results showed that the intensity backscattering alone reached an overall classification accuracy of 37.48% and a Kappa index of 0.26. Interestingly, the addition of polarimetric features increased to 71.35% and 0.66, respectively. It shows that the use of polarimetric decomposition features was relatively efficient in discriminating land cover classes. SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test. However, the addition of ALOS/PALSAR-2 derived features to SENTINEL-2A slightly improved accuracy and was marginally significant at a 95% confidence level only when all features were considered. Possible implications for that performance are the accumulated precipitation prior to SAR data acquisition, which coincides with the rainy season period. The experimental quad-polarization mode of ALOS/PALSAR- 2 shall be evaluated in the near future over different seasonal conditions to confirm results. Alternatively, further studies are then suggested by focusing on additional features derived from SAR data such as texture and interferometric coherence to increase classification accuracy. These measures would be an interesting data source for monitoring specific land cover classes such as the threatened grasslands and wetlands during periods of frequent cloud coverage. Future investigations could also address multitemporal approaches employing either single or multifrequency SAR. Full article
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21 pages, 8899 KiB  
Article
Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons
by Joana Borges, Thomas P. Higginbottom, Elias Symeonakis and Martin Jones
Remote Sens. 2020, 12(23), 3862; https://doi.org/10.3390/rs12233862 - 25 Nov 2020
Cited by 14 | Viewed by 4815
Abstract
Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and [...] Read more.
Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and the composition of savannah environments is challenging but essential in order to improve monitoring capabilities, prevent biodiversity loss and ensure the provision of ecosystem services. Here, we tested combinations of Sentinel-1 and Sentinel-2 data from three different seasons to optimise land cover mapping, focusing in the Ngorongoro Conservation Area (NCA) in Tanzania. The NCA has a bimodal rainfall pattern and is composed of a combination savannah and woodland landscapes. The best performing model achieved an overall accuracy of 86.3 ± 1.5% and included a combination of Sentinel-1 and 2 from the dry and short-dry seasons. Our results show that the optical models outperform their radar counterparts, the combination of multisensor data improves the overall accuracy in all scenarios and this is particularly advantageous in single-season models. Regarding the effect of season, models that included the short-dry season outperform the dry and wet season models, as this season is able to provide cloud free data and is wet enough to allow for the distinction between woody and herbaceous vegetation. Additionally, the combination of more than one season is beneficial for the classification, specifically if it includes the dry or the short-dry season. Combining several seasons is, overall, more beneficial for single-sensor data; however, the accuracies varied with land cover. In summary, the combination of several seasons and sensors provides a more accurate classification, but the target vegetation types should be taken into consideration. Full article
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30 pages, 10269 KiB  
Article
Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data
by Natalia C. Wiederkehr, Fabio F. Gama, Paulo B. N. Castro, Polyanna da Conceição Bispo, Heiko Balzter, Edson E. Sano, Veraldo Liesenberg, João R. Santos and José C. Mura
Remote Sens. 2020, 12(21), 3512; https://doi.org/10.3390/rs12213512 - 26 Oct 2020
Cited by 9 | Viewed by 4679
Abstract
We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover [...] Read more.
We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude–Pottier, van Zyl, Freeman–Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover. Full article
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20 pages, 3198 KiB  
Article
Airborne Lidar Sampling Pivotal for Accurate Regional AGB Predictions from Multispectral Images in Forest-Savanna Landscapes
by Le Bienfaiteur T. Sagang, Pierre Ploton, Bonaventure Sonké, Hervé Poilvé, Pierre Couteron and Nicolas Barbier
Remote Sens. 2020, 12(10), 1637; https://doi.org/10.3390/rs12101637 - 20 May 2020
Cited by 7 | Viewed by 3037
Abstract
Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to [...] Read more.
Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to radically increase sampling and enhance model calibration. Here we tested the potential of using ALS data for upscaling vegetation aboveground biomass (AGB) from field plots to a forest-savanna transitional landscape in the Guineo–Congolian region in Cameroon, using either a design-based approach or a model-based approach leveraging multispectral satellite imagery. Two sets of reference data were used: (1) AGB values collected from 62 0.16-ha plots distributed both in forests and savannas; and (2) an AGB map generated form ALS data. In the model-based approach, we trained Random Forest models using predictors from recent sensors of varying spectral and spatial resolutions (Spot 6/7, Landsat 8, and Sentinel 2), along with biophysical predictors derived after pre-processing into the Overland processing chain, following a forward variable selection procedure with a spatial 4-folds cross validation. The models calibrated with field plots lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. Decomposing bias along the AGB density range, we show that multispectral images can (in some specific cases) be used for unbiased prediction at landscape scale on the basis of ALS-calibrated statistical models. However, our results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixelwise predictions, because of large relative RMSPE, especially above (200–250 t/ha). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples. Full article
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