Special Issue "Advances in Remote Sensing for Global Forest Monitoring"

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 August 2020).

Special Issue Editors

Dr. Erkki Tomppo
Website
Guest Editor
Aalto University, Department of Electronics and Nanoengineering, Finland
University of Helsinki, Department of Forest Sciences, Finland
Interests: forest inventory, forest remote sensing, statistical methods
Special Issues and Collections in MDPI journals
Dr. Jaan Praks
Website
Guest Editor
Aalto University, Department of Electronics and Nanoengineering, Finland
Interests: microwave Earth Observation, SAR remote sensing, PolSAR/InSAR/Pol-InSAR, EM modeling of forests, hyperspectral remote sensing, nanosatellites
Special Issues and Collections in MDPI journals
Prof. Dr. Guangxing Wang
Website
Guest Editor
Southern Illinois University, Department of Geography and Environmental Resources
Interests: remote sensing; GIS; spatial statistics; natural and environmental resources; sampling design; environmental quality assessment; forest and city vegetation carbon sequestration; desertification trend monitoring; quality assessment and spatial uncertainty analysis
Special Issues and Collections in MDPI journals
Dr. Lars T. Waser
Website SciProfiles
Guest Editor
Swiss National Forest Inventory, Swiss Federal Research Institute WSL, Zuercherstr. 111, CH-8903 Birmensdorf, Switzerland
Interests: national forest inventory; spatially estimating forest parameters; tree species; growing stock; change detection; biodiversity; LiDAR; airborne and spaceborne; image-matching; canopy height model; land cover/land use change
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The need for temporarily, spatially, and thematically accurate information about forests is increasing because of their role in global carbon balance and sustanaibly social, economic, ecological, and cultural development. Some countries in the world have already had statistically sound forest inventories for 100 years, while others are still lacking, which makes the global information about forest statistics inaccurate.

Globally, forested areas decreased annually in the period of 2010–2015 by 0.08% (3.3 million hectares) (United Nations FAO 2016), although the increases occurred in some regions, for example, in East Asia, Western and Central Asia, Europe, Oceania, and North and Central America, which slowed down the global decrease. Based on the reports from the countries with full-time series, on the other hand, the growing stock has been estimated to increase annually by 0.17% (710 million cubic metres), while the above ground biomass decreased annually by 0.02% (92 million tons). 

Remote sensing has been utilized for national forest inventories for many decades, using mainly airborne, but increasingly, spaceborne remote sensing. Remotely sensed data have also been used at global level by FAO for the purpose of comparison and as complementary information, since 1980. Some institutes have attempted to conduct global forest inventories, sometimes providing contradicting information. This implies that remote sensing-based approaches are vulnerable for misclassification and the inaccurate estimation of forest parameters and changes, such as the recognition of temporarily un-stocked forests, forest degradation, and species composition, as well as in strictly following the globally adopted definitions.

The new active remote sensing technology, especially SAR, which is becoming increasingly available, provides new opportunities for large area and global forest inventory, and enough monitoring frequently, in a cost-efficient way. Technically and statistically sound methods are still being developed. This Special Issue invites research papers describing state-of-the-art in the field of remote sensing for forest parameter estimation and change monitoring at national, continental, or global scales. The topics could be, but are not limited to, the following:

  • Large area forest inentory and monitoring methods
  • Forest area and change estimation with multi-source data
  • Forest parameter estimation with multi-source data
  • Spatial sampling methods and sampling designs
  • Biomass estimation
  • Biodiversity
  • Forest degradation
  • Approaches for assessing uncertainties when using statistically sound inventory methods and multi-source data
  • Use of time series with SAR and optical remote sensing data
  • SAR interferometry and polarimetry

Prof. Dr. Erkki Tomppo
Assist. Prof. Dr. Jaan Praks
Prof. Dr. Guangxing Wang
Dr. Lars T. Waser
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 2200 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

  • global forest
  • large area estimation
  • forest inventory
  • forest monitoring
  • multi-source remote sensing
  • Sentinel and Radarsat
  • SAR remote sensing: PolSAR, InSAR, PolinSAR
  • error and uncertainty
  • time series
  • open source

Published Papers (7 papers)

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Research

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Open AccessArticle
Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China
Remote Sens. 2020, 12(19), 3122; https://doi.org/10.3390/rs12193122 (registering DOI) - 23 Sep 2020
Abstract
Leaf area is a key parameter underpinning ecosystem carbon, water and energy exchanges via photosynthesis, transpiration and absorption of radiation, from local to global scales. Satellite-based Earth Observation (EO) can provide estimates of leaf area index (LAI) with global coverage and high temporal [...] Read more.
Leaf area is a key parameter underpinning ecosystem carbon, water and energy exchanges via photosynthesis, transpiration and absorption of radiation, from local to global scales. Satellite-based Earth Observation (EO) can provide estimates of leaf area index (LAI) with global coverage and high temporal frequency. However, the error and bias contained within these EO products and their variation in time and across spatial resolutions remain poorly understood. Here, we used nearly 8000 in situ measurements of LAI from six forest environments in southern China to evaluate the magnitude, uncertainty, and dynamics of three widely used EO LAI products. The finer spatial resolution GEOV3 PROBA-V 300 m LAI product best estimates the observed LAI from a multi-site dataset (R2 = 0.45, bias = −0.54 m2 m−2, RMSE = 1.21 m2 m−2) and importantly captures canopy dynamics well, including the amplitude and phase. The GEOV2 PROBA-V 1 km LAI product performed the next best (R2 = 0.36, bias = −2.04 m2 m−2, RMSE = 2.32 m2 m−2) followed by MODIS 500 m LAI (R2 = 0.20, bias = −1.47 m2 m−2, RMSE = 2.29 m2 m−2). The MODIS 500 m product did not capture the temporal dynamics observed in situ across southern China. The uncertainties estimated by each of the EO products are substantially smaller (3–5 times) than the observed bias for EO products against in situ measurements. Thus, reported product uncertainties are substantially underestimated and do not fully account for their total uncertainty. Overall, our analysis indicates that both the retrieval algorithm and spatial resolution play an important role in accurately estimating LAI for the dense canopy forests in Southern China. When constraining models of the carbon cycle and other ecosystem processes are run, studies should assume that current EO product LAI uncertainty estimates underestimate their true uncertainty value. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)
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Open AccessArticle
Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry
Remote Sens. 2020, 12(18), 2884; https://doi.org/10.3390/rs12182884 - 06 Sep 2020
Abstract
Forest canopy height is one of the most important spatial characteristics for forest resource inventories and forest ecosystem modeling. Light detection and ranging (LiDAR) can be used to accurately detect canopy surface and terrain information from the backscattering signals of laser pulses, while [...] Read more.
Forest canopy height is one of the most important spatial characteristics for forest resource inventories and forest ecosystem modeling. Light detection and ranging (LiDAR) can be used to accurately detect canopy surface and terrain information from the backscattering signals of laser pulses, while photogrammetry tends to accurately depict the canopy surface envelope. The spatial differences between the canopy surfaces estimated by LiDAR and photogrammetry have not been investigated in depth. Thus, this study aims to assess LiDAR and photogrammetry point clouds and analyze the spatial differences in canopy heights. The study site is located in the Jigongshan National Nature Reserve of Henan Province, Central China. Six data sets, including one LiDAR data set and five photogrammetry data sets captured from an unmanned aerial vehicle (UAV), were used to estimate the forest canopy heights. Three spatial distribution descriptors, namely, the effective cell ratio (ECR), point cloud homogeneity (PCH) and point cloud redundancy (PCR), were developed to assess the LiDAR and photogrammetry point clouds in the grid. The ordinary neighbor (ON) and constrained neighbor (CN) interpolation algorithms were used to fill void cells in digital surface models (DSMs) and canopy height models (CHMs). The CN algorithm could be used to distinguish small and large holes in the CHMs. The optimal spatial resolution was analyzed according to the ECR changes of DSMs or CHMs resulting from the CN algorithms. Large negative and positive variations were observed between the LiDAR and photogrammetry canopy heights. The stratified mean difference in canopy heights increased gradually from negative to positive when the canopy heights were greater than 3 m, which means that photogrammetry tends to overestimate low canopy heights and underestimate high canopy heights. The CN interpolation algorithm achieved smaller relative root mean square errors than the ON interpolation algorithm. This article provides an operational method for the spatial assessment of point clouds and suggests that the variations between LiDAR and photogrammetry CHMs should be considered when modeling forest parameters. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)
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Open AccessArticle
Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data
Remote Sens. 2020, 12(14), 2238; https://doi.org/10.3390/rs12142238 - 13 Jul 2020
Abstract
The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for [...] Read more.
The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for DBH and HCB through the ground-based methods is cumbersome and costly. The indirect method of getting such information is possible from remote sensing databases, which can be used to build DBH and HCB prediction models. The DBH and HCB of the same trees are significantly correlated, and so their inherent correlations need to be appropriately accounted for in the DBH and HCB models. However, all the existing DBH and HCB models, including models based on light detection and ranging (LiDAR) have ignored such correlations and thus failed to account for the compatibility of DBH and HCB estimates, in addition to disregarding measurement errors. To address these problems, we developed a compatible simultaneous equation system of DBH and HCB error-in-variable (EIV) models using LiDAR-derived data and ground-measurements for 510 Picea crassifolia Kom trees in northwest China. Four versatile algorithms, such as nonlinear seemingly unrelated regression (NSUR), two-stage least square (2SLS) regression, three-stage least square (3SLS) regression, and full information maximum likelihood (FIML) were evaluated for their estimating efficiencies and precisions for a simultaneous equation system of DBH and HCB EIV models. In addition, two other model structures, namely, nonlinear least squares with HCB estimation not based on the DBH (NLS and NBD) and nonlinear least squares with HCB estimation based on the DBH (NLS and BD) were also developed, and their fitting precisions with a simultaneous equation system compared. The leave-one-out cross-validation method was applied to evaluate all estimating algorithms and their resulting models. We found that only the simultaneous equation system could illustrate the effect of errors associated with the regressors on the response variables (DBH and HCB) and guaranteed the compatibility between the DBH and HCB models at an individual level. In addition, such an established system also effectively accounted for the inherent correlations between DBH with HCB. However, both the NLS and BD model and the NLS and NBD model did not show these properties. The precision of a simultaneous equation system developed using NSUR appeared the best among all the evaluated algorithms. Our equation system does not require the stand-level information as input, but it does require the information of tree height, crown width, and crown projection area, all of which can be readily derived from LiDAR imagery using the delineation algorithms and ground-based DBH measurements. Our results indicate that NSUR is a more reliable and quicker algorithm for developing DBH and HCB models using large scale LiDAR-based datasets. The novelty of this study is that the compatibility problem of the DBH model and the HCB EIV model was properly addressed, and the potential algorithms were compared to choose the most suitable one (NSUR). The presented method and algorithm will be useful for establishing similar compatible equation systems of tree DBH and HCB EIV models for other tree species. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)
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Open AccessArticle
Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods—A Case Study from Dak Nong, Vietnam
Remote Sens. 2020, 12(9), 1367; https://doi.org/10.3390/rs12091367 - 26 Apr 2020
Cited by 1
Abstract
Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to [...] Read more.
Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km2 study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km2 (1% of the study area) to 2200 km2 (34% of the study area) with greater uncertainties for smaller classes. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)
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Open AccessEditor’s ChoiceArticle
Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion
Remote Sens. 2020, 12(2), 302; https://doi.org/10.3390/rs12020302 - 17 Jan 2020
Cited by 4
Abstract
The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build [...] Read more.
The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)
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Open AccessEditor’s ChoiceArticle
Estimation of Changes of Forest Structural Attributes at Three Different Spatial Aggregation Levels in Northern California using Multitemporal LiDAR
Remote Sens. 2019, 11(8), 923; https://doi.org/10.3390/rs11080923 - 16 Apr 2019
Cited by 8
Abstract
Accurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimation of structural changes, especially for biomass, is also key [...] Read more.
Accurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimation of structural changes, especially for biomass, is also key to quantify greenhouse gas (GHG) emissions/sequestration. We compared two different modeling strategies to estimate changes in V, BA and B, at three different spatial aggregation levels using auxiliary information from two light detection and ranging (LiDAR) flights. The study area is Blacks Mountains Experimental Forest, a ponderosa pine dominated forest in Northern California for which two LiDAR acquisitions separated by six years were available. Analyzed strategies consisted of (1) directly modeling the observed changes as a function of the LiDAR auxiliary information ( δ -modeling method) and (2) modeling V, BA and B at two different points in time, including a term to account for the temporal correlation, and then computing the changes as the difference between the predicted values of V, BA and B for time two and time one. We analyzed predictions and measures of uncertainty at three different level of aggregation (i.e., pixels, stands or compartments and the entire study area). Results showed that changes were very weakly correlated with the LiDAR auxiliary information. Both modeling alternatives provided similar results with a better performance of the δ -modeling for the entire study area; however, this method also showed some inconsistencies and seemed to be very prone to extrapolation problems. The y -modeling method, which seems to be less prone to extrapolation problems, allows obtaining more outputs that are flexible and can outperform the δ -modeling method at the stand level. The weak correlation between changes in structural attributes and LiDAR auxiliary information indicates that pixel-level maps have very large uncertainties and estimation of change clearly requires some degree of spatial aggregation; additionally, in similar environments, it might be necessary to increase the time lapse between LiDAR acquisitions to obtain reliable estimates of change. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)
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Review

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Open AccessReview
Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories
Remote Sens. 2020, 12(11), 1891; https://doi.org/10.3390/rs12111891 - 11 Jun 2020
Abstract
For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as [...] Read more.
For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)
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