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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 (15 June 2022) | Viewed by 14595

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Dr. Peter T. Wolter

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Guest Editor

Assistant Professor, Department of Natural Resource Ecology and Management, Iowa State University, Ames, IA 50011, USA
Interests: forest ecosystem monitoring; remote sensing; forest structure; forest fire
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Prof. Philip Townsend

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Guest Editor

Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: forest ecology; remote sensing; imaging spectroscopy; foliar biochemistry; plant metabolism and function
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

Satellite-based remote sensing has come a very long way since the launch of the first Earth-observing platform in 1959 (Explorer 6) and the transformational series of Landsat sensors beginning in July of 1972. By the end of 1973, efforts to quantify the biophysical properties of Earth’s surface via Landsat-1 (ERTS-1) sensor data, including forest composition and structure, had become a salient goal. Nearly 40 years later, we have the luxury of access to a plethora of data captured by a wide array of satellite-based assets, including high spatial resolution, high spectral resolution, high temporal resolution, optical, synthetic aperture radar, Lidar, and more. Combined with a rich, co-evolved history of novel processing techniques and strategies (recently revolutionized by cloud-based infrastructure), we are now poised to rapidly characterize, quantify, monitor, and understand the state and health of Earth’s forest ecosystems and resources better than ever before. Yet, burgeoning stressors on Earth’s forest ecosystems imposed by changing climate, population growth, and exotic insects and pathogens challenge us to develop novel, satellite-based solutions to our planet’s most vexing adaptation problems. Hence, this Special Issue welcomes articles dedicated to the advancement of satellite-based sensor systems, methodologies, and solutions that improve, expand, or automate forest structure, status, and health monitoring efforts.

Potential topics for this Special Issue include, but are not limited to, the following:

Pre-visual detection, identification, and assessment of biotic and abiotic forest stressors;
Forest productivity and ecosystem dynamics;
Response of forest ecosystems to climate change;
Vertically intelligent forest structure and moisture status assessment;
Novel data fusion analyses for forest dynamics monitoring and modeling;
Regional fire risk monitoring and modeling;
Cloud-based forest structure mapping.

Dr. Peter T. Wolter
Prof. Philip Townsend
Guest Editors

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Keywords

pre-visual detection
forest stressors
fire risk
vertical structure and moisture
forest productivity monitoring and modeling
satellite data fusion
understory structure
Google Earth Engine

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Published Papers (4 papers)

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Research

20 pages, 3294 KiB

Open AccessArticle

Landsat Data Based Prediction of Loblolly Pine Plantation Attributes in Western Gulf Region, USA

by Chongzhi Chen, Ke Wang, Luming Fang, Jason Grogan, Clinton Talmage and Yuhui Weng

Remote Sens. 2022, 14(19), 4702; https://doi.org/10.3390/rs14194702 - 21 Sep 2022

Cited by 2 | Viewed by 1574

Abstract

The suitability of using Landsat sensor variables to predict key stand attributes, including stand average dominant/codominant tree height (HT), mean diameter at breast height (DBH), the number of trees per hectare (NT), basal area per hectare (BA), and stand density index (SDI), of intensively managed loblolly pine plantations in the Western Gulf Region at the plot/stand level was assessed. In total, thirty Landsat sensor variables including six original bands, three vegetation indices, three Tasseled Cap transformed indices, and eighteen texture measure variables were used as predictors. Field data of 125 permanent plots located across east Texas and western Louisiana were used as reference data. Individual trees of those plots were measured at plot establishment (referred to as the first cycle measurement; average about 4.5 years old) and remeasured in three-year intervals (the second cycle measurement at approximately seven years old and the third cycle measurement at approximately 10 years old). Thus, field reference data represent stand development from open- (first cycle) to closed-canopy (third cycle). Models to predict stand HT, DBH, NT, BA, and SDI were developed by cycle using multiple linear regression (MLR) and also random forests (RF) methods. Results indicated that the first cycle stands HT, DBH, BA, and SDI were well predicted using the Landsat sensor variables with R² > 0.7 and low RMSEs. These relationships weakened with stand age, although still moderate with R² being around 0.45 for the second cycle measurement and became practically useless (R² < 0.30) for the third cycle measurement. For NT, no meaningful models were achieved regardless of the measurement cycle. The MLR and RF models were comparable in accuracy and had similar key predictors. Overall, the shortwave infrared bands, red band, and wetness index were the most important predictors, but their dominance declined with the cycle. Texture measure variables were relatively less important but a trend of increasing their importance with cycle was noted. Results show promise for operationally predicting stand variables for young pine plantations, an age class that typically presents significant challenges using conventional forest measurement methodologies. Potential methods to further improve model accuracy and how to use the results within the context of pine plantation management planning in the region were discussed. Full article

(This article belongs to the Special Issue Satellite-Based Forest Structure Mapping)

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19 pages, 4863 KiB

Open AccessArticle

Impact of Training Set Configurations for Differentiating Plantation Forest Genera with Sentinel-2 Imagery and Machine Learning

by Caley Higgs and Adriaan van Niekerk

Remote Sens. 2022, 14(16), 3992; https://doi.org/10.3390/rs14163992 - 16 Aug 2022

Cited by 2 | Viewed by 1917

Abstract

Forest plantations in South Africa impose genus-specific demands on limited soil moisture. Hence, plantation composition and distribution mapping is critical for water conservation planning. Genus maps are used to quantify the impact of post-harvest genus-exchange activities in the forestry sector. Collecting genus data using in situ methods is costly and time-consuming, especially when performed at regional or national scales. Although remotely sensed data and machine learning show potential for mapping genera at regional scales, the efficacy of such methods is highly dependent on the size and quality of the training data used to build the models. However, it is not known what sampling scheme (e.g., sample size, proportion per genus, and spatial distribution) is most effective to map forest genera over large and complex areas. Using Sentinel-2 imagery as inputs, this study evaluated the effects of different sampling strategies (e.g., even, uneven, and area-proportionate) for training the random forests machine learning classifier to differentiate between Acacia, Eucalyptus, and Pinus trees in South Africa. Sample size (s) was related to the number of input features (n) to better understand the potential impact of sample sparseness. The results show that an even sample with maximum size (100%, s~91n) produced the highest overall accuracy (76.3%). Although larger training set sizes (s > n) resulted in higher OAs, a saturation point was reached at s~64n. Full article

(This article belongs to the Special Issue Satellite-Based Forest Structure Mapping)

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15 pages, 2973 KiB

Open AccessArticle

Exploring the Relationship between Forest Canopy Height and Canopy Density from Spaceborne LiDAR Observations

by Heather Kay, Maurizio Santoro, Oliver Cartus, Pete Bunting and Richard Lucas

Remote Sens. 2021, 13(24), 4961; https://doi.org/10.3390/rs13244961 - 7 Dec 2021

Cited by 4 | Viewed by 5862

Abstract

Forest structure is a useful proxy for carbon stocks, ecosystem function and species diversity, but it is not well characterised globally. However, Earth observing sensors, operating in various modes, can provide information on different components of forests enabling improved understanding of their structure and variations thereof. The Ice, Cloud and Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS), providing LiDAR footprints from 2003 to 2009 with close to global coverage, can be used to capture elements of forest structure. Here, we evaluate a simple allometric model that relates global forest canopy height (RH100) and canopy density measurements to explain spatial patterns of forest structural properties. The GLA14 data product (version 34) was applied across subdivisions of the World Wildlife Federation ecoregions and their statistical properties were investigated. The allometric model was found to correspond to the ICESat GLAS metrics (median mean squared error, MSE: 0.028; inter-quartile range of MSE: 0.022–0.035). The relationship between canopy height and density was found to vary across biomes, realms and ecoregions, with denser forest regions displaying a greater increase in canopy density values with canopy height, compared to sparser or temperate forests. Furthermore, the single parameter of the allometric model corresponded with the maximum canopy density and maximum height values across the globe. The combination of the single parameter of the allometric model, maximum canopy density and maximum canopy height values have potential application in frameworks that target the retrieval of above-ground biomass and can inform on both species and niche diversity, highlighting areas for conservation, and potentially enabling the characterisation of biophysical drivers of forest structure. Full article

(This article belongs to the Special Issue Satellite-Based Forest Structure Mapping)

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30 pages, 18805 KiB

Open AccessArticle

Potential of Sentinel-1 C-Band Time Series to Derive Structural Parameters of Temperate Deciduous Forests

by Moritz Bruggisser, Wouter Dorigo, Alena Dostálová, Markus Hollaus, Claudio Navacchi, Stefan Schlaffer and Norbert Pfeifer

Remote Sens. 2021, 13(4), 798; https://doi.org/10.3390/rs13040798 - 22 Feb 2021

Cited by 22 | Viewed by 4249

Abstract

With the increasing occurrence of forest fires in the mid-latitudes and the alpine region, fire risk assessments become important in these regions. Fuel assessments involve the collection of information on forest structure as, e.g., the stand height or the stand density. The potential of airborne laser scanning (ALS) to provide accurate forest structure information has been demonstrated in several studies. Yet, flight acquisitions at the state level are carried out in intervals of typically five to ten years in Central Europe, which often makes the information outdated. The Sentinel-1 (S-1) synthetic aperture radar mission provides freely accessible earth observation (EO) data with short revisit times of 6 days. Forest structure information derived from this data source could, therefore, be used to update the respective ALS descriptors. In our study, we investigated the potential of S-1 time series to derive stand height and fractional cover, which is a measure of the stand density, over a temperate deciduous forest in Austria. A random forest (RF) model was used for this task, which was trained using ALS-derived forest structure parameters from 2018. The comparison of the estimated mean stand height from S-1 time series with the ALS derived stand height shows a root mean square error (RMSE) of 4.76 m and a bias of 0.09 m on a 100 m cell size, while fractional cover can be retrieved with an RMSE of 0.08 and a bias of 0.0. However, the predictions reveal a tendency to underestimate stand height and fractional cover for high-growing stands and dense areas, respectively. The stratified selection of the training set, which we investigated in order to achieve a more homogeneous distribution of the metrics for training, mitigates the underestimation tendency to some degree, yet, cannot fully eliminate it. We subsequently applied the trained model to S-1 time series of 2017 and 2019, respectively. The computed difference between the predictions suggests that large decreases in the forest height structure in this two-year interval become apparent from our RF-model, while inter-annual forest growth cannot be measured. The spatial patterns of the predicted forest height, however, are similar for both years (Pearson’s R = 0.89). Therefore, we consider that S-1 time series in combination with machine learning techniques can be applied for the derivation of forest structure information in an operational way. Full article

(This article belongs to the Special Issue Satellite-Based Forest Structure Mapping)

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