Prediction of forest aboveground biomass using multitemporal multispectral remote sensing data

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal informatio

Deep neural networks with transfer learning for forest variable estimation using sentinel-2 imagery in boreal forest

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (|BIAS%| = 0.8%). We found 3×3 pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (|BIAS%| = 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept

Modelling Stand Variables of Beech Coppice Forest Using Spectral Sentinel-2A Data and the Machine Learning Approach

Background and Purpose: Coppice forests have a particular socio-economic and ecological role in forestry and environmental management. Their production sustainability and spatial stability become imperative for forestry sector as well as for local and global communities. Recently, integrated forest inventory and remotely sensed data analysed with non-parametrical statistical methods have enabled more detailed insight into forest structural characteristics. The aim of this research was to estimate forest attributes of beech coppice forest stands in the Sarajevo Canton through the integration of inventory and Sentinel S2A satellite data using machine learning methods. Materials and Methods: Basal area, mean stand diameter, growing stock and total volume data were determined from the forest inventory designed for represented stands of coppice forests. Spectral data were collected from bands of Sentinel S2A satellite image, vegetation indices (difference, normalized difference and ratio vegetation index) and biophysical variables (fraction of absorbed photosynthetically active radiation, leaf area index, fraction of vegetation cover, chlorophyll content in the leaf and canopy water content). Machine learning rule-based M5 model tree (M5P) and random forest (RF) methods were used for forest attribute estimation. Predictor subset selection was based on wrapping assuming M5P and RF learning schemes. Models were developed on training data subsets (402 sample plots) and evaluations were performed on validation data subsets (207 sample plots). Performance of the models was evaluated by the percentage of the root mean squared error over the mean value (rRMSE) and the square of the correlation coefficient between the observed and estimated stand variables. Results and Conclusions: Predictor subset selection resulted in a varied number of predictors for forest attributes and methods with their larger contribution in RF (between 8 and 11). Spectral biophysical variables dominated in subsets. The RF resulted in smaller errors for training sets for all attributes than M5P, while both methods delivered very high errors for validation sets (rRMSE above 50%). The lowest rRMSE of 50% was obtained for stand basal area. The observed variability explained by the M5P and RF models in training subsets was about 30% and 95% respectively, but those values were lower in test subsets (below 12%) but still significant. Differences of the sample and modelled forest attribute means were not significant, while modelled variability for all forest attributes was significantly lower (p<0.01). It seems that additional information is needed to increase prediction accuracy, so stand information (management classes, site class, soil type, canopy closure and others), new sampling strategy and new spectral products could be integrated and examined in further more complex modelling of forest attributes

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data

Tree Species Classification : Analyzing Multitemporal Satellite Imagery and Multispectral Airborne Laser Scanning Data

Tree species composition of forests affects the whole ecosystem and is part of the information needed for an efficient planning of forest management. This thesis explores how recent developments in remote sensing can provide more accurate tree species mapping. I try to answer the question of how the properties of these data can be used to derive more information on tree species. Out of the four papers in this thesis, two papers examine how multitemporal satellite imagery from the Sentinel-2 mission can be of use, and the other two papers investigate what properties of multispectral airborne laser scanning (MSALS) data that contain the most information on tree species. We applied a Bayesian method to multitemporal satellite imagery for tree species classification of pixels in the hemiboreal forest of Remningstorp in southwestern Sweden. The Bayesian method was applied to 142 Sentinel-2 images, and to a subset of images ranked and selected by the separability of tree species classes. The method was also compared to a Random Forest classifier for 45 Sentinel-2 images of boreal forest in mid-Sweden. The Bayesian method performed better for homogeneous tree species classes, while Random Forest performed better for heterogeneous classes. Data from two MSALS systems were used for classifying the tree species of individual trees. Optech Titan-X data were used to classify free-standing trees of nine species in Remningstorp. By using Riegl VQ-1560i-DW data, we performed a tree species classification in a more operational setting for three tree species in closed-canopy hemiboreal forest in Asa in southern Sweden. Multispectral intensity features provided a great improvement in classification accuracy in both cases, compared to using only structural features or combining them with monospectral intensity features. For Optech Titan-X, the green wavelength performed poorly, but for Riegl VQ-1560i-DW, the green wavelength provided the most information for separability, especially for birch (Betula spp.). There are two main conclusions in this thesis. The first is that Bayesian methods that updates probabilities as new observations are made provides an opportunity to automate the addition of satellite images for an updated classification. The second is that MSALS data provides more information on tree species than monospectral data and tree crown structure do, with the most information coming from the upper parts of the canopy. Nonetheless, what wavelengths of light that contribute most to tree species classification accuracy is highly dependent on what MSALS system that is used

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Resource Communication : ForestAz - Using Google Earth Engine and Sentinel data for forest monitoring in the Azores Islands (Portugal)

AIM OF STUDY: ForestAz application was developed to (i) map Azorean forest areas accurately through semiautomatic supervised classification; (ii) assess vegetation condition (e.g., greenness and moisture) by computing and comparing several spectral indices; and (iii) quantitatively evaluate the stocks and dynamics of aboveground carbon (AGC) sequestrated by Azorean forest areas. AREA OF STUDY: ForestAz focuses primarily on the Public Forest Perimeter of S. Miguel Island (Archipelago of the Azores, Portugal), with about 3808 hectares. MATERIAL AND METHODS: ForestAz was developed with Javascript for the Google Earth Engine platform, relying solely on open satellite remote sensing data, as Copernicus Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (multispectral). MAIN RESULTS: By accurately mapping S. Miguel island forest areas using a detailed species-based vegetation mapping approach; by allowing frequent and periodic monitoring of vegetation condition; and by quantitatively assessing the stocks and dynamics of AGC by these forest areas, this remote sensing-based application may constitute a robust and low-cost operational tool able to support local/regional decision-making on forest planning and management. RESEARCH HIGHLIGHTS: This collaborative initiative between the University of the Azores and the Azores Regional Authority in Forest Affairs was selected to be one of the 99 user stories by local and regional authorities described in the catalog edited by the European Commission, the Network of European Regions Using Space Technologies (NEREUS Association), and the European Space Agency (ESA).info:eu-repo/semantics/publishedVersio

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

The global tree carrying capacity (keynote)

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