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The point cloud-based 3D model of forest helps to understand the growth and distribution pattern of trees, to improve the fine management of forestry resources. This paper describes the process of constructing a fine rubber forest growth model map based on 3D point clouds. Firstly, a multi-scale feature extraction module within the point cloud column is used to enhance the PointPillars learning capability. The Swin Transformer module is employed in the backbone to enrich the contextual semantics and acquire global features with the self-attention mechanism. All of the rubber trees are accurately identified and segmented to facilitate single-trunk localisation and feature extraction. Then, the structural parameters of the trunks calculated by RANSAC and IRTLS cylindrical fitting methods are compared separately. A growth model map of rubber trees is constructed. The experimental results show that the precision and recall of the target detection reach 0.9613 and 0.8754, respectively, better than the original network. The constructed rubber forest information map contains detailed and accurate trunk locations and key structural parameters, which are useful to optimise forestry resource management and guide the enhancement of mechanisation of rubber tapping. [ABSTRACT FROM AUTHOR]

Accurate and reliable informal settlement maps are fundamental decision-making tools for planning, and for expediting informed management of cities. However, extraction of spatial information for informal settlements has remained a mammoth task due to the spatial heterogeneity of urban landscape components, requiring complex analytical processes. To date, the use of Google Earth Engine platform (GEE), with cloud computing prowess, provides unique opportunities to map informal settlements with precision and enhanced accuracy. This paper leverages cloud-based computing techniques within GEE to integrate spectral and textural features for accurate extraction of the location and spatial extent of informal settlements in Durban, South Africa. The paper aims to investigate the potential and advantages of GEE's innovative image processing techniques to precisely depict morphologically varied informal settlements. Seven data input models derived from Sentinel 2A bands, band-derived texture metrics, and spectral indices were investigated through a random forest supervised protocol. The main objective was to explore the value of different data input combinations in accurately mapping informal settlements. The results revealed that the classification based on spectral bands + textural information yielded the highest informal settlement identification accuracy (94% F-score). The addition of spectral indices decreased mapping accuracy. Our results confirm that the highest spatial accuracy is achieved with the 'textural features' model, which yielded the lowest root-mean-square log error (0.51) and mean absolute percent error (0.36). Our approach highlights the capability of GEE's complex integrative data processing capabilities in extracting morphological variations of informal settlements in rugged and heterogeneous urban landscapes, with reliable accuracy. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanners, unmanned aerial LiDAR, and unmanned aerial photogrammetry are increasingly becoming the go-to methods for forest analysis and mapping. The three-dimensionality of the point clouds generated by these technologies is ideal for capturing the structural features of trees such as trunk diameter, canopy volume, and biomass. A prerequisite for extracting these features from point clouds is tree segmentation. This paper introduces an unsupervised method for segmenting individual trees from point clouds. Our novel, canopy-to-root, least-cost routing method segments trees in a single routine, accomplishing stem location and tree segmentation simultaneously without needing prior knowledge of tree stem locations. Testing on benchmark terrestrial-laser-scanned datasets shows that we achieve state-of-the-art performances in individual tree segmentation and stem-mapping accuracy on boreal and temperate hardwood forests regardless of forest complexity. To support mapping at scale, we test on unmanned aerial photogrammetric and LiDAR point clouds and achieve similar results. The proposed algorithm's independence from a specific data modality, along with its robust performance in simple and complex forest environments and accurate segmentation results, make it a promising step towards achieving reliable stem-mapping capabilities and, ultimately, towards building automatic forest inventory procedures. [ABSTRACT FROM AUTHOR]

Forest cover mapping is of paramount importance for environmental monitoring, biodiversity assessment, and forest resource management. In the realm of forest cover mapping, significant advancements have been made by leveraging fully supervised semantic segmentation models. However, the process of acquiring a substantial quantity of pixel-level labelled data is prone to time-consuming and labour-intensive procedures. To address this issue, this paper proposes a novel semi-supervised-learning-based semantic segmentation framework that leverages limited labelled and numerous unlabelled data, integrating multi-level perturbations and model ensembles. Our framework incorporates a multi-level perturbation module that integrates input-level, feature-level, and model-level perturbations. This module aids in effectively emphasising salient features from remote sensing (RS) images during different training stages and facilitates the stability of model learning, thereby effectively preventing overfitting. We also propose an ensemble-voting-based label generation strategy that enhances the reliability of model-generated labels, achieving smooth label predictions for challenging boundary regions. Additionally, we designed an adaptive loss function that dynamically adjusts the focus on poorly learned categories and dynamically adapts the attention towards labels generated during both the student and teacher stages. The proposed framework was comprehensively evaluated using two satellite RS datasets, showcasing its competitive performance in semi-supervised forest-cover-mapping scenarios. Notably, the method outperforms the fully supervised approach by 1–3% across diverse partitions, as quantified by metrics including mIoU, accuracy, and mPrecision. Furthermore, it exhibits superiority over other state-of-the-art semi-supervised methods. These results indicate the practical significance of our solution in various domains, including environmental monitoring, forest management, and conservation decision-making processes. [ABSTRACT FROM AUTHOR]

There are a variety of land cover products generated from remote-sensing images. However, misclassification errors in individual products and inconsistency among them undermine their utilities for research and other applications. While it is worth developing advanced pattern classifiers and utilizing the images of finer spatial, temporal, and/or spectral resolution for increased classification accuracy, it is also sensible to increase map classification accuracy through effective map fusion by exploiting complementarity among multi-source products over a study area. This paper presents a novel fusion method that works by weighting multiple source products based on their map-reference cover type transition probabilities, which are predicted using random forest for individual map pixels. The proposed method was tested and compared with three alternatives: consensus-based weighting, random forest, and locally modified Dempster–Shafer evidential reasoning, in a case study, over Shaanxi province, China. For this case study, three types of land cover products (GlobeLand30, FROM-GLC, and GLC_FCS30) of two nominal years (2010 and 2020) were used as the base maps for fusion. Reference sample data for model training and testing were collected following a robust stratified random sampling design that allows for augmenting reference data flexibly. Accuracy assessments show that overall accuracies (OAs) of fused land cover maps have been improved (1~9% in OAs), with the proposed method outperforming other methods by 2~8% in OAs. The proposed method does not need to have the base products' classification systems harmonized beforehand, thus being robust and highly recommendable for fusing land cover products. [ABSTRACT FROM AUTHOR]

One-third of Germany's land surface area is covered by forest (around 11.4 million hectares), and thus, it characterizes the landscape. The forest is a habitat for a large number of animal and plant species, a source of raw materials, important for climate protection, and a well-being refuge for people, to name just a few of its many functions. During the annual forest condition surveys, the crown condition of German forests is assessed on the basis of field samples at fixed locations, as the crown condition of forest trees is considered an important indicator of their vitality. Since the start of the surveys in 1984, the mean crown defoliation of all tree species has increased, now averaging about 25% for all tree species. Additionally, it shows a strong rise in the rate of dieback. In 2019, the most significant changes were observed. Due to the drastic changes in recent years, efforts are being made to assess the situation of the forest using different remote sensing methods. There are now a number of freely available products provided to the public, and more will follow as a result of numerous projects in the context of earth-observation (EO)-based monitoring and mapping of the forests in Germany. In 2020, the situation regarding the use of remote sensing for the German forest was already investigated in more detail. However, these results no longer reflect the current situation. The changes of the last 3 years are the content of this publication. For this study, 84 citable research publications were thoroughly analyzed and compared with the situation in 2020. As a major result, we found a shift in the research focus towards disturbance monitoring and a tendency to cover larger areas, including national-scale studies. In addition to the review of the scientific literature, we also reviewed current research projects and related products. In congruence to the recent developments in terms of publications in scientific journals, these projects and products reflect the need for comprehensive, timely, large-area, and complementary EO-based information around forests expressed in multiple political programs. With this review, we provide an update of previous work and link it to current research activities. We conclude that there are still gaps between the information needs of forest managers who usually rely on information from field perspectives and the EO-based information products. [ABSTRACT FROM AUTHOR]

Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions. Forest managers and public bodies need improved and cost-effective forest monitoring tools. Research studies have been carried out to assess the use of optical and radar images for producing forest height or biomass maps. The main limitations are the quantity, quality and representativeness of the reference data for model training. The Global Ecosystem Dynamics Investigation (GEDI) mission (full waveform LiDAR on board the International Space Station) has provided an unprecedented number of forest canopy height samples from 2019. These samples could be used to improve reference datasets. This paper aims to present and validate a method for estimating forest dominant height from open access optical and radar satellite images (Sentinel-1, Sentinel-2 and ALOS-2 PALSAR-2), and then to assess the use of GEDI samples to replace field height measurements in model calibration. Our approach combines satellite image features and dominant height measurements, or GEDI metrics, in a Support Vector Machine regression algorithm, with a feature selection process. The method is tested on mixed uneven-aged broadleaved and coniferous forests in France. Using dominant height measurements for model training, the cross-validation shows 7.3 to 11.6% relative Root Mean Square Error (RMSE) depending on the forest class. When using GEDI height metrics instead of field measurements for model training, errors increase to 12.8–16.7% relative RMSE. This level of error remains satisfactory; the use of GEDI could allow the production of dominant height maps on large areas with better sample representativeness. Future work will focus on confirming these results on new study sites, improving the filtering and processing of GEDI data, and producing height maps at regional or national scale. The resulting maps will help forest managers and public bodies to optimise forest resource inventories, as well as allow scientists to integrate these cartographic data into climate models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Spatially-detailed forest height data are useful to monitor local, regional and global carbon cycle. LiDAR remote sensing can measure three-dimensional forest features but generating spatially-contiguous forest height maps at a large scale (e.g., continental and global) is problematic because existing LiDAR instruments are still data-limited and expensive. This paper proposes a new approach based on an artificial neural network (ANN) for modeling of forest canopy heights over the China continent. Our model ingests spaceborne LiDAR metrics and multiple geospatial predictors including climatic variables (temperature and precipitation), forest type, tree cover percent and land surface reflectance. The spaceborne LiDAR instrument used in the study is the Geoscience Laser Altimeter System (GLAS), which can provide within-footprint forest canopy heights. The ANN was trained with pairs between spatially discrete LiDAR metrics and full gridded geo-predictors. This generates valid conjugations to predict heights over the China continent. The ANN modeled heights were evaluated with three different reference data. First, field measured tree heights from three experiment sites were used to validate the ANN model predictions. The observed tree heights at the site-scale agreed well with the modeled forest heights (R = 0.827, and RMSE = 4.15 m). Second, spatially discrete GLAS observations and a continuous map from the interpolation of GLAS-derived tree heights were separately used to evaluate the ANN model. We obtained R of 0.725 and RMSE of 7.86 m and R of 0.759 and RMSE of 8.85 m, respectively. Further, inter-comparisons were also performed with two existing forest height maps. Our model granted a moderate agreement with the existing satellite-based forest height maps (R = 0.738, and RMSE = 7.65 m (R² = 0.52, and RMSE = 8.99 m). Our results showed that the ANN model developed in this paper is capable of estimating forest heights over the China continent with a satisfactory accuracy. Forth coming research on our model will focus on extending the model to the estimation of woody biomass. [ABSTRACT FROM AUTHOR]

The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018–2022, achieving high migration accuracy ( R 2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine. [ABSTRACT FROM AUTHOR]

Modeling forest structure using multi-source satellite data is beneficial to understanding the relationship between vertical and horizontal structure and image features to provide more comprehensive and abundant information for the study of forest structural complexity. This study investigates and models forest structure as a multivariate structure based on sample data and active-passive remote sensing data (Landsat8, Sentinel-2A, and ALOS-2 PALSAR) from the Saihanba Forest in Hebei Province, Northern China, to measure forest structural complexity, relying on a relationship-driven model between field and satellite data. In this study, we considered the effects of the role of satellite variables in different vertical structure types and horizontal structure ranges, used two methods to stepwise select significant variables (stepwise forward selection and Pearson correlation coefficient), and employed a multivariate modeling technique (redundancy analysis) to derive a forest composite structure index (FSI), combining both horizontal and vertical structure attributes. The results show that optical texture can better represent forest structure characteristics, polarization interferometric radar information can represent the vertical structure information of forests, and combining the two can represent 77% of the variance of multiple forest structural attributes. The new FSI can explain 93% of the relationship between stand structure and satellite variables, and the linear fit R2 to the measured data reaches 0.91, which largely shows the situation of the measured data. The generated forest structure map more accurately reflects the complexity of the forest structure in the Saihanba Forest, achieving a supplementary explanation of the measured data. [ABSTRACT FROM AUTHOR]

Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public. [ABSTRACT FROM AUTHOR]

This paper report experiences from the processing and mosaicking of 518 TanDEM-X image pairs covering the entirety of Sweden, with two single map products of above-ground biomass (AGB) and forest stem volume (VOL), both with 10 m resolution. The main objective was to explore the possibilities and overcome the challenges related to forest mapping extending a large number of adjacent satellite scenes. Hence, numerous examples are presented to illustrate challenges and possible solutions. To derive the forest maps, the observables backscatter, interferometric phase height and interferometric coherence, obtained from TanDEM-X, were evaluated using empirical robust linear regression models with reference data extracted from 2288 national forest inventory plots with a 10 m radius. The interferometric phase height was the single most important observable, to predict AGB and VOL. The mosaics were evaluated on different datasets with field-inventoried stands across Sweden. The root mean square error (RMSE) was about 21%-25% (27-30 tons/ha and 52-65 m3/ha) at the stand level. It was noted that the most influencing factors on the observables in this study were local temperature and geolocation errors that were challenging to robustly compensate against. Because of this variability at the scene-level, determinations of AGB and VOL for single stands are recommended to be used with care, as an equivalent accuracy is difficult to achieve for all different scenes, with varying acquisition conditions. Still, for the evaluated stands, the mosaics were of sufficient accuracy to be used for forest management at the stand level. [ABSTRACT FROM AUTHOR]

Mapping and quantification of forest biomass change are key for forest management and for forests' contribution to the global carbon budget. We explored the potential of covering this with repeated acquisitions with TanDEM-X. We used an eight-year period in a Tanzanian miombo woodland as a test case, having repeated TanDEM-X elevation data for this period and repeated field inventory data. We also investigated the use of GEDI space–LiDAR footprint AGB estimates as an alternative to field inventory. The map of TanDEM-X elevation change appeared to be an accurate representation of the geography of forest biomass change. The relationship between TanDEM-X phase height and above-ground biomass (AGB) could be represented as a straight line passing through the origin, and this relationship was the same at both the beginning and end of the period. We obtained a similar relationship when we replaced field plot data with the GEDI data. In conclusion, temporal change in miombo woodland biomass is closely related to change in InSAR elevation, and this enabled both an accurate mapping and quantification wall to wall within 5–10% error margins. The combination of TanDEM-X and GEDI may have a near-global potential for estimation of temporal change in forest biomass. [ABSTRACT FROM AUTHOR]

This paper investigated the potential of multispectral airborne laser scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensor solution for forest mapping that is capable of providing species-specific information, required for forest management and planning purposes. Experiments were conducted using 1903 ground measured trees from 22 sample plots and multispectral ALS data, acquired with an Optech Titan scanner over a boreal forest, mainly consisting of Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies), and birch (Betula sp.), in southern Finland. ALS-features used as predictors for tree species were extracted from segmented tree objects and used in random forest classification. Different combinations of features, including point cloud features, and intensity features of single and multiple channels, were tested. Among the field-measured trees, 61.3% were correctly detected. The best overall accuracy (OA) of tree species classification achieved for correctly-detected trees was 85.9% (Kappa = 0.75), using a point cloud and single-channel intensity features combination, which was not significantly different from the ones that were obtained either using all features (OA = 85.6%, Kappa = 0.75), or single-channel intensity features alone (OA = 85.4%, Kappa = 0.75). Point cloud features alone achieved the lowest accuracy, with an OA of 76.0%. Field-measured trees were also divided into four categories. An examination of the classification accuracy for four categories of trees showed that isolated and dominant trees can be detected with a detection rate of 91.9%, and classified with a high overall accuracy of 90.5%. The corresponding detection rate and accuracy were 81.5% and 89.8% for a group of trees, 26.4% and 79.1% for trees next to a larger tree, and 7.2% and 53.9% for trees situated under a larger tree, respectively. The results suggest that Channel 2 (1064 nm) contains more information for separating pine, spruce, and birch, followed by channel 1 (1550 nm) and channel 3 (532 nm) with an overall accuracy of 81.9%, 78.3%, and 69.1%, respectively. Our results indicate that the use of multispectral ALS data has great potential to lead to a single-sensor solution for forest mapping. [ABSTRACT FROM AUTHOR]

Most bamboo forests grow in humid climates in low-latitude tropical or subtropical monsoon areas, and they are generally located in hilly areas. Bamboo trunks are very straight and smooth, which means that bamboo forests have low structural diversity. These features are beneficial to synthetic aperture radar (SAR) microwave penetration and they provide special information in SAR imagery. However, some factors (e.g., foreshortening) can compromise the interpretation of SAR imagery. The fusion of SAR and optical imagery is considered an effective method with which to obtain information on ground objects. However, most relevant research has been based on two types of remote sensing image. This paper proposes a new fusion scheme, which combines three types of image simultaneously, based on two fusion methods: bidimensional empirical mode decomposition (BEMD) and the Gram-Schmidt transform. The fusion of panchromatic and multispectral images based on the Gram-Schmidt transform can enhance spatial resolution while retaining multispectral information. BEMD is an adaptive decomposition method that has been applied widely in the analysis of nonlinear signals and to the nonstable signal of SAR. The fusion of SAR imagery with fused panchromatic and multispectral imagery using BEMD is based on the frequency information of the images. It was established that the proposed fusion scheme is an effective remote sensing image interpretation method, and that the value of entropy and the spatial frequency of the fused images were improved in comparison with other techniques such as the discrete wavelet, à-trous, and non-subsampled contourlet transform methods. Compared with the original image, information entropy of the fusion image based on BEMD improves about 0.13-0.38. Compared with the other three methods it improves about 0.06-0.12. The average gradient of BEMD is 4%-6% greater than for other methods. BEMD maintains spatial frequency 3.2-4.0 higher than other methods. The experimental results showed the proposed fusion scheme could improve the accuracy of bamboo forest classification. Accuracy increased by 12.1%, and inaccuracy was reduced by 11.0%. [ABSTRACT FROM AUTHOR]

Using Montarice in central Adriatic Italy as a case study, this paper focuses on the extraction of the spectral (i.e., plant colour) and geometrical (i.e., plant height) components of a crop canopy from archived aerial photographs, treating both parameters as proxies for archaeological prospection. After the creation of orthophotographs and a canopy height model using image-based modelling, new archaeological information is extracted from this vegetation model by applying relief-enhancing visualisation techniques. Through interpretation of the resulting data, a combination of the co-registered spectral and geometrical vegetation dimensions clearly add new depth to interpretative mapping, which is typically based solely on colour differences in orthophotographs. [ABSTRACT FROM AUTHOR]

Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency ( R 2 = 0.85, RMSE = 2.2 m , pRMSE = 11.24%). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimization of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves R 2 , RMSE, and pRMSE of 0.64, 3.38 m , and 16.08%, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models. [ABSTRACT FROM AUTHOR]

Forest canopy height data are essential to assessing forest biomass and carbon storage, and they can be efficiently retrieved using the Advanced Terrain Laser Altimetry System (ATLAS) onboard ICESat-2. However, due to the sparse and discrete distribution of ATLAS footprints, the wall-to-wall mapping of forest canopy heights requires a combination of other ancillary data. In order to match the ATLAS data with ancillary data, or estimate higher-resolution canopy heights, the standard ATLAS canopy height products (ATL08), reported at a fixed step size of 100 m (12 m × 100 m), are typically divided into 30 m step sizes. There is little concern about the accuracy of the generated 30 m segment (12 m × 30 m) dataset. Furthermore, previous studies have primarily evaluated the along-track performance of the canopy height product, rather than the ability of ATLAS to provide an accurate representation of canopy heights at the pixel-wise level. In this study, we use airborne LiDAR data as references with which to evaluate the along-track accuracy of 30 m segment canopy height products and their accuracy in representing canopy height at the pixel-wise level. A framework method for spatially matching ATLAS canopy height estimate data with ancillary data accurately is also established in this study. This enables the use of ATLAS data to characterize pixel-wise canopy heights more precisely. The results show that the accuracy of the 30 m segment version dataset in representing the pixel-wise canopy heights (R2 = 0.38, RMSE = 8.37 m) is lower than its along-track accuracy (R2 = 0.44, RMSE = 7.63 m). Using only the nighttime data for the Taranaki region, the proposed method improves the estimation of pixel-wise forest canopy heights, with the R2 increasing from 0.49 to 0.59, the RMSE decreasing from 7.48 m to 5.51 m, and the %RMSE decreasing from 36.7% to 27.6%. This study contributes to understanding the accuracy of the ATLAS in reflecting pixel-wise canopy height and provides a new way of spatially matching ATLAS canopy height data with other remote sensing data. [ABSTRACT FROM AUTHOR]

Currently, the integration of satellite-based LiDAR (ICESat-2) and continuous remote sensing imagery has been extensively applied to mapping forest canopy height over large areas. A considerable fraction of low-quality photons exists in ICESAT-2/ATL08 products, which restricts the performance of regional canopy height estimation. To solve these problems, a Local Noise Removal-Light Gradient Boosting Machine (LNR-LGB) method was proposed in this study, which efficiently filtered the unreliable canopy photons in ATL08, constructed an extrapolation model by combining multiple remote sensing data, and finally mapped the 30 m forest canopy height of Hunan Province in 2020. To verify the feasibility of this method, the canopy parameters were also filtered based on ATL08 product attributes (traditional method), and the accuracy of the two models was compared using the 10-fold cross-validation. The conclusions were as follows: (1) compared with the traditional model, the overall accuracy of the LNR-LGB model was approximately doubled, in which R2 increased from 0.46 to 0.65 and RMSE decreased from 6.11 m to 3.48 m; (2) the forest height in Hunan Province ranged from 2.53 to 50.79 m with an average value of 18.34 m. The LNR-LGB method will provide a new concept for achieving high-accuracy mapping of regional forest height. [ABSTRACT FROM AUTHOR]

Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART).We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs. [ABSTRACT FROM AUTHOR]

Remote sensing provides a consistent form of observation for biodiversity monitoring across space and time. However, the regional mapping of forest species diversity is still difficult because of the complexity of species distribution and overlapping tree crowns. A new method called "spectranomics" that maps forest species richness based on leaf chemical and spectroscopic traits using imaging spectroscopy was developed by Asner and Martin. In this paper, we use this method to detect the relationships among the spectral, biochemical and taxonomic diversity of tree species, based on 20 dominant canopy species collected in a subtropical forest study site in China. Eight biochemical components (chlorophyll, carotenoid, specific leaf area, equivalent water thickness, nitrogen, phosphorus, cellulose and lignin) are quantified by spectral signatures (R² = 0.57-0.85, p < 0.01). We also find that the simulated maximum species number based on the eight optimal biochemical components is approximately 15, which is suitable for most 30 m × 30 m forest sites within this study area. This research may support future work on regional species diversity mapping using airborne imaging spectroscopy. [ABSTRACT FROM AUTHOR]

Secondary forest succession on abandoned agricultural land has played a significant role in land cover changes in Europe over the past several decades. However, it is difficult to quantify over large areas. In this paper, we present a conceptual framework for mapping forest succession patterns using vegetation structure information derived from LiDAR data supported by national topographic vector data. This work was performed in the Szczawnica commune in the Polish Carpathians. Using object-based image analysis segments of no vegetation, and sparse/dense low/medium/high vegetation were distinguished and subsequently compared to the national topographic dataset to delineate agricultural land that is covered by vegetation, which indicates secondary succession on abandoned fields. The results showed that 18.7% of the arable land and 40.4% of grasslands, that is 31.0% of the agricultural land in the Szczawnica commune, may currently be experiencing secondary forest succession. The overall accuracy of the approach was assessed using georeferenced terrestrial photographs and was found to be 95.0%. The results of this study indicate that the proposed methodology can potentially be applied in large-scale mapping of secondary forest succession patterns on abandoned land in mountain areas. [ABSTRACT FROM AUTHOR]

Quantifying post-fire effects in a forested landscape is important to ascertain burn severity, ecosystem recovery and post-fire hazard assessments and mitigation planning. Reporting of such post-fire effects assumes significance in fire-prone countries such as USA, Australia, Spain, Greece and Portugal where prescribed burns are routinely carried out. This paper describes the use of Terrestrial Laser Scanning (TLS) to estimate and map change in the forest understorey following a prescribed burn. Eighteen descriptive metrics are derived from bi-temporal TLS which are used to analyse and visualise change in a control and fire-altered plot. Metrics derived are Above Ground Height-based (AGH) percentiles and heights, point count and mean intensity. Metrics such as AGH50change, mean AGHchange and point countchange are sensitive enough to detect subtle fire-induced change (28%-52%) whilst observing little or no change in the control plot (0-4%). A qualitative examination with field measurements of the spatial distribution of burnt areas and percentage area burnt also show similar patterns. This study is novel in that it examines the behaviour of TLS metrics for estimating and mapping fire induced change in understorey structure in a single-scan mode with a minimal fixed reference system. Further, the TLS-derived metrics can be used to produce high resolution maps of change in the understorey landscape. [ABSTRACT FROM AUTHOR]

This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mapping of the Amazon rainforest. Indeed, the evolution in time of the interferometric coherence can be properly modeled as an exponential decay and the retrieved interferometric parameters can be used, together with the backscatter, as input features to the machine learning Random Forests classifier. Furthermore, we present an analysis on the benefits of the use of textural information, derived from Sentinel-1 backscatter, in order to enhance the classification accuracy. These textures are computed through the Sum And Difference Histograms methodology and the final classification accuracy, resulting by adding them to the aforementioned features, is a thematic map that exceeds an overall agreement of 85 % , when validated using the optical external reference Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) map. The experiments presented in the final part of the paper are enriched with a further analysis and discussion on the selected scenes using updated multispectral Sentinel-2 acquisitions. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models are gaining popularity in forest variable prediction using Earth observation (EO) images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while high-quality representative wall-to-wall reference data for end-to-end training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a "model transfer" (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. The examined SeUNet model uses multi-source EO data to predict forest height. Here, EO data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, ALOS-2 PALSAR-2 SAR mosaics and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the SeUNet prediction achieved root mean squared error (RMSE) of 2.70 m and R2 of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure. [ABSTRACT FROM AUTHOR]

As one of the world's major forestry countries, accurate forest-type maps in China are of great importance for the monitoring and management of forestry resources. Classifying and mapping forest types on a large scale across the country is challenging due to the complex composition of forest types, the similarity of spectral features among forest types, and the need to collect and process large amounts of data. In this study, we generated a medium-resolution (30 m) forest classification map of China using multi-source remote sensing images and local samples. A mapping framework based on Google Earth Engine (GEE) was constructed mainly using the spectral, textural, and structural features of Sentinel-1 and Sentinel-2 remote sensing images, while local acquisition data were utilized as the mapping channel for training. The proposed method includes the following steps. First, local data processing is performed to obtain training and validation samples. Second, Sentinel-1 and Sentinel-2 data are processed to improve the classification accuracy by using the enhanced vegetation index (EVI) and the red-edge position index (REPI) computed based on the S2A data. Third, to improve classification efficiency, useless bands are removed and important bands are retained through feature importance analysis. Finally, random forest (RF) is used as a classifier to train the above features, and the classification results are used for mapping and accuracy evaluation. The validation of the samples showed an accuracy of 82.37% and a Kappa value of 0.72. The results showed that the total forest area in China is 21,662,261.17 km 2 , of which 1,127,294.42 km 2 of coniferous forests account for 52% of the total area, 981,690.98 km 2 of broad-leaf forests account for 45.3 % of the total area, and 57,275.77 km 2 of mixed coniferous and broad-leaf forests account for 2.6% of the total area. Upon further evaluation, we found that textural and structural features play a greater role in classification compared to spectral features. Our study shows that combining multi-source high-resolution remote sensing imagery with locally collected samples can produce forest maps for large areas. Our maps can accurately reflect the distribution of forests in China, which is conducive to forest conservation and development. [ABSTRACT FROM AUTHOR]

Forest fires have significant impacts on economies, cultures, and ecologies worldwide. Developing predictive models for forest fire probability is crucial for preventing and managing these fires. Such models contribute to reducing losses and the frequency of forest fires by informing prevention efforts effectively. The objective of this study was to assess and map the forest fire susceptibility (FFS) in the Indian Western Himalayas (IWH) region by employing a GIS-based fuzzy analytic hierarchy process (Fuzzy-AHP) technique, and to evaluate the FFS based on forest type and at district level in the states of Jammu and Kashmir, Himachal Pradesh, and Uttarakhand. Seventeen potential indicators were chosen for the vulnerability assessment of the IWH region to forest fires. These indicators encompassed physiographic factors, meteorological factors, and anthropogenic factors that significantly affect the susceptibility to fire in the region. The significant factors in FFS mapping included FCR, temperature, and distance to settlement. An FFS zone map of the IWH region was generated and classified into five categories of very low, low, medium, high, and very high FFS. The analysis of FFS based on the forest type revealed that tropical moist deciduous forests have a significant vulnerability to forest fire, with 86.85% of its total area having very high FFS. At the district level, FFS was found to be high in sixteen districts and very high in seventeen districts, constituting 25.7% and 22.6% of the area of the IWH region. Particularly, Lahul and Spiti had 63.9% of their total area designated as having very low FSS, making it the district least vulnerable to forest fires, while Udham Singh Nagar had a high vulnerability with approximately 86% of its area classified as having very high FFS. ROC-AUC analysis, which provided an appreciable accuracy of 79.9%, was used to assess the validity of the FFS map produced in the present study. Incorporating the FFS map into sustainable development planning will assist in devising a holistic strategy that harmonizes environmental conservation, community safety, and economic advancement. This approach can empower decision makers and relevant stakeholders to take more proactive and informed actions, promoting resilience and enhancing long-term well-being. [ABSTRACT FROM AUTHOR]

Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes particularly conspicuous in regions characterized by significant deforestation, like Southeast Asia, where forest mapping uncertainty is more pronounced, presenting users with challenges in selecting appropriate datasets across diverse regions. Moreover, this situation impedes the further enhancement of accuracy for forest mapping products. The aim of this research is to assess the consistency and accuracy of six recently produced forest mapping products in Southeast Asia. These products include three 10 m land cover products (Finer Resolution Observation and Monitoring Global LC (FROM-GLC10), ESA WorldCover 10 m 2020 (ESA2020), and ESRI 2020 Land Cover (ESRI2020)) and three forest thematic mapping products (Global PALSAR-2 Forest/Non-Forest map (JAXA FNF2020), global 30 m spatial distribution of forest cover in 2020 (GFC30_2020), and Generated_Hansen2020, which was synthesized based on Hansen TreeCover2010 (Hansen2010) and Hansen Global Forest Change (Hansen GFC) for the year 2020). Firstly, the research compared the area and spatial consistency. Next, accuracy was assessed using field validation points and manual densification points. Finally, the research analyzed the geographical environmental and biophysical factors influencing consistency. The results show that ESRI2020 had the highest overall accuracy for forest, followed by ESA2020, FROM-GLC10, and Generated_Hansen2020. Regions with elevations ranging from 200 to 3000 m and slopes below 15° or above 25° showed high spatial consistency, whereas other regions showed low consistency. Inconsistent regions showed complex landscapes heavily influenced by human activities; these regions are prone to being confused with shrubs and cropland and are also impacted by rubber and oil palm plantations, significantly affecting the accuracy of forest mapping. Based on the research findings, ESRI2020 is recommended for mountainous areas and abundant forest regions. However, in areas significantly affected by human activities, such as forest and non-forest edges and mixed areas of plantations and natural forests, caution should be taken with product selection. The research has identified areas of forest inconsistency that require attention in future forest mapping. To enhance our understanding of forest mapping and generate high-precision forest cover maps, it is recommended to incorporate multi-source data, subdivide forest types, and increase the number of sample points. [ABSTRACT FROM AUTHOR]

Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km2). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ2 of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity's overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector. [ABSTRACT FROM AUTHOR]

Current satellite remote sensing methods struggle to detect and map forest degradation, which is a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height ( h ϕ ) is a promising variable for measuring forest disturbances, as it is closely related to the mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrains, despite the fact that much of the world's remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between h ϕ and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising multilooking prior to the calculation of h ϕ strengthens this relationship, and that degradation estimates across steep slopes in the surrounding region are improved by selecting data from the most appropriate pass directions on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available. [ABSTRACT FROM AUTHOR]

Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990 km 2 ) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach 90 % . Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the potential and limitations of high-resolution single-pass interferometric synthetic aperture radar (InSAR) data for forest mapping. In particular, we present forest/non-forest classification mosaics of the State of Pennsylvania, USA, generated using TanDEM-X data at ground resolutions down to 6 m. The investigated data set was acquired between 2011 in bistatic stripmap single polarization (HH) mode. Among the different factors affecting the quality of InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering, which typically occurs in the presence of vegetation, and is a very sensitive indicator for the discrimination of forested from non-forested areas. For this reason, it has been chosen as input observable for performing the classification. In this framework, both standard boxcar and nonlocal filtering methods have been considered for the estimation of the volume correlation factor. The resulting forest/non-forest mosaics have been validated using an accurate vegetation map of the region derived from Lidar-Optic data as external independent reference. Thanks to their outstanding performance in terms of noise reduction, together with spatial features preservation, nonlocal filters show a level of agreement of about 80.5% and we observed a systematic improvement in terms of accuracy with respect to the boxcar filtering at the same resolution of about 4.5 percent points. This approach is therefore of primary importance to achieve a reliable classification at such fine resolution. Finally, the high-resolution forest/non-forest classification product of the State of Pennsylvania presented in this paper demonstrates once again the outstanding capabilities of the TanDEM-X system for a wide spectrum of commercial services and scientific applications in the field of the biosphere. [ABSTRACT FROM AUTHOR]

The analysis of rapid environment changes requires orbital sensors with high frequency of data acquisition to minimize cloud interference in the study of dynamic processes such as Amazon tropical deforestation. Moreover, a medium to high spatial resolution data is required due to the nature and complexity of variables involved in the process. In this paper we describe a multiresolution multitemporal technique to simulate Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image using Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The proposed method preserves the spectral resolution and increases the spatial resolution for mapping Amazon Rainfores deforestation using low computational resources. To evaluate this technique, sample images were acquired in the Amazon rainforest border (MODIS tile H12-V10 and ETM+/Landsat 7 path 227 row 68) for 17 July 2002 and 05 October 2002. The MODIS-based simulated ETM+ and the corresponding original ETM+ images were compared through a linear regression method. Additionally, the bootstrap technique was used to calculate the confidence interval for the model to estimate and to perform a sensibility analysis. Moreover, a Linear Spectral Mixing Model, which is the technique used for deforestation mapping in Program for Deforestation Assessment in the Brazilian Legal Amazonia (PRODES) developed by National Institute for Space Research (INPE), was applied to analyze the differences in deforestation estimates. The results showed high correlations, with values between 0.70 and 0.94 (p < 0.05, student's t test) for all ETM+ bands, indicating a good assessment between simulated and observed data (p < 0.05, Z-test). Moreover, simulated image showed a good agreement with a reference image, originating commission errors of 1% of total area estimated as deforestation in a sample area test. Furthermore, approximately 6% or 70 km2 of deforestation areas were missing in simulated image classification. Therefore, the use of Landsat simulated image provides better deforestation estimation than MODIS alone. [ABSTRACT FROM AUTHOR]

Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks. [ABSTRACT FROM AUTHOR]

Our objective was to develop a method for the assessment of forest area and structural variables for cases in which the availability of representative ground reference data is poor and these data are not collected from the whole area of interest. We implemented two independent approaches to the estimation of the forest variables of a European boreal forest: (i) the computation of wall-to-wall estimates using moderate- to low-resolution VIIRS imagery from the Suomi NPP mission; and (ii) the visual interpretation of plots of samples from very high resolution (VHR) satellite data obtained via a two-stage design. Our focus was on the statistical comparison of forest resources at a country or larger level. The study area was boreal forest ranging from Norway to the Ural Mountains in Russia. We computed a seamless mosaic from 111 VIIRS images. From the mosaic, we computed predictions for the forest area, growing stock volume, height of the dominating tree layer, proportion of conifers and broadleaved trees, site fertility class, and leaf area index. The reference data for the VIIRS imagery were national forest inventory (NFI)-based raster maps from Finland. The first stage sample of VHR data included 42 images; of these, a second stage sample of 2690 plots was visually interpreted for the same variables. The forest area prediction from VIIRS for the whole study area was 1.2% higher than the VHR-based result. All other structural variable predictions using VIIRS fitted within the 95% confidence intervals computed from the VHR sample except for estimates of the main tree species groups, which were outside the limits. A comparison of VIIRS-based forest area estimates using Finnish and Swedish NFI data indicated overestimations of 10.0% points and 4.6% points, whereas the total growing stock volumes were overestimated by 8% and underestimated by 3.4%, respectively. The correlation coefficients between the VIIRS and VHR image predictions at the 42 VHR image locations varied from 0.70 to 0.85. The VIIRS maps strongly averaged the local predictions due to their coarse spatial resolutions. Based on our findings, the approach using two independent estimations yielded similar figures for the central forest variables for the European boreal forest. A model computed using reference data from a small part of the area of interest can provide satisfactory predictions for a much larger area with a similar biome. Therefore, our concept is applicable to the estimation and overall mapping of a forest area and central structural variables at regional to national levels. [ABSTRACT FROM AUTHOR]

Forest growing stock volume (GSV, m3/ha) is an indispensable variable for forest resources supervision. Precise measurement of GSV is conducive to monitoring forest dynamics. Nevertheless, little research has explored the pattern pixel values and the function of texture features derived from a vegetation index (VI) for GSV prediction. In this study, we investigated combining linear regression or Random Forest with Sentinel-2 spectral predictors and image textures derived from spectral bands and vegetation indices, which were based on standard deviation values or mean values at the plot level, for predicting GSV of Masson pine, theropencedrymion and all the survey plots in Anji County, China. Specifically, ten groups of experiments encompassing combinations of spectral parameters and texture measures were established for detecting the potential of image textures based on two different image pixel statistics. The results showed that texture measures derived from VI were superior to spectral parameters or texture measures derived from spectral bands for estimating the GSV of single tree species. Moreover, texture features based on shortwave infrared bands or their related VI and the standard deviation at pixel level for spectral bands/indices were emphasized. Finally, the mean value at the plot level exhibited slightly stronger potential than the standard deviation in general. [ABSTRACT FROM AUTHOR]

In this paper, we consider a new method for forest canopy height estimation using TanDEM-X single-pass radar interferometry. We exploit available information from sample-based, space-borne LiDAR systems, such as the Global Ecosystem Dynamics Investigation (GEDI) sensor, which offers high-resolution vertical profiling of forest canopies. To respond to this, we have developed a new extended Fourier-Legendre series approach for fusing high-resolution (but sparsely spatially sampled) GEDI LiDAR waveforms with TanDEM-X radar interferometric data to improve wide-area and wall-to-wall estimation of forest canopy height. Our key methodological development is a fusion of the standard uniform assumption for the vertical structure function (the SINC function) with LiDAR vertical profiles using a Fourier-Legendre approach, which produces a convergent series of approximations of the LiDAR profiles matched to the interferometric baseline. Our results showed that in our test site, the Petawawa Research Forest, the SINC function is more accurate in areas with shorter canopy heights (<~27 m). In taller forests, the SINC approach underestimates forest canopy height, whereas the Legendre approach avails upon simulated GEDI forest structural vertical profiles to overcome SINC underestimation issues. Overall, the SINC + Legendre approach improved canopy height estimates (RMSE = 1.29 m) compared to the SINC approach (RMSE = 4.1 m). [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany. [ABSTRACT FROM AUTHOR]

Forest fires are one of the most severe natural disasters facing global ecosystems, as they have a significant impact on ecological security and social development. As remote sensing technology has developed, burned areas can now be quickly extracted to support fire monitoring and post-disaster recovery. This study focused on monitoring forest fires that occurred in Chongqing, China, in August 2022. The burned area was identified using various satellite images, including Sentinel-2, Landsat8, Environmental Mitigation II A (HJ2A), and Gaofen-6 (GF-6). The burned area was extracted using visual interpretation, differenced Normalized Difference Vegetation Index (dNDVI), and differenced Normalized Burnup Ratio (dNBR). The results showed that: (1) The results of the three monitoring methods were very consistent, with a coefficient of determination R2 > 0.96. (2) A threshold method based on the dNBR-extracted burned area was used to analyze fire severity, with moderate-severity fires making up the majority (58.05%) of the fires. (3) Different topographic factors had some influence on the severity of the forest fires. High elevation, steep slopes and the northwestern aspect had the largest percentage of burned area. [ABSTRACT FROM AUTHOR]

Forest canopy height is one of the critical parameters for carbon sink estimation. Although spaceborne lidar data can obtain relatively high precision canopy height on discrete light spots, to obtain continuous canopy height, the integration of optical remote sensing image data is required to achieve "from discrete to continuous" extrapolation based on different prediction models (parametric model and non-parametric model). This study focuses on the Shangri-La area and seeks to assess the practical applicability of two predictive models under complex mountainous conditions, using a combination of active and passive remote sensing data from ICESat-2 and Sentinel-2. The research aims to enhance our understanding of the effectiveness of these models in addressing the unique challenges presented by mountainous terrain, including rugged topography, variable vegetation cover, and extreme weather conditions. Through this work, we hope to contribute to the development of improved geospatial prediction algorithms for mountainous regions worldwide. The results show the following: (1) the fitting effect of the selected parametric model (empirical function regression) is poor in the area of Quercus acutissima and Pinus yunnanensis; (2) evaluation of the importance of each explanatory variable in the non-parametric model (random forest regression) shows that topographic and meteorological factors play a dominant role in canopy height inversion; (3) when random forest regression is applied to the inversion of canopy height, there is often a problem of error accumulation, which is of particular concern to the Quercus acutissima and Pinus yunnanensis; (4) the random forest regression with the optimal features has relatively higher precision by comparing the inversion accuracy of canopy height data of the empirical function regression, random forest regression with all features, and random forest regression with the optimal features in the study area, i.e., R2 (coefficient of determination) = 0.865 and RMSE (root mean square error) = 3.184 m. In contrast, the poor estimation results reflected by the empirical function regression, mainly resulting from the lack of consideration of topographic and meteorological factors, are not applicable to the inversion of canopy height under complex topographic conditions. [ABSTRACT FROM AUTHOR]

A new algorithm for detecting branch attachments on stems based on a voxel approach and line object detection by a voting procedure is introduced. This algorithm can be used to evaluate the quality of stems by giving the branch density of each standing tree. The detected branches were evaluated using field-sampled trees. The algorithm detected 63% of the total amount of branch whorls and 90% of the branch whorls attached in the height interval from 0 to 10 m above ground. The suggested method could be used to create maps of forest stand stem quality data. [ABSTRACT FROM AUTHOR]

Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement. [ABSTRACT FROM AUTHOR]

Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (EM) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: −2.87 m). [ABSTRACT FROM AUTHOR]

There are several new and imminent space-based sensors intended to support mapping of forest structure and biomass. These instruments, along with advancing cloud-based mapping platforms, will soon contribute to a proliferation of biomass maps. One means of differentiating the quality of different maps and estimation strategies will be comparison of results against independent field-based estimates at various scales. The Forest Inventory and Analysis Program of the US Forest Service (FIA) maintains a designed sample of uniformly measured field plots across the conterminous United States. This paper reports production of a map of statistical estimates of mean biomass, created at approximately the finest scale (64,000-hectare hexagons) allowed by FIA's sample density. This map may be useful for assessing the accuracy of future remotely sensed biomass estimates. Equally important, fine-scale mapping of FIA estimates highlights several ways in which field- and remote sensing-based methods must be aligned to ensure comparability. For example, the biomass in standing dead trees, which may or may not be included in biomass estimates, represents a source of potential discrepancy that FIA shows to be particularly important in the Western US. Likewise, alternative allometric equations (which link measurable tree dimensions such as diameter to difficult-to-measure variables like biomass) strongly impact biomass estimates in ways that can vary over short distances. Potential mismatch in the conditions counted as forests also varies greatly over space. Field-to-map comparisons will ideally minimize these sources of uncertainty by adopting common allometry, carbon pools, and forest definitions. Our national hexagon-level benchmark estimates, provided in Supplementary Files, therefore addresses multiple pools and allometric approaches independently, while providing explicit forest area and uncertainty information. This range of information is intended to allow scientists to minimize potential discrepancies in support of unambiguous validation. [ABSTRACT FROM AUTHOR]

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context. [ABSTRACT FROM AUTHOR]

The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation. [ABSTRACT FROM AUTHOR]

Mapping the distribution of forest resources at tree species levels is important due to their strong association with many quantitative and qualitative indicators. With the ongoing development of artificial intelligence technologies, the effectiveness of deep-learning classification models for high spatial resolution (HSR) remote sensing images has been proved. However, due to the poor statistical separability and complex scenarios, it is still challenging to realize fully automated and highly accurate forest types at tree species level mapping. To solve the problem, a novel end-to-end deep learning fusion method for HSR remote sensing images was developed by combining the advantageous properties of multi-modality representations and the powerful features of post-processing step to optimize the forest classification performance refined to the dominant tree species level in an automated way. The structure of the proposed model consisted of a two-branch fully convolutional network (dual-FCN8s) and a conditional random field as recurrent neural network (CRFasRNN), which named dual-FCN8s-CRFasRNN in the paper. By constructing a dual-FCN8s network, the dual-FCN8s-CRFasRNN extracted and fused multi-modality features to recover a high-resolution and strong semantic feature representation. By imbedding the CRFasRNN module into the network as post-processing step, the dual-FCN8s-CRFasRNN optimized the classification result in an automatic manner and generated the result with explicit category information. Quantitative evaluations on China's Gaofen-2 (GF-2) HSR satellite data showed that the dual-FCN8s-CRFasRNN provided a competitive performance with an overall classification accuracy (OA) of 90.10%, a Kappa coefficient of 0.8872 in the Wangyedian forest farm, and an OA of 74.39%, a Kappa coefficient of 0.6973 in the GaoFeng forest farm, respectively. Experiment results also showed that the proposed model got higher OA and Kappa coefficient metrics than other four recently developed deep learning methods and achieved a better trade-off between automaticity and accuracy, which further confirmed the applicability and superiority of the dual-FCN8s-CRFasRNN in forest types at tree species level mapping tasks. [ABSTRACT FROM AUTHOR]

Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In spite of the great technological advances made in airborne and spaceborne sensors over the past decades, current Earth observation (EO) change detection methods still need to overcome numerous limitations. Optical sensors have been commonly used for detecting land use and land cover changes (LULCC), however, the requirement of certain technical and environmental conditions (e.g., sunlight, not cloud-coverage) restrict their use. More recently, synthetic aperture radar (SAR)-based change detection approaches have been used to overcome these technical limitations, but they commonly rely on static detection approaches (e.g., pre and post disturbance scenario comparison) that are slow to monitor change. In this context, this paper presents a novel approach for mapping forest structural changes in a continuous and near-real-time manner using dense Sentinel-1 image time-series. Our cumulative sum–spatial mean corrected (CUSU-SMC) algorithm approach is based on cumulative sum statistical analysis, which allows the continuous monitoring of radar signal variations, derived from forest structural change. Taking advantage of the high data availability offered by the Sentinel-1 (S-1) C-band constellation, we used an S-1 ground range detected (GRD) dual (VV, VH) polarization timeseries, formed by a total of 84 images, to monitor clear-cutting operations carried out in a Scottish forest during 2019. The analysis showed a user's accuracy of 82% for the (conservative) detection approach. The use of a post-processing neighbor filter increased the detection performance to a user's accuracy of 86% with an overall accuracy of 77% for areas of a minimum extent of 0.4 ha. To further validate the detection performance of the method, the CUSU-SMC change detector was tested against commonly-used pairwise change detection approaches for the same period. These results emphasize the capabilities of dense SAR time-series for environmental monitoring and provide a useful tool for optimizing national forest inventories. [ABSTRACT FROM AUTHOR]