Remote Sensing

Research

21 pages, 1695 KiB

Open AccessArticle

Classification Strategies for Unbalanced Binary Maps: Finding Ponderosa Pine (Pinus ponderosa) in the Willamette Valley

by Audrey P. Riddell, Stephen A. Fitzgerald, Chu Qi and Bogdan M. Strimbu

Remote Sens. 2020, 12(20), 3325; https://doi.org/10.3390/rs12203325 - 13 Oct 2020

Cited by 1 | Viewed by 2645

Abstract

Forest species classifications are becoming increasingly automated as advances are made in machine learning. Complex algorithms can reach high accuracies, but are not always suitable for small-scale classifications, which may benefit from simpler conventional methods. The goal of this classification was to identify [...] Read more.

Forest species classifications are becoming increasingly automated as advances are made in machine learning. Complex algorithms can reach high accuracies, but are not always suitable for small-scale classifications, which may benefit from simpler conventional methods. The goal of this classification was to identify contiguous stands of ponderosa pine (Pinus ponderosa Douglas ex Lawson) against a mix of forest and non-forest background in the southern Willamette Valley, Oregon. The study area is approximately 816,600 ha, considerably larger than most study areas used for presenting techniques for tree species classification. To achieve the objective, we used two classification procedures, one parametric and one non-parametric. For the parametric method, we selected the maximum likelihood (ML) algorithm, whereas for the non-parametric method we chose the random forest (RF) algorithm. To identify ponderosa pine, we used 1 m spatial resolution red-green-blue-infrared (RGBI) aerial images supplied by the U.S. National Agriculture Imagery Program (NAIP) and 1 m spatial resolution canopy height models (CHMs) provided by the Oregon Department of Geology and Mineral Industries (DOGAMI). We tested four data variations for each method: Aerial imagery, CHM-masked aerial imagery, aerial imagery with an additional CHM band, and CHM-masked aerial imagery with a CHM band. The parametric classifications of aerial imagery alone reached an average kappa coefficient of 0.29, which increased to 0.51 when masked with CHM data. The incorporation of CHM data as a fifth band resulted in a similar improvement in kappa (0.47), but the most effective parametric method was the incorporation of CHM data as both a fifth band and a post-classification mask, resulting in a kappa coefficient of 0.89. The non-parametric classification of aerial imagery achieved a mean validation kappa coefficient of 0.85 collectively and 0.90 individually, which only increased by approximately 0.01 or less when the CHM masks were applied. The addition of the CHM band increased the kappa value to 0.91 for both individual and collective tile classifications. The highest kappa of all methods was achieved through five-band non-parametric classification with the addition of the CHM band (0.94) for both collective and individual classifications. Our results suggest that parametric methods, when enhanced with a CHM mask, could be suitable for large-area, small-scale classifications based on RGBI imagery, but a non-parametric classification of fused spectral and height data will generally achieve the highest accuracy for large, unbalanced datasets. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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25 pages, 10402 KiB

Open AccessArticle

Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand

by Veeranun Songsom, Werapong Koedsin, Raymond J. Ritchie and Alfredo Huete

Remote Sens. 2019, 11(8), 955; https://doi.org/10.3390/rs11080955 - 22 Apr 2019

Cited by 48 | Viewed by 7255

Abstract

Vegetation phenology is the annual cycle timing of vegetation growth. Mangrove phenology is a vital component to assess mangrove viability and includes start of season (SOS), end of season (EOS), peak of season (POS), and length of season (LOS). Potential environmental drivers include [...] Read more.

Vegetation phenology is the annual cycle timing of vegetation growth. Mangrove phenology is a vital component to assess mangrove viability and includes start of season (SOS), end of season (EOS), peak of season (POS), and length of season (LOS). Potential environmental drivers include air temperature (Ta), surface temperature (Ts), sea surface temperature (SST), rainfall, sea surface salinity (SSS), and radiation flux (Ra). The Enhanced vegetation index (EVI) was calculated from Moderate Resolution Imaging Spectroradiometer (MODIS, MOD13Q1) data over five study sites between 2003 and 2012. Four of the mangrove study sites were located on the Malay Peninsula on the Andaman Sea and one site located on the Gulf of Thailand. The goals of this study were to characterize phenology patterns across equatorial Thailand Indo-Malay mangrove forests, identify climatic and aquatic drivers of mangrove seasonality, and compare mangrove phenologies with surrounding upland tropical forests. Our results show the seasonality of mangrove growth was distinctly different from the surrounding land-based tropical forests. The mangrove growth season was approximately 8–9 months duration, starting in April to June, peaking in August to October and ending in January to February of the following year. The 10-year trend analysis revealed significant delaying trends in SOS, POS, and EOS for the Andaman Sea sites but only for EOS at the Gulf of Thailand site. The cumulative rainfall is likely to be the main factor driving later mangrove phenologies. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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18 pages, 1244 KiB

Open AccessArticle

Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data

by Julia Marrs and Wenge Ni-Meister

Remote Sens. 2019, 11(7), 819; https://doi.org/10.3390/rs11070819 - 5 Apr 2019

Cited by 66 | Viewed by 8447

Abstract

The use of light detection and ranging (LiDAR) techniques for recording and analyzing tree and forest structural variables shows strong promise for improving established hyperspectral-based tree species classifications; however, previous multi-sensoral projects were often limited by error resulting from seasonal or flight path [...] Read more.

The use of light detection and ranging (LiDAR) techniques for recording and analyzing tree and forest structural variables shows strong promise for improving established hyperspectral-based tree species classifications; however, previous multi-sensoral projects were often limited by error resulting from seasonal or flight path differences. The National Aeronautics and Space Administration (NASA) Goddard’s LiDAR, hyperspectral, and thermal imager (G-LiHT) is now providing co-registered data on experimental forests in the United States, which are associated with established ground truths from existing forest plots. Free, user-friendly machine learning applications like the Orange Data Mining Extension for Python recently simplified the process of combining datasets, handling variable redundancy and noise, and reducing dimensionality in remotely sensed datasets. Neural networks, CN2 rules, and support vector machine methods are used here to achieve a final classification accuracy of 67% for dominant tree species in experimental plots of Howland Experimental Forest, a mixed coniferous–deciduous forest with ten dominant tree species, and 59% for plots in Penobscot Experimental Forest, a mixed coniferous–deciduous forest with 15 dominant tree species. These accuracies are higher than those produced using LiDAR or hyperspectral datasets separately, suggesting that combined spectral and structural data have a greater richness of complementary information than either dataset alone. Using greatly simplified datasets created by our dimensionality reduction methodology, machine learner performance remains comparable or higher to that using the full dataset. Across forests, the identification of shared structural and spectral variables suggests that this methodology can successfully identify parameters with high explanatory power for differentiating among tree species, and opens the possibility of addressing large-scale forestry questions using optimized remote sensing workflows. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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25 pages, 2096 KiB

Open AccessArticle

Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping

by Sachit Rajbhandari, Jagannath Aryal, Jon Osborn, Arko Lucieer and Robert Musk

Remote Sens. 2019, 11(5), 503; https://doi.org/10.3390/rs11050503 - 1 Mar 2019

Cited by 26 | Viewed by 5856

Abstract

Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. [...] Read more.

Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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25 pages, 5919 KiB

Open AccessArticle

Predictive Ecosystem Mapping of South-Eastern Australian Temperate Forests Using Lidar-Derived Structural Profiles and Species Distribution Models

by Melissa Fedrigo, Stephen B. Stewart, Stephen H. Roxburgh, Sabine Kasel, Lauren T. Bennett, Helen Vickers and Craig R. Nitschke

Remote Sens. 2019, 11(1), 93; https://doi.org/10.3390/rs11010093 - 7 Jan 2019

Cited by 16 | Viewed by 6912

Abstract

Modern approaches to predictive ecosystem mapping (PEM) have not thoroughly explored the use of ‘characteristic’ gradients, which describe vegetation structure (e.g., light detection and ranging (lidar)-derived structural profiles). In this study, we apply a PEM approach by classifying the dominant stand types within [...] Read more.

Modern approaches to predictive ecosystem mapping (PEM) have not thoroughly explored the use of ‘characteristic’ gradients, which describe vegetation structure (e.g., light detection and ranging (lidar)-derived structural profiles). In this study, we apply a PEM approach by classifying the dominant stand types within the Central Highlands region of south-eastern Australia using both lidar and species distribution models (SDMs). Similarity percentages analysis (SIMPER) was applied to comprehensive floristic surveys to identify five species which best separated stand types. The predicted distributions of these species, modelled using random forests with environmental (i.e., climate, topography) and optical characteristic gradients (Landsat-derived seasonal fractional cover), provided an ecological basis for refining stand type classifications based only on lidar-derived structural profiles. The resulting PEM model represents the first continuous distribution map of stand types across the study region that delineates ecotone stands, which are seral communities comprised of species typical of both rainforest and eucalypt forests. The spatial variability of vegetation structure incorporated into the PEM model suggests that many stand types are not as continuous in cover as represented by current ecological vegetation class distributions that describe the region. Improved PEM models can facilitate sustainable forest management, enhanced forest monitoring, and informed decision making at landscape scales. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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20 pages, 12433 KiB

Open AccessArticle

A Single-Tree Processing Framework Using Terrestrial Laser Scanning Data for Detecting Forest Regeneration

by Johannes Heinzel and Christian Ginzler

Remote Sens. 2019, 11(1), 60; https://doi.org/10.3390/rs11010060 - 29 Dec 2018

Cited by 9 | Viewed by 4403

Abstract

Direct assessment of forest regeneration from remote sensing data is a previously little-explored problem. This is due to several factors which complicate object detection of small trees in the understory. Most existing studies are based on airborne laser scanning (ALS) data, which often [...] Read more.

Direct assessment of forest regeneration from remote sensing data is a previously little-explored problem. This is due to several factors which complicate object detection of small trees in the understory. Most existing studies are based on airborne laser scanning (ALS) data, which often has insufficient point densities in the understory forest layers. The present study uses plot-based terrestrial laser scanning (TLS) and the survey design was similar to traditional forest inventory practices. Furthermore, a framework of methods was developed to solve the difficulties of detecting understory trees for quantifying regeneration in temperate montane forest. Regeneration is of special importance in our montane study area, since large parts are declared as protection forest against alpine natural hazards. Close to nature forest structures were tackled by separating 3D tree stem detection from overall tree segmentation. In support, techniques from 3D mathematical morphology, Hough transformation and state-of-the-art machine learning were applied. The methodical framework consisted of four major steps. These were the extraction of the tree stems, the estimation of the stem diameters at breast height (DBH), the image segmentation into individual trees and finally, the separation of two groups of regeneration. All methods were fully automated and utilized volumetric 3D image information which was derived from the original point cloud. The total amount of regeneration was split into established regeneration, consisting of trees with a height > 130 cm in combination with a DBH < 12 cm and unestablished regeneration, consisting of trees with a height < 130 cm. Validation was carried out against field-based expert estimates of percentage ground cover, differentiating seven classes that were similar to those used by forest inventory. The mean absolute error (MAE) of our method for established regeneration was 1.11 classes and for unestablished regeneration only 0.27 classes. Considering the metrical distances between the class centres, the MAE amounted 8.08% for established regeneration and 2.23% for unestablished regeneration. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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21 pages, 5479 KiB

Open AccessArticle

Forest Height Estimation Based on Constrained Gaussian Vertical Backscatter Model Using Multi-Baseline P-Band Pol-InSAR Data

by Xiaofan Sun, Bingnan Wang, Maosheng Xiang, Shuai Jiang and Xikai Fu

Remote Sens. 2019, 11(1), 42; https://doi.org/10.3390/rs11010042 - 28 Dec 2018

Cited by 13 | Viewed by 3690

Abstract

In the case of low frequencies (e.g., P-band) radar observations, the Gaussian Vertical Backscatter (GVB) model, a model that takes into account the vertical heterogeneity of the wave-canopy interactions, can describe the forest vertical backscatter profile (VBP) more accurately. However, the GVB model [...] Read more.

In the case of low frequencies (e.g., P-band) radar observations, the Gaussian Vertical Backscatter (GVB) model, a model that takes into account the vertical heterogeneity of the wave-canopy interactions, can describe the forest vertical backscatter profile (VBP) more accurately. However, the GVB model is highly complex, seriously reducing the inversion efficiency because of a number of variables. Given that concern, this paper proposes a constrained Gaussian Vertical Backscatter (CGVB) model to reduce the complexity of the GVB model by establishing a constraint relationship between forest height and the backscattering vertical fluctuation (BVF) of the GVB model. The CGVB model takes into account the influence of incidence angle on scattering mechanisms. The BVF of VBP described by the CGVB model is expressed with forest height and a polynomial function of incidence angle. In order to build the CGVB model, this paper proposes the supervised learning based on RANSAC (SLBR). The proposed SLBR method used forest height as a prior knowledge to determine the function of incidence angle in the CGVB model. In this process, the Random Sample Consensus (RANSAC) method is applied to perform function fitting. Before building the CGVB model, iterative weighted complex least squares (IWCLS) is employed to extract the required volume coherence. Based on the CGVB model, forest height estimation was obtained by nonlinear least squares optimization. E-SAR P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) data acquired during the BIOSAR 2008 campaign was used to test the performance of the proposed CGVB model. It can be observed that, compared with Random Volume over Ground (RVoG) model, the proposed CGVB model improves the estimation accuracy of the areas with incidence angle less than

0.8

rad and less than

0.6

rad by

28.57 %

and

40.35 %

, respectively. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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22 pages, 6837 KiB

Open AccessArticle

Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data

by Yuyun Chen, Longwei Li, Dengsheng Lu and Dengqiu Li

Remote Sens. 2019, 11(1), 7; https://doi.org/10.3390/rs11010007 - 20 Dec 2018

Cited by 46 | Viewed by 8831

Abstract

Bamboo forests, due to rapid growth and short harvest rotation, play an important role in carbon cycling and local economic development. Accurate estimation of bamboo forest aboveground biomass (AGB) has garnered increasing attention during the past two decades. However, remote sensing-based AGB estimation [...] Read more.

Bamboo forests, due to rapid growth and short harvest rotation, play an important role in carbon cycling and local economic development. Accurate estimation of bamboo forest aboveground biomass (AGB) has garnered increasing attention during the past two decades. However, remote sensing-based AGB estimation for bamboo forests is challenging due to poor understanding of the mechanisms between bamboo forest growth characteristics and remote sensing data. The objective of this research is to examine the remote sensing characteristics of on-year and off-year bamboo forests at different dates and their AGB estimation performance. This research used multiple Sentinel-2 data to explore AGB estimation of bamboo forests in Zhejiang Province, China, by taking into account the unique characteristics of on-year and off-year bamboo forest growth features. Combining field survey data and Sentinel-2 spectral responses (spectral bands and vegetation indices) and textural images, random forest was used to identify key variables for AGB estimation. The results show that (1) the on-year and off-year bamboo forests have considerably different spectral signatures, especially in the wavelengths between red edge 2 and near-infrared wavelength (NIR2) (740–865 nm), making it possible to separate on-year and off-year bamboo forests; (2) on-year bamboo forests have similar spectral signatures although AGB increases from as small as 40 Mgha⁻¹ to as high as 90 Mgha⁻¹, implying that optical sensor data cannot effectively model on-year bamboo AGB; (3) off-year bamboo AGB has significant relationships with red and shortwave infrared (SWIR) spectral bands in the April image and with red edge 2 in the July image, but the AGB saturation problem yields poor estimation accuracy; (4) stratification considerably improved off-year bamboo AGB estimation but not on-year, non-stratification using the April image is recommended; and (5) Sentinel-2 data cannot solve the bamboo AGB data saturation problem when AGB is greater than 70 Mgha⁻¹, similar to other optical sensor data such as Landsat. More research should be conducted in the future to integrate multiple sources—remotely sensed data (e.g., lidar, optical sensor data) and ancillary data (e.g., soil, topography)—into AGB modeling to improve the estimation. The use of very high spatial resolution images that can effectively extract tree density information may improve bamboo AGB estimation and yield new insights. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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17 pages, 2624 KiB

Open AccessFeature PaperArticle

Quantifying Changes on Forest Succession in a Dry Tropical Forest Using Angular-Hyperspectral Remote Sensing

by Virginia Garcia Millan and Arturo Sanchez-Azofeifa

Remote Sens. 2018, 10(12), 1865; https://doi.org/10.3390/rs10121865 - 22 Nov 2018

Cited by 11 | Viewed by 3724

Abstract

The tropical dry forest (TDF) is one the most threatened ecosystems in South America, existing on a landscape with different levels of ecological succession. Among satellites dedicated to Earth observation and monitoring ecosystem succession, CHRIS/PROBA is the only satellite that presents quasi-simultaneous multi-angular [...] Read more.

The tropical dry forest (TDF) is one the most threatened ecosystems in South America, existing on a landscape with different levels of ecological succession. Among satellites dedicated to Earth observation and monitoring ecosystem succession, CHRIS/PROBA is the only satellite that presents quasi-simultaneous multi-angular pointing and hyperspectral imaging. These two characteristics permit the study of structural and compositional differences of TDFs with different levels of succession. In this paper, we use 2008 and 2014 CHRIS/PROBA images from a TDF in Minas Gerais, Brazil to study ecosystem succession after abandonment. Using a −55° angle of observation; several classifiers including spectral angle mapper (SAM), support vector machine (SVM), and decision trees (DT) were used to test how well they discriminate between different successional stages. Our findings suggest that the SAM is the most appropriate method to classify TDFs as a function of succession (accuracies ~80 for % for late stage, ~85% for the intermediate stage, ~70% for early stage, and ~50% for other classes). Although CHRIS/PROBA cannot be used for large-scale/long-term monitoring of tropical forests because of its experimental nature; our results support the potential of using multi-angle hyperspectral data to characterize the structure and composition of TDFs in the near future. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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24 pages, 6610 KiB

Open AccessArticle

Semi-Automated Delineation of Stands in an Even-Age Dominated Forest: A LiDAR-GEOBIA Two-Stage Evaluation Strategy

by Nuria Sanchez-Lopez, Luigi Boschetti and Andrew T. Hudak

Remote Sens. 2018, 10(10), 1622; https://doi.org/10.3390/rs10101622 - 12 Oct 2018

Cited by 10 | Viewed by 4232

Abstract

Regional scale maps of homogeneous forest stands are valued by forest managers and are of interest for landscape and ecological modelling. Research focused on stand delineation has substantially increased in the last decade thanks to the development of Geographic Object Based Image Analysis [...] Read more.

Regional scale maps of homogeneous forest stands are valued by forest managers and are of interest for landscape and ecological modelling. Research focused on stand delineation has substantially increased in the last decade thanks to the development of Geographic Object Based Image Analysis (GEOBIA). Nevertheless, studies focused on even-age dominated forests are still few and the proposed approaches are often heuristic, local, or lacking objective evaluation protocols. In this study, we present a two-stage evaluation strategy combining both unsupervised and supervised evaluation methods for semi-automatic delineation of forest stands at regional scales using Light Detection and Ranging (LiDAR) raster summary metrics. The methodology is demonstrated on two contiguous LiDAR datasets covering more than 54,000 ha in central Idaho, where clearcuts were a common harvesting method during the twentieth century. Results show good delineation of even-aged forests and demonstrate the ability of LiDAR to discriminate stands harvested more than 50 years ago, that are generally challenging to discriminate with optical data. The two-stage strategy reduces the reference data required within the supervised evaluation and increases the scope of a reliable semi-automatic delineation to larger areas. This is an objective and straightforward approach that could potentially be replicated and adapted to address other study needs. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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30 pages, 26149 KiB

Open AccessArticle

A Forest Attribute Mapping Framework: A Pilot Study in a Northern Boreal Forest, Northwest Territories, Canada

by Craig Mahoney, Ron J. Hall, Chris Hopkinson, Michelle Filiatrault, Andre Beaudoin and Qi Chen

Remote Sens. 2018, 10(9), 1338; https://doi.org/10.3390/rs10091338 - 22 Aug 2018

Cited by 25 | Viewed by 6953

Abstract

A methods framework is presented that utilizes field plots, airborne light detection and ranging (LiDAR), and spaceborne Geoscience Laser Altimeter System (GLAS) data to estimate forest attributes over a 20 Mha area in Northern Canada. The framework was implemented to scale up forest [...] Read more.

A methods framework is presented that utilizes field plots, airborne light detection and ranging (LiDAR), and spaceborne Geoscience Laser Altimeter System (GLAS) data to estimate forest attributes over a 20 Mha area in Northern Canada. The framework was implemented to scale up forest attribute models from field data to intersecting airborne LiDAR data, and then to GLAS footprints. GLAS data were sequentially filtered and submitted to the k-nearest neighbour (k-NN) imputation algorithm to yield regional estimates of stand height and crown closure at a 30 m resolution. Resulting outputs were assessed against independent airborne LiDAR data to evaluate regional estimates of stand height (mean difference = −1 m, RMSE = 5 m) and crown closure (mean difference = −5%, RMSE = 9%). Additional assessments were performed as a function of dominant vegetation type and ecoregion to further evaluate regional products. These attributes form the primary descriptive structure attributes that are typical of forest inventory mapping programs, and provide insight into how they can be derived in northern boreal regions where field information and physical access is often limited. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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27 pages, 5513 KiB

Open AccessArticle

Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees

by Brigitte Colin, Michael Schmidt, Samuel Clifford, Alan Woodley and Kerrie Mengersen

Remote Sens. 2018, 10(8), 1260; https://doi.org/10.3390/rs10081260 - 10 Aug 2018

Cited by 6 | Viewed by 8072

Abstract

Data aggregation is a necessity when working with big data. Data reduction steps without loss of information are a scientific and computational challenge but are critical to enable effective data processing and information delineation in data-rich studies. We investigated the effect of four [...] Read more.

Data aggregation is a necessity when working with big data. Data reduction steps without loss of information are a scientific and computational challenge but are critical to enable effective data processing and information delineation in data-rich studies. We investigated the effect of four spatial aggregation schemes on Landsat imagery on prediction accuracy of green photosynthetic vegetation (PV) based on fractional cover (FCover). To reduce data volume we created an evenly spaced grid, overlaid that on the PV band and delineated the arithmetic mean of PV fractions contained within each grid cell. The aggregated fractions and the corresponding geographic grid cell coordinates were then used for boosted regression tree prediction models. Model goodness of fit was evaluated by the Root Mean Squared Error (RMSE). Two spatial resolutions (3000 m and 6000 m) offer good prediction accuracy whereas others show either too much unexplained variability model prediction results or the aggregation resolution smoothed out local PV in heterogeneous land. We further demonstrate the suitability of our aggregation scheme, offering an increased processing time without losing significant topographic information. These findings support the feasibility of using geographic coordinates in the prediction of PV and yield satisfying accuracy in our study area. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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12 pages, 3595 KiB

Open AccessArticle

UAV Photogrammetry of Forests as a Vulnerable Process. A Sensitivity Analysis for a Structure from Motion RGB-Image Pipeline

by Julian Frey, Kyle Kovach, Simon Stemmler and Barbara Koch

Remote Sens. 2018, 10(6), 912; https://doi.org/10.3390/rs10060912 - 9 Jun 2018

Cited by 95 | Viewed by 10758

Abstract

Structural analysis of forests by UAV is currently growing in popularity. Given the reduction in platform costs, and the number of algorithms available to analyze data output, the number of applications has grown rapidly. Forest structures are not only linked to economic value [...] Read more.

Structural analysis of forests by UAV is currently growing in popularity. Given the reduction in platform costs, and the number of algorithms available to analyze data output, the number of applications has grown rapidly. Forest structures are not only linked to economic value in forestry, but also to biodiversity and vulnerability issues. LiDAR remains the most promising technique for forest structural assessment, but small LiDAR sensors suitable for UAV applications are expensive and are limited to a few manufactures. The estimation of 3D-structures from two-dimensional image sequences called ‘Structure from motion’ (SfM) overcomes this limitation by photogrammetrically reconstructing point clouds similar to those rendered from LiDAR sensors. The result of these techniques in highly structured terrain strongly depends on the methods employed during image acquisition, therefore structural indices might be vulnerable to misspecifications in flight campaigns. In this paper, we outline how image overlap and ground sampling distances affect image reconstruction completeness in 2D and 3D. Higher image overlaps and coarser GSDs have a clearly positive influence on reconstruction quality. Therefore, higher accuracy requirements in the GSD must be compensated by a higher image overlap. The best results are achieved with an image overlap of > 95% and a resolution of > 5 cm. The most important environmental factors have been found to be wind and terrain elevation, which could be an indicator of vegetation density. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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11092 KiB

Open AccessArticle

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

by Kotaro Iizuka, Taichiro Yonehara, Masayuki Itoh and Yoshiko Kosugi

Remote Sens. 2018, 10(1), 13; https://doi.org/10.3390/rs10010013 - 22 Dec 2017

Cited by 116 | Viewed by 15062

Abstract

Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such [...] Read more.

Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such situations, we deployed an unmanned aerial system (UAS) multirotor drone in a cypress forest in a mountainous area of Japan. The structure from motion (SfM) method was used to construct a three-dimensional (3D) model of the forest (tree) structures from aerial photos. Tree height was estimated from the 3D model and compared to in situ ground data. We also analyzed the relationships between a biophysical parameter, diameter at breast height (DBH), of individual trees with canopy width and area measured from orthorectified images. Despite the constraints of ground exposure in a highly dense forest area, tree height was estimated at an accuracy of root mean square error = 1.712 m for observed tree heights ranging from 16 to 24 m. DBH was highly correlated with canopy width (R² = 0.7786) and canopy area (R² = 0.7923), where DBH ranged from 11 to 58 cm. The results of estimating forest parameters indicate that drone-based remote-sensing methods can be utilized to accurately analyze the spatial extent of forest structures. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)

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