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Optimizing Forest Inventories with Remote Sensing Techniques

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A special issue of Forests (ISSN 1999-4907).

Deadline for manuscript submissions: closed (31 March 2017) | Viewed by 74973

Special Issue Information

Dear Colleagues,

Knowledge on the state of forest ecosystems is important for evaluating ecosystem services ranging from resources and biodiversity to protection and recreation. Traditionally, forest attributes are collated in the framework of either sample-based terrestrial inventories or terrestrial descriptions of forest stands in the field. Due to the high cost of data collection, small inventories on a forest enterprise level is now particularly rare. However, recent advances in remote sensing technology has enabled provision of spatially explicit information covering large areas. Thus, forest inventories from enterprise level to national forest inventories and even up to global scales will benefit. Remote sensing supports data acquisition and results on all scales: Retrieving forest attributes on plot level (e.g. terrestrial laser scanning), reduction of estimation errors of forest attributes on strata level and enabling the mapping of forest attributes. With this Special Issue, we promote knowledge and strategies for an optimized combination of inventory field data with remote sensing technology. Thus, we encourage contributions from studies covering all fields and scales, including experimental, monitoring and modelling approaches.

Dr. Christian Ginzler
Guest Editor

Manuscript Submission Information

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Keywords

Forest inventory
Remote sensing
Stratification
Small area estimation
Sample plots
Forest management
Forest information

Published Papers (11 papers)

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Research

10990 KiB

Open AccessArticle

Assessing Pine Processionary Moth Defoliation Using Unmanned Aerial Systems

by Adrián Cardil, Udayalakshmi Vepakomma and Lluis Brotons

Forests 2017, 8(10), 402; https://doi.org/10.3390/f8100402 - 23 Oct 2017

Cited by 35 | Viewed by 7429

Abstract

Pine processionary moth (PPM) is one of the most destructive insect defoliators in the Mediterranean for many conifers, causing losses of growth, vitality and eventually the death of trees during outbreaks. There is a growing need for cost-effective monitoring of the temporal and spatial impacts of PPM in forest ecology to better assess outbreak spread patterns and provide guidance on the development of measures targeting the negative impacts of the species on forests, industry and human health. Remote sensing technology mounted on unmanned aerial systems (UASs) with high-resolution image processing has been proposed to assess insect outbreak impacts at local and forest stand levels. Here, we used UAS-acquired RGB imagery in two pine sites to quantify defoliation at the tree-level and to verify the accuracy of the estimates. Our results allowed the identification of healthy, infested and completely defoliated trees and suggested that pine defoliation estimates using UASs are robust and allow high-accuracy (79%) field-based infestation indexes to be derived that are comparable to those used by forest technicians. When compared to current field-based methods, our approach provides PPM impact assessments with an efficient data acquisition method in terms of time and staff, allowing the quantitative estimation of defoliation at tree-level scale. Furthermore, our method could be expanded to a number of situations and scaled up in combination with satellite remote sensing imagery or citizen science approaches. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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4232 KiB

Open AccessArticle

Considerations towards a Novel Approach for Integrating Angle-Count Sampling Data in Remote Sensing Based Forest Inventories

by Melanie Kirchhoefer, Johannes Schumacher, Petra Adler and Gerald Kändler

Forests 2017, 8(7), 239; https://doi.org/10.3390/f8070239 - 05 Jul 2017

Cited by 12 | Viewed by 5642

Abstract

Integration of remote sensing (RS) data in forest inventories for enhancing plot-based forest variable prediction is a widely researched topic. Geometric consistency between forest inventory plots and areas for extraction of RS-based predictive metrics is considered a crucial factor for accurate modelling of forest variables. Achieving geometric consistency is particularly difficult with regard to angle-count sampling (ACS) plots, which have neither distinct shape nor distinct extent. This initial study considers a new approach for integrating ACS and RS data, where the concept of ACS is transferred to RS-based metrics extraction. By using the relationship between tree height and diameter at breast height (DBH), pixels of a RS-based canopy height model are extracted if their value suggests a DBH that would lead to inclusion in an angle-count sample at the given distance to the plot centre. Different variations of this approach are tested by modelling timber volume in national forest inventory plots in Germany. The results are compared to those achieved using fixed-radius plots. A root mean square error of approximately 42% is achieved by both the new and fixed-radius approaches. Therefore, the new approach is not yet considered sufficient for overcoming all difficulties concerning the integration of ACS plot and RS data. However, possibilities for improvement are discussed and will be the subject of further research. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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4001 KiB

Open AccessArticle

Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery

by Wen Zhang, Baoxin Hu, Murray Woods and Glen Brown

Forests 2017, 8(7), 234; https://doi.org/10.3390/f8070234 - 30 Jun 2017

Cited by 8 | Viewed by 5376

Abstract

In this study, we demonstrate the potential of using high spatial resolution airborne imagery to characterize the structural development stages of forest canopies. Four forest succession stages were adopted: stand initiation, young multistory, understory reinitiation, and old growth. Remote sensing metrics describing the spatial patterns of forest structures were derived and a Random Forest learning algorithm was used to classify forest succession stages. These metrics included texture variables from Gray Level Co-occurrence Measures (GLCM), range and sill from the semi-variogram, and the fraction of shadow and its spatial distribution. Among all the derived variables, shadow fractions and the GLCM variables of contrast, mean, and dissimilarity were the most important for characterizing the forest succession stages (classification accuracy of 89%). In addition, a LiDAR (Light Detection and Ranging) derived forest structural index (predicted Lorey’s height) was employed to validate the classification result. The classification using imagery spatial variables was shown to be consistent with the LiDAR derived variable (R² = 0.68 and Root Mean Square Error (RMSE) = 2.39). This study demonstrates that high spatial resolution imagery was able to characterize forest succession stages with promising accuracy and may be considered an alternative to LiDAR data for this kind of application. Also, the results of stand development stages build a framework for future wildlife habitat mapping. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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5790 KiB

Open AccessArticle

Comparing Airborne Laser Scanning, and Image-Based Point Clouds by Semi-Global Matching and Enhanced Automatic Terrain Extraction to Estimate Forest Timber Volume

by Sami Ullah, Matthias Dees, Pawan Datta, Petra Adler and Barbara Koch

Forests 2017, 8(6), 215; https://doi.org/10.3390/f8060215 - 17 Jun 2017

Cited by 24 | Viewed by 6445

Abstract

Information pertaining to forest timber volume is crucial for sustainable forest management. Remotely-sensed data have been incorporated into operational forest inventories to serve the need for ever more diverse and detailed forest statistics and to produce spatially explicit data products. In this study, data derived from airborne laser scanning and image-based point clouds were compared using three volume estimation methods to aid wall-to-wall mapping of forest timber volume. Estimates of forest height and tree density metrics derived from remotely-sensed data are used as explanatory variables, and forest timber volumes based on sample field plots are used as response variables. When compared to data derived from image-based point clouds, airborne laser scanning produced slightly more accurate estimates of timber volume, with a root mean square error (RMSE) of 26.3% using multiple linear regression. In comparison, RMSEs for volume estimates derived from image-based point clouds were 28.3% and 29.0%, respectively, using Semi-Global Matching and enhanced Automatic Terrain Extraction methods. Multiple linear regression was the best-performing parameter estimation method when compared to k-Nearest Neighbour and Support Vector Machine. In many countries, aerial imagery is acquired and updated on regular cycles of 1–5 years when compared to more costly, once-off airborne laser scanning surveys. This study demonstrates point clouds generated from such aerial imagery can be used to enhance the estimation of forest parameters at a stand and forest compartment level-scale using small area estimation methods while at the same time achieving sampling error reduction and improving accuracy at the forest enterprise-level scale. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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3609 KiB

Open AccessArticle

Tree Density and Forest Productivity in a Heterogeneous Alpine Environment: Insights from Airborne Laser Scanning and Imaging Spectroscopy

by Parviz Fatehi, Alexander Damm, Reik Leiterer, Mahtab Pir Bavaghar, Michael E. Schaepman and Mathias Kneubühler

Forests 2017, 8(6), 212; https://doi.org/10.3390/f8060212 - 16 Jun 2017

Cited by 7 | Viewed by 6655

Abstract

We outline an approach combining airborne laser scanning (ALS) and imaging spectroscopy (IS) to quantify and assess patterns of tree density (TD) and forest productivity (FP) in a protected heterogeneous alpine forest in the Swiss National Park (SNP). We use ALS data and a local maxima (LM) approach to predict TD, as well as IS data (Airborne Prism Experiment—APEX) and an empirical model to estimate FP. We investigate the dependency of TD and FP on site related factors, in particular on surface exposition and elevation. Based on reference data (i.e., 1598 trees measured in 35 field plots), we observed an underestimation of ALS-based TD estimates of 40%. Our results suggest a limited sensitivity of the ALS approach to small trees as well as a dependency of TD estimates on canopy heterogeneity, structure, and species composition. We found a weak to moderate relationship between surface elevation and TD (R² = 0.18–0.69) and a less pronounced trend with FP (R² = 0.0–0.56), suggesting that both variables depend on gradients of resource availability. Further to the limitations faced in the sensitivity of the applied approaches, we conclude that the combined application of ALS and IS data was convenient for estimating tree density and mapping FP in north-facing forested areas, however, the accuracy was lower in south-facing forested areas covered with multi-stemmed trees. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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12799 KiB

Open AccessArticle

Terrestrial Laser Scanning for Forest Inventories—Tree Diameter Distribution and Scanner Location Impact on Occlusion

by Meinrad Abegg, Daniel Kükenbrink, Jürgen Zell, Michael E. Schaepman and Felix Morsdorf

Forests 2017, 8(6), 184; https://doi.org/10.3390/f8060184 - 26 May 2017

Cited by 66 | Viewed by 6997

Abstract

The rapid development of portable terrestrial laser scanning (TLS) devices in recent years has led to increased attention to their applicability for forest inventories, especially where direct measurements are very expensive or nearly impossible. However, in terms of precision and reproducibility, there are still some pending questions. In this study, we investigate the influence of stand parameters on the TLS-related visibility in forest plots. We derived 2740 stand parameters from Swiss national forest inventory sample plots. Based on these parameters, we defined virtual scenes of the forest plots with the software “Blender”. Using Blender’s ray-tracing features, we assessed the 3D coverage in a cubic space and 2D visibility properties for each of the virtual plots with different scanner placement schemes. We provide a formula to calculate the maximum number of possible hits for any object size at any distance from a scanner with any resolution. Additionally, we show that the Weibull scale parameter describing a stand, in addition to the number of trees and the mean diameter of the dominant 100 trees per hectare, has a significant and relevant influence on the visibility of the sample plot. Furthermore, we show the effectiveness and the efficiency of 40 scanner location patterns. These experiments demonstrate that intuitively distributing scanner locations evenly within the sample plot, with similar distances between locations and from the edge of the sample plot, provides the best overall visibility of the stand. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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1674 KiB

Open AccessArticle

Comparing Empirical and Semi-Empirical Approaches to Forest Biomass Modelling in Different Biomes Using Airborne Laser Scanner Data

by Endre H. Hansen, Liviu T. Ene, Ernest W. Mauya, Zdeněk Patočka, Tomáš Mikita, Terje Gobakken and Erik Næsset

Forests 2017, 8(5), 170; https://doi.org/10.3390/f8050170 - 16 May 2017

Cited by 12 | Viewed by 5148

Abstract

Airborne laser scanner (ALS) data are used operationally to support field inventories and enhance the accuracy of forest biomass estimates. Modelling the relationship between ALS and field data is a fundamental step of such applications and the quality of the model is essential for the final accuracy of the estimates. Different modelling approaches and variable transformations have been advocated in the existing literature, but comparisons are few or non-existent. In the present study, two main approaches to modelling were compared: the empirical and semi-empirical approaches. Evaluation of model performance was conducted using a conventional evaluation criterion, i.e., the mean square deviation (MSD). In addition, a novel evaluation criterion, the model error (ME), was proposed. The ME was constructed by combining a MSD expression and a model-based variance estimate. For the empirical approach, multiple regression models were developed with two alternative transformation strategies: square root transformation of the response, and natural logarithmic transformation of both response and predictors. For the semi-empirical approach, a nonlinear regression of a power model form was chosen. Two alternative predictor variables, mean canopy height and top canopy height, were used separately. Results showed that the semi-empirical approach resulted in the smallest MSD in three of five study sites. The empirical approach resulted in smaller ME in the temperate and boreal biomes, while the semi-empirical approach resulted in smaller ME in the tropical biomes. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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2456 KiB

Open AccessArticle

Drones as a Tool for Monoculture Plantation Assessment in the Steepland Tropics

by Ethan Miller, Jonathan P. Dandois, Matteo Detto and Jefferson S. Hall

Forests 2017, 8(5), 168; https://doi.org/10.3390/f8050168 - 12 May 2017

Cited by 19 | Viewed by 7316

Abstract

Smallholder tree plantations are expanding in the steepland tropics due to demand for timber and interest in ecosystem services, such as carbon storage. Financial mechanisms are developing to compensate vegetation carbon stores. However, measuring biomass—necessary for accessing carbon funds—at small scales is costly and time-intensive. Therefore, we test whether low-cost drones can accurately estimate height and biomass in monoculture plantations in the tropics. We used Ecosynth, a drone-based structure from motion technique, to build 3D vegetation models from drone photographs. These data were filtered to create a digital terrain model (DTM) and digital surface model (DSM). Two different canopy height models (CHMs) from the Ecosynth DSM were obtained by subtracting terrain elevations from the Ecosynth DTM and a LIDAR DTM. We compared height and biomass derived from these CHMs to field data. Both CHMs accurately predicted the height of all species combined; however, the CHM from the LiDAR DTM predicted heights and biomass on a per-species basis more accurately. Height and biomass estimates were strong for evergreen single-stemmed trees, and unreliable for small leaf-off species during the dry season. This study demonstrates that drones can estimate plantation biomass for select species when used with an accurate DTM. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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8264 KiB

Open AccessArticle

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

by Weili Kou, Changxian Liang, Lili Wei, Alexander J. Hernandez and Xuejing Yang

Forests 2017, 8(2), 34; https://doi.org/10.3390/f8020034 - 29 Jan 2017

Cited by 29 | Viewed by 7318

Abstract

Updated extent, area, and spatial distribution of tropical evergreen forests from inventory data provides valuable knowledge for research of the carbon cycle, biodiversity, and ecosystem services in tropical regions. However, acquiring these data in mountainous regions requires labor-intensive, often cost-prohibitive field protocols. Here, we report about validated methods to rapidly identify the spatial distribution of tropical forests, and obtain accurate extent estimates using phenology-based procedures that integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. Firstly, an analysis of temporal profiles of annual time-series MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) was developed to identify the key phenology phase for extraction of tropical evergreen forests in five typical lands cover types. Secondly, identification signatures of tropical evergreen forests were selected and their related thresholds were calculated based on Landsat NDVI, EVI, and LSWI extracted from ground true samples of different land cover types during the key phenology phase. Finally, a map of tropical evergreen forests was created by a pixel-based thresholding. The developed methods were tested in Xishuangbanna, China, and the results show: (1) Integration of Landsat and MODIS images performs well in extracting evergreen forests in tropical complex mountainous regions. The overall accuracy of the resulting map of the case study was 92%; (2) Annual time series of high-temporal-resolution remote sensing images (MODIS) can effectively be used for identification of the key phenology phase (between Julian Date 20 and 120) to extract tropical evergreen forested areas through analysis of NDVI, EVI, and LSWI of different land cover types; (3) NDVI and LSWI are two effective metrics (NDVI ≥ 0.670 and 0.447 ≥ LSWI ≥ 0.222) to depict evergreen forests from other land cover types during the key phenology phase in tropical complex mountainous regions. This method can make full use of the Landsat and MODIS archives as well as their advantages for tropical evergreen forests geospatial inventories, and is simple and easy to use. This method is suggested for use with other similar regions. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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4939 KiB

Open AccessArticle

Differentiation of Alternate Harvesting Practices Using Annual Time Series of Landsat Data

by Lukas R. Jarron, Txomin Hermosilla, Nicholas C. Coops, Michael A. Wulder, Joanne C. White, Geordie W. Hobart and Donald G. Leckie

Forests 2017, 8(1), 15; https://doi.org/10.3390/f8010015 - 28 Dec 2016

Cited by 27 | Viewed by 8203

Abstract

Sustainable forest management practices allow for a range of harvest prescriptions, including clearcut, clearcut with residual, and partial or selective cutting, which are largely distinguished by the amount of canopy cover removed. The different prescriptions are aimed to emulate natural disturbance, encourage regeneration (seed trees), or offer other ecosystem services, such as the maintenance of local biodiversity or habitat features. Using remotely sensed data, stand-replacing disturbance associated with clearcutting is commonly accurately detected. Novel time series-based change detection products offer an opportunity to determine the capacity to detect and label a wider range of harvest practices. In this research, we demonstrate the capacity of time series imagery, spectral metrics, and related attributed change products, to distinguish between different harvesting practices over a study area in central British Columbia, Canada. Producer’s accuracy of harvest attribution was 79%, with 93% of harvest blocks >5 ha accurately identified. In relation to the amount of canopy cover removed, clearcut harvesting was the most accurately classified (84%), followed by clearcut with residual (79%), and partial cut (64%). Applying detailed spectral metrics derived from Landsat data revealed clearcut and partial cuts to be spectrally distinct. The annual nature of the Landsat time series also offers spatial harvest information within typical, often decadal, forest inventory update cycles. The statistically significant (p < 0.05) relationship between harvest practices and Landsat spectral information indicates a capacity to add increased attribution richness to remote sensing depictions of forest harvest. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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1803 KiB

Open AccessArticle

Direct Measurement of Tree Height Provides Different Results on the Assessment of LiDAR Accuracy

by Emanuele Sibona, Alessandro Vitali, Fabio Meloni, Lucia Caffo, Alberto Dotta, Emanuele Lingua, Renzo Motta and Matteo Garbarino

Forests 2017, 8(1), 7; https://doi.org/10.3390/f8010007 - 23 Dec 2016

Cited by 58 | Viewed by 7168

Abstract

In this study, airborne laser scanning-based and traditional field-based survey methods for tree heights estimation are assessed by using one hundred felled trees as a reference dataset. Comparisons between remote sensing and field-based methods were applied to four circular permanent plots located in the western Italian Alps and established within the Alpine Space project NewFor. Remote sensing (Airborne Laser Scanning, ALS), traditional field-based (indirect measurement, IND), and direct measurement of felled trees (DIR) methods were compared by using summary statistics, linear regression models, and variation partitioning. Our results show that tree height estimates by Airborne Laser Scanning (ALS) approximated to real heights (DIR) of felled trees. Considering the species separately, Larix decidua was the species that showed the smaller mean absolute difference (0.95 m) between remote sensing (ALS) and direct field (DIR) data, followed by Picea abies and Pinus sylvestris (1.13 m and 1.04 m, respectively). Our results cannot be generalized to ALS surveys with low pulses density (<5/m²) and with view angles far from zero (nadir). We observed that the tree heights estimation by laser scanner is closer to actual tree heights (DIR) than traditional field-based survey, and this was particularly valid for tall trees with conical shape crowns. Full article

(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)

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