Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: From Precision Resources Inventory to Operational Forestry

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 44379

Share This Special Issue

Special Issue Editors

Dr. Krzysztof Stereńczak

E-Mail Website
Guest Editor

Department of Geomatics, Forest Research Institute, Braci Leśnej 3 Street, Sękocin Stary, 05-090 Raszyn, Poland
Interests: remote sensing; laser scanning; precision forestry; forest management; forest health
Special Issues, Collections and Topics in MDPI journals

Dr. Hooman Latifi

E-Mail Website
Guest Editor

1. Department of Remote Sensing, University of Würzburg, Würzburg, Germany
2. Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
Interests: ecosystem monitoring; vegetation health; time series remote sensing; LiDAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

High-spatial resolution remote sensing embraces a broad range of data, including airborne, mobile, and terrestrial laser scanning, aerial imagery, unmanned aerial vehicles (UAV), airborne synthetic aperture radar (SAR), and aerial/terrestrial spectroscopy, which, in turn, entail development and adoption of appropriate methodological approaches. All in all, the bundle of those data and methods, together with relevant sampling designs and field surveys for small-scale domains, form the framework for so-called “precision forestry”, with the main objective to maximize information extraction and analysis, mostly based on individual objects in forest ecosystems, for research, monitoring, and management purposes. Nevertheless, the majority of practical applications and monitoring programs entail medium- to large-scale information, mostly on levels of sample plot, parcel or other management units on regular repetition rates. In the context of remote sensing, this would mean shifting, but not necessarily downgrading, from smaller, but high-precision domain (single objects and individuals) to more generalized (pixel or segment) spatial domains while not notably compensating information accuracy. The main questions around this include those concerning:

The spatial extrapolation methods, sampling design, and error propagation studies;
Multidimensional, multiscale, multilevel, and multitemporal RS, especially LIDAR and UAV data analysis for forest management and monitoring purposes;
Implementation of RS, especially LIDAR and UAV-based products in precision forestry.

In this Special Issue of Remote Sensing, we will pursue these and other related issues by hosting contributions presenting state-of-the-art data and methods with a special focus on the applications of remotely-sensed methods in precision and operational forestry. Thus, we invite all colleagues from different parts of the world to contribute to this Special Issue by submitting high-quality relevant works. We particularly welcome submissions in which uncommon methodical approaches have been developed and results were implemented in practical forest management at various scales. This call is also possibly open to communications, meta-analyses, and reviews, provided they are relevant and the detailed structure in which transfer from RS data analysis to operational precision forestry is addressed.

PD Dr. Hooman Latifi
Prof. Krzysztof Stereńczak
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Precision forestry
Operational forestry
High-spatial resolution data
Multitemporal data
Sampling strategy
Error prediction and propagation

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

27 pages, 9647 KiB

Open AccessArticle

Accuracy and Precision of Stem Cross-Section Modeling in 3D Point Clouds from TLS and Caliper Measurements for Basal Area Estimation

by Sarah Witzmann, Laura Matitz, Christoph Gollob, Tim Ritter, Ralf Kraßnitzer, Andreas Tockner, Karl Stampfer and Arne Nothdurft

Remote Sens. 2022, 14(8), 1923; https://doi.org/10.3390/rs14081923 - 15 Apr 2022

Cited by 11 | Viewed by 3394

Abstract

The utilization of terrestrial laser scanning (TLS) data for forest inventory purposes has increasingly gained recognition in the past two decades. Volume estimates from TLS data are usually derived from the integral of cross-section area estimates along the stem axis. The purpose of this study was to compare the performance of circle, ellipse, and spline fits applied to cross-section area modeling, and to evaluate the influence of different modeling parameters on the cross-section area estimation. For this purpose, 20 trees were scanned with FARO Focus^3D X330 and afterward felled to collect stem disks at different heights. The contours of the disks were digitized under in vitro laboratory conditions to provide reference data for the evaluation of the in situ TLS-based cross-section modeling. The results showed that the spline model fit achieved the most precise and accurate estimate of the cross-section area when compared to the reference cross-section area (RMSD (Root Mean Square Deviation) and bias of only 3.66% and 0.17%, respectively) and was able to exactly represent the shape of the stem disk (ratio between intersection and union of modeled and reference cross-section area of 88.69%). In comparison, contour fits with ellipses and circles yielded higher RMSD (5.28% and 10.08%, respectively) and bias (1.96% and 3.27%, respectively). The circle fit proved to be especially robust with respect to varying parameter settings, but provided exact estimates only for regular-shaped stem disks, such as those from the upper parts of the stem. Spline-based models of the cross-section at breast height were further used to examine the influence of caliper orientation on the volume estimation. Simulated caliper measures of the DBH showed an RMSD of 3.99% and a bias of 1.73% when compared to the reference DBH, which was calculated via the reference cross-section area, resulting in biased estimates of basal area and volume. DBH estimates obtained by simulated cross-calipering showed statistically significant deviations from the reference. The findings cast doubt on the customary utilization of manually calipered diameters as reference data when evaluating the accuracy of TLS data, as TLS-based estimates have reached an accuracy level surpassing traditional caliper measures. Full article

(This article belongs to the Special Issue Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: From Precision Resources Inventory to Operational Forestry)

► Show Figures

Graphical abstract

17 pages, 3787 KiB

Open AccessArticle

Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada

by Siddhartha Khare, Annie Deslauriers, Hubert Morin, Hooman Latifi and Sergio Rossi

Remote Sens. 2022, 14(1), 100; https://doi.org/10.3390/rs14010100 - 26 Dec 2021

Cited by 12 | Viewed by 4379

Abstract

Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R² from 0.66 to 0.85) than NDVI (R² from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather. Full article

(This article belongs to the Special Issue Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: From Precision Resources Inventory to Operational Forestry)

► Show Figures

Figure 1

17 pages, 3388 KiB

Open AccessArticle

Investigating the Effects of k and Area Size on Variance Estimation of Multiple Pixel Areas Using a k-NN Technique for Forest Parameters

by Dylan Walshe, Daniel McInerney, João Paulo Pereira and Kenneth A. Byrne

Remote Sens. 2021, 13(22), 4688; https://doi.org/10.3390/rs13224688 - 20 Nov 2021

Cited by 1 | Viewed by 2247

Abstract

Combining auxiliary variables and field inventory data of forest parameters using the model-based approach is frequently used to produce synthetic estimates for small areas. These small areas arise when it may not be financially feasible to take ground measurements or when such areas are inaccessible. Until recently, these estimates have been calculated without providing a measure of the variance when aggregating multiple pixel areas. This paper uses a Random Forest algorithm to produce estimates of quadratic mean diameter at breast height (QMDBH) (cm), basal area (m

^{2}

^{- 1}

), stem density (n/ha

^{- 1}

), and volume (m

^{3}

^{- 1}

), and subsequently estimates the variance of multiple pixel areas using a k-NN technique. The area of interest (AOI) is the state owned commercial forests in the Slieve Bloom mountains in the Republic of Ireland, where the main species are Sitka spruce (Picea sitchensis (Bong.) Carr.) and Lodgepole pine (Pinus contorta Dougl.). Field plots were measured in summer 2018 during which a lidar campaign was flown and Sentinel 2 satellite imagery captured, both of which were used as auxiliary variables. Root mean squared error (RMSE%) and R

^{2}

values for the modelled estimates of QMDBH, basal area, stem density, and volume were 19% (0.70), 22% (0.67), 28% (0.62), and 26% (0.77), respectively. An independent dataset of pre-harvest forest stands was used to validate the modelled estimates. A comparison of measured values versus modelled estimates was carried out for a range of area sizes with results showing that estimated values in areas less than 10–15 ha in size exhibit greater uncertainty. However, as the size of the area increased, the estimated values became increasingly analogous to the measured values for all parameters. The results of the variance estimation highlighted: (i) a greater value of k was needed for small areas compared to larger areas in order to obtain a similar relative standard deviation (RSD) and (ii) as the area increased in size, the RSD decreased, albeit not indefinitely. These results will allow forest managers to better understand how aspects of this variance estimation technique affect the accuracy of the uncertainty associated with parameter estimates. Utilising this information can provide forest managers with inventories of greater accuracy, therefore ensuring a more informed management decision. These results also add further weight to the applicability of the k-NN variance estimation technique in a range of forests landscapes. Full article

(This article belongs to the Special Issue Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: From Precision Resources Inventory to Operational Forestry)

► Show Figures

Figure 1

27 pages, 9701 KiB

Open AccessArticle

A Consumer Grade UAV-Based Framework to Estimate Structural Attributes of Coppice and High Oak Forest Stands in Semi-Arid Regions

by Arvin Fakhri and Hooman Latifi

Remote Sens. 2021, 13(21), 4367; https://doi.org/10.3390/rs13214367 - 29 Oct 2021

Cited by 13 | Viewed by 3488

Abstract

Semi-arid tree covers, in both high and coppice growth forms, play an essential role in protecting water and soil resources and provides multiple ecosystem services across fragile ecosystems. Thus, they require continuous inventories. Quantification of forest structure in these tree covers provides important measures for their management and biodiversity conservation. We present a framework, based on consumer-grade UAV photogrammetry, to separately estimate primary variables of tree height (H) and crown area (A) across diverse coppice and high stands dominated by Quercus brantii Lindl. along the latitudinal gradient of Zagros mountains of western Iran. Then, multivariate linear regressions were parametrized with H and A to estimate the diameter at breast height (DBH) of high trees because of its importance to accelerate the existing practical DBH inventories across Zagros Forests. The estimated variables were finally applied to a model tree aboveground biomass (AGB) for both vegetative growth forms by local allometric equations and Random Forest models. In each step, the estimated variables were evaluated against the field reference values, indicating practically high accuracies reaching root mean square error (RMSE) of 0.68 m and 4.74 cm for H and DBH, as well as relative RMSE < 10% for AGB estimates. The results generally suggest an effective framework for single tree-based attribute estimation over mountainous, semi-arid coppice, and high stands. Full article

(This article belongs to the Special Issue Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: From Precision Resources Inventory to Operational Forestry)

► Show Figures

Figure 1

35 pages, 23179 KiB

Open AccessArticle

Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology

by Christoph Gollob, Tim Ritter, Ralf Kraßnitzer, Andreas Tockner and Arne Nothdurft

Remote Sens. 2021, 13(16), 3129; https://doi.org/10.3390/rs13163129 - 7 Aug 2021

Cited by 103 | Viewed by 19835

Abstract

The estimation of single tree and complete stand information is one of the central tasks of forest inventory. In recent years, automatic algorithms have been successfully developed for the detection and measurement of trees with laser scanning technology. Nevertheless, most of the forest inventories are nowadays carried out with manual tree measurements using traditional instruments. This is due to the high investment costs for modern laser scanner equipment and, in particular, the time-consuming and incomplete nature of data acquisition with stationary terrestrial laser scanners. Traditionally, forest inventory data are collected through manual surveys with calipers or tapes. Practically, this is both labor and time-consuming. In 2020, Apple implemented a Light Detection and Ranging (LiDAR) sensor in the new Apple iPad Pro (4th Gen) and iPhone Pro 12. Since then, access to LiDAR-generated 3D point clouds has become possible with consumer-level devices. In this study, an Apple iPad Pro was tested to produce 3D point clouds, and its performance was compared with a personal laser scanning (PLS) approach to estimate individual tree parameters in different forest types and structures. Reference data were obtained by traditional measurements on 21 circular forest inventory sample plots with a 7 m radius. The tree mapping with the iPad showed a detection rate of 97.3% compared to 99.5% with the PLS scans for trees with a lower diameter at a breast height (dbh) threshold of 10 cm. The root mean square error (RMSE) of the best dbh measurement out of five different dbh modeling approaches was 3.13 cm with the iPad and 1.59 cm with PLS. The data acquisition time with the iPad was approximately 7.51 min per sample plot; this is twice as long as that with PLS but 2.5 times shorter than that with traditional forest inventory equipment. In conclusion, the proposed forest inventory with the iPad is generally feasible and achieves accurate and precise stem counts and dbh measurements with efficient labor effort compared to traditional approaches. Along with future technological developments, it is expected that other consumer-level handheld devices with integrated laser scanners will also be developed beyond the iPad, which will serve as an accurate and cost-efficient alternative solution to the approved but relatively expensive TLS and PLS systems. Such a development would be mandatory to broadly establish digital technology and fully automated routines in forest inventory practice. Finally, high-level progress is generally expected for the broader scientific community in forest ecosystem monitoring, as the collection of highly precise 3D point cloud data is no longer hindered by financial burdens. Full article

(This article belongs to the Special Issue Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: From Precision Resources Inventory to Operational Forestry)

► Show Figures

Figure 1

17 pages, 17409 KiB

Open AccessArticle

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

by Ivan Sačkov, Ivan Barka and Tomáš Bucha

Remote Sens. 2020, 12(24), 4189; https://doi.org/10.3390/rs12244189 - 21 Dec 2020

Cited by 13 | Viewed by 3203

Abstract

Mapping aboveground woody biomass (AGB) on abandoned agricultural land (AAL) is required by relevant stakeholders to monitor the spatial dynamics of farmland afforestation, to assess the carbon sequestration, and to set the appropriate management of natural resources. The objective of this study was, therefore, to present and assess a workflow consisting of (1) the spatial identification of AAL based on a combination of airborne laser scanning (ALS) data, cadastral data, and Land Parcel Identification System data, and (2) the prediction of AGB on AAL using an area-based approach and a nonparametric random forest (RF) model based on a combination of field and ALS data. Part of the second objective was also to evaluate the applicability of (1) the author-developed algorithm for the calculation of ALS metrics and (2) a single comprehensive RF model for the whole area of interest. The study was conducted in the forest management unit Víg?aš (Slovakia, Central Europe) covering a total area of 12,472 ha. Specifically, five reference areas consisting of 11,194 reference points were used to assess the accuracy of the spatial identification of AAL, and seventy-five ground reference plots were used for the development of the ALS-based AGB model and for assessing the accuracy of the AGB map. The overall accuracy of the spatial identification of AAL was found to be 93.00% (Cohen’s kappa = 0.82). The difference between ALS-predicted and ground-observed AGB reached a relative root mean square error (RMSE) at 26.1%, 33.1%, and 21.3% for the whole sample size, plots dominated by shrub species, and plots dominated by tree species, respectively. Full article

(This article belongs to the Special Issue Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: From Precision Resources Inventory to Operational Forestry)

► Show Figures

Graphical abstract

22 pages, 2196 KiB

Open AccessArticle

Digital Aerial Photogrammetry (DAP) and Airborne Laser Scanning (ALS) as Sources of Information about Tree Height: Comparisons of the Accuracy of Remote Sensing Methods for Tree Height Estimation

by Miłosz Mielcarek, Agnieszka Kamińska and Krzysztof Stereńczak

Remote Sens. 2020, 12(11), 1808; https://doi.org/10.3390/rs12111808 - 3 Jun 2020

Cited by 37 | Viewed by 6165

Abstract

The rapid developments in the field of digital aerial photogrammetry (DAP) in recent years have increased interest in the application of DAP data for extracting three-dimensional (3D) models of forest canopies. This technology, however, still requires further investigation to confirm its reliability in estimating forest attributes in complex forest conditions. The main purpose of this study was to evaluate the accuracy of tree height estimation based on a crown height model (CHM) generated from the difference between a DAP-derived digital surface model (DSM) and an airborne laser scanning (ALS)-derived digital terrain model (DTM). The tree heights determined based on the DAP-CHM were compared with ground-based measurements and heights obtained using ALS data only (ALS-CHM). Moreover, tree- and stand-related factors were examined to evaluate the potential influence on the obtained discrepancies between ALS- and DAP-derived heights. The obtained results indicate that the differences between the means of field-measured heights and DAP-derived heights were statistically significant. The root mean square error (RMSE) calculated in the comparison of field heights and DAP-derived heights was 1.68 m (7.34%). The results obtained for the CHM generated using only ALS data produced slightly lower errors, with RMSE = 1.25 m (5.46%) on average. Both ALS and DAP displayed the tendency to underestimate tree heights compared to those measured in the field; however, DAP produced a higher bias (1.26 m) than ALS (0.88 m). Nevertheless, DAP heights were highly correlated with the heights measured in the field (R² = 0.95) and ALS-derived heights (R² = 0.97). Tree species and height difference (the difference between the reference tree height and mean tree height in a sample plot) had the greatest influence on the differences between ALS- and DAP-derived heights. Our study confirms that a CHM computed based on the difference between a DAP-derived DSM and an ALS-derived DTM can be successfully used to measure the height of trees in the upper canopy layer. Full article

(This article belongs to the Special Issue Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: From Precision Resources Inventory to Operational Forestry)

► Show Figures

Journal Menu

Journal Browser

Optimizing the Usages of High-Spatial Resolution Remote Sensing Data: From Precision Resources Inventory to Operational Forestry

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI