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27 pages, 4238 KiB

Open AccessArticle

Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas

by Jennifer N. Hird, Jahan Kariyeva and Gregory J. McDermid

Remote Sens. 2021, 13(23), 4745; https://doi.org/10.3390/rs13234745 - 23 Nov 2021

Cited by 13 | Viewed by 6063

Abstract

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access [...] Read more.

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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18 pages, 3436 KiB

Open AccessArticle

Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

by Jessica Esteban, Alfredo Fernández-Landa, José Luis Tomé, Cristina Gómez and Miguel Marchamalo

Remote Sens. 2021, 13(18), 3611; https://doi.org/10.3390/rs13183611 - 10 Sep 2021

Cited by 9 | Viewed by 2516

Abstract

Understanding forest dynamics at the stand level is crucial for sustainable management. Landsat time series have been shown to be effective for identification of drastic changes, such as natural disturbances or clear-cuts, but detecting subtle changes requires further research. Time series of six [...] Read more.

Understanding forest dynamics at the stand level is crucial for sustainable management. Landsat time series have been shown to be effective for identification of drastic changes, such as natural disturbances or clear-cuts, but detecting subtle changes requires further research. Time series of six Landsat-derived vegetation indexes (VIs) were analyzed with the BFAST (Breaks for Additive Season and Trend) algorithm aiming to characterize the changes resulting from harvesting practices of different intensities (clear-cutting, cutting with seed-trees, and thinning) in a Mediterranean forest area of Spain. To assess the contribution of airborne laser scanner (ALS) data and the potential implications of it being after or before the detected changes, two scenarios were defined (based on the year in which ALS data were acquired (2010), and thereby detecting changes from 2005 to 2010 (before ALS data) and from 2011 to 2016 (after ALS data). Pixels identified as change by BFAST were attributed with change in VI intensity and ALS-derived statistics (99th height percentile and forest canopy cover) for classification with random forests, and derivation of change maps. Fusion techniques were applied to leverage the potential of each individual VI change map and to reduce mapping errors. The Tasseled Cap Brightness (TCB) and Normalized Burn Ratio (NBR) indexes provided the most accurate results, the latter being more precise for thinning detection. Our results demonstrate the suitability of Landsat time series and ALS data to characterize forest stand changes caused by harvesting practices of different intensity, with improved accuracy when ALS data is acquired after the change occurs. Clear-cuttings were more readily detectable compared to cutting with seed-trees and thinning, detection of which required fusion approaches. This methodology could be implemented to produce annual cartography of harvesting practices, enabling more accurate statistics and spatially explicit identification of forest operations. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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18 pages, 3442 KiB

Open AccessFeature PaperArticle

Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

by Jonathon J. Donager, Andrew J. Sánchez Meador and Ryan C. Blackburn

Remote Sens. 2021, 13(12), 2297; https://doi.org/10.3390/rs13122297 - 11 Jun 2021

Cited by 34 | Viewed by 7628

Abstract

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in [...] Read more.

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R² for field observed and lidar-derived basal area, m² ha⁻¹, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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20 pages, 3801 KiB

Open AccessFeature PaperArticle

Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

by Iván Dorado-Roda, Adrián Pascual, Sergio Godinho, Carlos A. Silva, Brigite Botequim, Pablo Rodríguez-Gonzálvez, Eduardo González-Ferreiro and Juan Guerra-Hernández

Remote Sens. 2021, 13(12), 2279; https://doi.org/10.3390/rs13122279 - 10 Jun 2021

Cited by 69 | Viewed by 9222

Abstract

Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area [...] Read more.

Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km² in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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22 pages, 4440 KiB

Open AccessFeature PaperArticle

Shedding New Light on Mountainous Forest Growth: A Cross-Scale Evaluation of the Effects of Topographic Illumination Correction on 25 Years of Forest Cover Change across Nepal

by Jamon Van Den Hoek, Alexander C. Smith, Kaspar Hurni, Sumeet Saksena and Jefferson Fox

Remote Sens. 2021, 13(11), 2131; https://doi.org/10.3390/rs13112131 - 28 May 2021

Cited by 16 | Viewed by 5244

Abstract

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, [...] Read more.

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016). TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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19 pages, 6739 KiB

Open AccessFeature PaperArticle

An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

by Martina Deur, Mateo Gašparović and Ivan Balenović

Remote Sens. 2021, 13(10), 1868; https://doi.org/10.3390/rs13101868 - 11 May 2021

Cited by 19 | Viewed by 3868

Abstract

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the [...] Read more.

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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20 pages, 1103 KiB

Open AccessFeature PaperArticle

Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping

by Sergio Revilla, María Teresa Lamelas, Darío Domingo, Juan de la Riva, Raquel Montorio, Antonio Luis Montealegre and Alberto García-Martín

Remote Sens. 2021, 13(3), 342; https://doi.org/10.3390/rs13030342 - 20 Jan 2021

Cited by 10 | Viewed by 2673

Abstract

Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of [...] Read more.

Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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22 pages, 1979 KiB

Open AccessFeature PaperArticle

Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires

by Darío Domingo, Juan de la Riva, María Teresa Lamelas, Alberto García-Martín, Paloma Ibarra, Maite Echeverría and Raúl Hoffrén

Remote Sens. 2020, 12(21), 3660; https://doi.org/10.3390/rs12213660 - 8 Nov 2020

Cited by 33 | Viewed by 4617

Abstract

Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest [...] Read more.

Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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23 pages, 4123 KiB

Open AccessFeature PaperArticle

Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia

by Habitamu Taddese, Zerihun Asrat, Ingunn Burud, Terje Gobakken, Hans Ole Ørka, Øystein B. Dick and Erik Næsset

Remote Sens. 2020, 12(20), 3335; https://doi.org/10.3390/rs12203335 - 13 Oct 2020

Cited by 14 | Viewed by 4004

Abstract

Periodic assessment of forest aboveground biomass (AGB) is essential to regulate the impacts of the changing climate. However, AGB estimation using field-based sample survey (FBSS) has limited precision due to cost and accessibility constraints. Fortunately, remote sensing technologies assist to improve AGB estimation [...] Read more.

Periodic assessment of forest aboveground biomass (AGB) is essential to regulate the impacts of the changing climate. However, AGB estimation using field-based sample survey (FBSS) has limited precision due to cost and accessibility constraints. Fortunately, remote sensing technologies assist to improve AGB estimation precisions. Thus, this study assessed the role of remotely sensed (RS) data in improving the precision of AGB estimation in an Afromontane forest in south-central Ethiopia. The research objectives were to identify RS variables that are useful for estimating AGB and evaluate the extent of improvement in the precision of the remote sensing-assisted AGB estimates beyond the precision of a pure FBSS. Reference AGB data for model calibration and estimation were collected from 111 systematically distributed circular sample plots (SPs) of 1000 m² area. Independent variables were derived from Landsat-8, Sentinel-2 and PlanetScope images acquired in January 2019. The area-weighted mean and standard deviation of the spectral reflectance, spectral index and texture (only for PlanetScope) variables were extracted for each SP. A maximum of two independent variables from each image type was fitted to a generalized linear model for AGB estimation using model-assisted estimators. The results of this study revealed that the Landsat-8 model with the predictor variable of shortwave infrared band reflectance and the PlanetScope model with the predictor variable of green band reflectance had estimation efficiency of 1.40 and 1.37, respectively. Similarly, the Sentinel-2 model, which had predictor variables of shortwave infrared reflectance and standard deviation of green leaf index, improved AGB estimation with the relative efficiency of 1.68. Utilizing freely available Sentinel-2 data seems to enhance the AGB estimation efficiency and reduce cost and extensive fieldwork in inaccessible areas. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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17 pages, 8078 KiB

Open AccessFeature PaperArticle

Comparison of Conventional Change Detection Methodologies Using High-Resolution Imagery to Find Forest Damage Caused by Typhoons

by Flavio Furukawa, Junko Morimoto, Nobuhiko Yoshimura and Masami Kaneko

Remote Sens. 2020, 12(19), 3242; https://doi.org/10.3390/rs12193242 - 6 Oct 2020

Cited by 18 | Viewed by 4106

Abstract

The number of intense tropical cyclones is expected to increase in the future, causing severe damage to forest ecosystems. Remote sensing plays an important role in detecting changes in land cover caused by these tropical storms. Remote sensing techniques have been widely used [...] Read more.

The number of intense tropical cyclones is expected to increase in the future, causing severe damage to forest ecosystems. Remote sensing plays an important role in detecting changes in land cover caused by these tropical storms. Remote sensing techniques have been widely used in different phases of disaster risk management because they can deliver information rapidly to the concerned parties. Although remote sensing technology is already available, an examination of appropriate methods based on the type of disaster is still missing. Our goal is to compare the suitability of three different conventional classification methods for fast and easy change detection analysis using high-spatial-resolution and high-temporal-resolution remote sensing imagery to identify areas with windthrow and landslides caused by typhoons. In August 2016, four typhoons hit Hokkaido, the northern island of Japan, creating large areas of windthrow and landslides. We compared the normalized difference vegetation index (NDVI) filtering method, the spectral angle mapper (SAM) method, and the support vector machine (SVM) method to identify windthrow and landslides in two different study areas in southwestern Hokkaido. These methodologies were evaluated using PlanetScope data with a resolution of 3 m/px and validated with reference data based on Worldview2 data with a very high resolution of 0.46 m/px. The results showed that all three methods, when applied to high-spatial-resolution imagery, can deliver sufficient results for windthrow and landslide detection. In particular, the SAM method performed better at windthrow detection, and the NDVI filtering method performed better at landslide detection. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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16 pages, 2479 KiB

Open AccessFeature PaperArticle

From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure

by Nicolò Camarretta, Peter A. Harrison, Arko Lucieer, Brad M. Potts, Neil Davidson and Mark Hunt

Remote Sens. 2020, 12(19), 3184; https://doi.org/10.3390/rs12193184 - 29 Sep 2020

Cited by 32 | Viewed by 5167

Abstract

The use of unmanned aerial vehicles (UAVs) for remote sensing of natural environments has increased over the last decade. However, applications of this technology for high-throughput individual tree phenotyping in a quantitative genetic framework are rare. We here demonstrate a two-phased analytical pipeline [...] Read more.

The use of unmanned aerial vehicles (UAVs) for remote sensing of natural environments has increased over the last decade. However, applications of this technology for high-throughput individual tree phenotyping in a quantitative genetic framework are rare. We here demonstrate a two-phased analytical pipeline that rapidly phenotypes and filters for genetic signals in traditional and novel tree productivity and architectural traits derived from ultra-dense light detection and ranging (LiDAR) point clouds. The goal of this study was rapidly phenotype individual trees to understand the genetic basis of ecologically and economically significant traits important for guiding the management of natural resources. Individual tree point clouds were acquired using UAV-LiDAR captured over a multi-provenance common-garden restoration field trial located in Tasmania, Australia, established using two eucalypt species (Eucalyptus pauciflora and Eucalyptus tenuiramis). Twenty-five tree productivity and architectural traits were calculated for each individual tree point cloud. The first phase of the analytical pipeline found significant species differences in 13 of the 25 derived traits, revealing key structural differences in productivity and crown architecture between species. The second phase investigated the within species variation in the same 25 structural traits. Significant provenance variation was detected for 20 structural traits in E. pauciflora and 10 in E. tenuiramis, with signals of divergent selection found for 11 and 7 traits, respectively, putatively driven by the home-site environment shaping the observed variation. Our results highlight the genetic-based diversity within and between species for traits important for forest structure, such as crown density and structural complexity. As species and provenances are being increasingly translocated across the landscape to mitigate the effects of rapid climate change, our results that were achieved through rapid phenotyping using UAV-LiDAR, raise the need to understand the functional value of productivity and architectural traits reflecting species and provenance differences in crown structure and the interplay they have on the dependent biotic communities. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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21 pages, 6300 KiB

Open AccessFeature PaperArticle

Spatial Agreement among Vegetation Disturbance Maps in Tropical Domains Using Landsat Time Series

by Inacio T. Bueno, Greg J. McDermid, Eduarda M. O. Silveira, Jennifer N. Hird, Breno I. Domingos and Fausto W. Acerbi Júnior

Remote Sens. 2020, 12(18), 2948; https://doi.org/10.3390/rs12182948 - 11 Sep 2020

Cited by 10 | Viewed by 3661

Abstract

Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there is a large range of datasets for monitoring, analyzing, [...] Read more.

Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there is a large range of datasets for monitoring, analyzing, and detecting disturbances, many methods have been well-studied and successfully implemented. However, factors such as the vegetation type, input data, and change detection method can significantly alter the outcomes of a disturbance-detection study. We evaluated the spatial agreement of disturbance maps provided by the Breaks For Additive Season and Trend (BFAST) algorithm, evaluating seven spectral indices in three distinct vegetation domains in Brazil: Atlantic forest, savanna, and semi-arid woodland, by assessing levels of agreement between the outputs. We computed individual map accuracies based on a reference dataset, then ranked their performance, while also observing their relationships with specific vegetation domains. Our results indicated a low rate of spatial agreement among index-based disturbance maps, which itself was minimally influenced by vegetation domain. Wetness indices produced greater detection accuracies in comparison to greenness-related indices free of saturation. The normalized difference moisture index performed best in the Atlantic forest domains, yet performed poorest in semi-arid woodland, reflecting its specific sensitivity to vegetation and its water content. The normalized difference vegetation index led to high disturbance detection accuracies in the savanna and semi-arid woodland domains. This study offered novel insight into vegetation disturbance maps, their relationship to different ecosystem types, and corresponding accuracies. Distinct input data can produce non-spatially correlated disturbance maps and reflect site-specific sensitivity. Future research should explore algorithm limitations presented in this study, as well as the expansion to other techniques and vegetation domains across the globe. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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18 pages, 9444 KiB

Open AccessFeature PaperArticle

Forest Cover Change Pattern after the Intervention of Community Forestry Management System in the Mid-Hill of Nepal: A Case Study

by Shankar Tripathi, Rajan Subedi and Hari Adhikari

Remote Sens. 2020, 12(17), 2756; https://doi.org/10.3390/rs12172756 - 25 Aug 2020

Cited by 25 | Viewed by 6335

Abstract

An account of widespread degradation and deforestation in Nepal has been noticed in various literature sources. Although the contribution of community forests (CF) on the improvement of forest cover and condition in the Mid-hill of Nepal is positive, detailed study to understand the [...] Read more.

An account of widespread degradation and deforestation in Nepal has been noticed in various literature sources. Although the contribution of community forests (CF) on the improvement of forest cover and condition in the Mid-hill of Nepal is positive, detailed study to understand the current situation seems important. The study area (Tanahun District) lies in the Gandaki Province of western Nepal. The objective of this study was to estimate the forest cover change over the specified period and to identify factors influencing the change. We used Landsat images from the years 1976, 1991, and 2015 to classify land use and land cover. We considered community perception in addition to the forest cover map to understand the different causes of forest cover change. Forest cover decreased from 1976 to 1991 annually at a rate of 0.96%. After 1991, the forest increased annually at a rate of 0.63%. The overall forest cover in the district regained its original status. Factors related to increasing forest cover were emigration, occupation shift, agroforestry practices, as well as particularly by plantation on barren lands, awareness among forest users, and conservation activities conducted by local inhabitants after the government forest was handed over to community members as a community forest management system. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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20 pages, 2373 KiB

Open AccessFeature PaperArticle

Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

by Tuomas Yrttimaa, Ville Luoma, Ninni Saarinen, Ville Kankare, Samuli Junttila, Markus Holopainen, Juha Hyyppä and Mikko Vastaranta

Remote Sens. 2020, 12(17), 2672; https://doi.org/10.3390/rs12172672 - 19 Aug 2020

Cited by 16 | Viewed by 4990

Abstract

Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using [...] Read more.

Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS in quantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindrical characteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and forest stands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99–1.22 cm for diameter at breast height (Δdbh), 44.14–55.49 cm² for basal area (Δg), and 1.91–4.85 m for tree height (Δh). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60–1.13 cm was recorded for changes in basal area-weighted mean diameter (ΔD_g), 0.81–2.26 m for changes in basal area-weighted mean height (ΔH_g), 1.40–2.34 m²/ha for changes in mean basal area (ΔG), and 74–193 n/ha for changes in the number of trees per hectare (ΔTPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, D_g, H_g, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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23 pages, 5649 KiB

Open AccessFeature PaperArticle

Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion

by Pedro Rodríguez-Veiga, Joao Carreiras, Thomas Luke Smallman, Jean-François Exbrayat, Jamleck Ndambiri, Faith Mutwiri, Divinah Nyasaka, Shaun Quegan, Mathew Williams and Heiko Balzter

Remote Sens. 2020, 12(15), 2380; https://doi.org/10.3390/rs12152380 - 24 Jul 2020

Cited by 9 | Viewed by 6582

Abstract

The characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. Here we [...] Read more.

The characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. Here we present the most detailed and rigorous national-scale assessment of aboveground woody biomass carbon stocks and dynamics for Kenya to date. A non-parametric random forest algorithm was trained to retrieve aboveground woody biomass carbon (AGBC) for the year 2014 ± 1 and forest disturbances for the 2014–2017 period using in situ forest inventory plot data and satellite Earth Observation (EO) data. The ecosystem carbon cycling of Kenya’s forests and wooded grassland were assessed using a model-data fusion framework, CARDAMOM, constrained by the woody biomass datasets from this study as well as time series information on leaf area, fire events and soil organic carbon. Our EO-derived AGBC stocks were estimated as 140 Mt C for forests and 199 Mt C for wooded grasslands. The total AGBC loss during the study period was estimated as 1.89 Mt C with a dispersion below 1%. The CARDAMOM analysis estimated woody productivity to be three times larger in forests (mean = 1.9 t C ha⁻¹ yr⁻¹) than wooded grasslands (0.6 t C ha⁻¹ yr⁻¹), and the mean residence time of woody C in forests (16 years) to be greater than in wooded grasslands (10 years). This study stresses the importance of carbon sequestration by forests in the international climate mitigation efforts under the Paris Agreement, but emphasizes the need to include non-forest ecosystems such as wooded grasslands in international greenhouse gas accounting frameworks. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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21 pages, 8676 KiB

Open AccessFeature PaperArticle

Drought Impacts on Vegetation in Southeastern Europe

by Patrícia Páscoa, Célia M. Gouveia, Ana C. Russo, Roxana Bojariu, Sergio M. Vicente-Serrano and Ricardo M. Trigo

Remote Sens. 2020, 12(13), 2156; https://doi.org/10.3390/rs12132156 - 6 Jul 2020

Cited by 20 | Viewed by 5093

Abstract

We evaluated the response of vegetation’s photosynthetic activity to drought conditions from 1998 to 2014 over Romania and the Republic of Moldova. The connection between vegetation stress and drought events was assessed by means of a correlation analysis between the monthly Standardized Precipitation [...] Read more.

We evaluated the response of vegetation’s photosynthetic activity to drought conditions from 1998 to 2014 over Romania and the Republic of Moldova. The connection between vegetation stress and drought events was assessed by means of a correlation analysis between the monthly Standardized Precipitation Evaporation Index (SPEI), at several time scales, and the Normalized Difference Vegetation Index (NDVI), as well as an assessment of the simultaneous occurrence of extremes in both indices. The analysis of the relationship between drought and vegetation was made for the growing season (from April to October of the entire period), and special attention was devoted to the severe drought event of 2000/2001, considered as the driest since 1961 for the study area. More than three quarters (77%) of the agricultural land exhibits a positive correlation between the two indices. The sensitivity of crop areas to drought is strong, as the impacts were detected from May to October, with a peak in July. On the other hand, forests were found to be less sensitive to drought, as the impacts were limited mostly to July and August. Moreover, vegetation of all land cover classes showed a dependence between the sign of the correlation and the elevation gradient. Roughly 60% (20%) of the study domain shows a concordance of anomalously low vegetation activity with dry conditions of at least 50% (80%) in August. By contrast, a lower value of concordance was observed over the Carpathian Mountains. During the severe drought event of 2000/2001, a decrease in vegetation activity was detected for most of the study area, showing a decrease lasting at least 4 months, between April and October, for more than two thirds (71%) of the study domain. Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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43 pages, 20227 KiB

Open AccessFeature PaperReview

Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review

by Yago Diez, Sarah Kentsch, Motohisa Fukuda, Maximo Larry Lopez Caceres, Koma Moritake and Mariano Cabezas

Remote Sens. 2021, 13(14), 2837; https://doi.org/10.3390/rs13142837 - 19 Jul 2021

Cited by 84 | Viewed by 12454

Abstract

Forests are the planet’s main CO

_{2}

filtering agent as well as important economical, environmental and social assets. Climate change is exerting an increased stress, resulting in a need for improved research methodologies to study their health, composition or evolution. Traditionally, information about [...] Read more.

Forests are the planet’s main CO

_{2}

filtering agent as well as important economical, environmental and social assets. Climate change is exerting an increased stress, resulting in a need for improved research methodologies to study their health, composition or evolution. Traditionally, information about forests has been collected using expensive and work-intensive field inventories, but in recent years unoccupied autonomous vehicles (UAVs) have become very popular as they represent a simple and inexpensive way to gather high resolution data of large forested areas. In addition to this trend, deep learning (DL) has also been gaining much attention in the field of forestry as a way to include the knowledge of forestry experts into automatic software pipelines tackling problems such as tree detection or tree health/species classification. Among the many sensors that UAVs can carry, RGB cameras are fast, cost-effective and allow for straightforward data interpretation. This has resulted in a large increase in the amount of UAV-acquired RGB data available for forest studies. In this review, we focus on studies that use DL and RGB images gathered by UAVs to solve practical forestry research problems. We summarize the existing studies, provide a detailed analysis of their strengths paired with a critical assessment on common methodological problems and include other information, such as available public data and code resources that we believe can be useful for researchers that want to start working in this area. We structure our discussion using three main families of forestry problems: (1) individual Tree Detection, (2) tree Species Classification, and (3) forest Anomaly Detection (forest fires and insect Infestation). Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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