Special Issue "Feature Paper Special Issue on Forest Remote Sensing"

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

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Prof. Dr. Peter Krzystek
Website SciProfiles
Guest Editor
Head of Photogrammetry and Remote Sensing Laboratory, Department of Geoinformatics, Munich University of Applied Sciences, Germany
Interests: LiDAR; remote sensing; computer vision; machine learning; forestry; UAV
Special Issues and Collections in MDPI journals
Dr. Juan Guerra Hernandez
Website
Guest Editor
(1) 3edata. Centro de iniciativas empresariais. Fundación CEL. O Palomar s/n, 27004 Lugo, Spain
(2) Forest Research Centre, School of Agriculture, University of Lisbon, Instituto Superior de Agronomia (ISA), Tapada da Ajuda, 1349-017 Lisboa, Portugal
Interests: LiDAR; Remote Sensing; Forest inventory; modeling; Forestry; UAV

Special Issue Information

Dear Colleagues,

Forest remote sensing provides important knowledge to better understand forests and the problems to preserve them as ecosystems, carbon sink, and renewable energy resources. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes.

In this feature paper Special issue, we intend to provide an unique collection of original research work in the field of forest remote sensing that addresses new approaches using remote sensing data at a global, national, regional, and local scale. In particular, we encourage invited scientists to demonstrate the enormous possibilities of advanced methods and technologies for applications in forest resource management. Topics may cover a broad range of new statistical methods and recent instrument developments to gain accurate information on the status and distribution of forest structures over various time scales. Manuscripts will be accepted by the editorial office and editorial board members by invitation only.

Topics may include but are not limited to the following:

  • Monitoring of climate change mitigation in forests;
  • Monitoring of forest health and forest degradation;
  • Biodiversity of forests;
  • Ecosystem service monitoring;
  • Advanced forest inventory;
  • New sensors and platforms for forest applications;
  • Machine learning and deep learning approaches for estimating forest structure attributes;
  • Advanced statistical methods (model-based inference and uncertainty assessment);
  • Multisensor, multitemporal, multiresolution data;
  • Integration and data fusion approaches using multiple remote sensing data (LiDAR, optical, SAR, hyperspectral, multispectral) sources to forest monitoring;
  • Large-scale forest monitoring using LiDAR data with synergies among platforms (airborne, terrestrial, and spaceborne (specially GEDI and ICESat-2 missions) for forest inventory and monitoring.

Procedure

  1. All submissions will be rigorously reviewed according to the Remote Sensing journal guidelines.
  2. Manuscripts that are not suitable for this Special Issue will be notified as soon as after consultation with the editorial board members. Authors of these manuscripts may still consider submitting in the forest remote sensing section or any other section of Remote Sensing as a regular paper. Other manuscripts will be forwarded for review.
  3. Manuscripts that are not selected as feature papers will be notified after the first round of reviews. The selection will be based on the review. Authors of those manuscripts that are not selected for the Special Issue may decide to revise and submit as a regular paper in the forest remote sensing section of the Remote Sensing journal. Please note that authors of these manuscripts need to shoulder the publication fees.
  4. Other manuscripts will be sent for a second round of reviews. However, this does not necessarily mean that a manuscript under the second round of reviews will be published as a feature paper. We will still seek comments and suggestions from reviewers.

Please contact Traey Wu ([email protected]), the section managing editor, if you have any questions.

Prof. Dr. Peter Krzystek
Dr. Juan Guerra Hernandez
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 papers will be 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 2200 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.

Published Papers (5 papers)

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Research

Open AccessFeature PaperArticle
Spatial Agreement among Vegetation Disturbance Maps in Tropical Domains Using Landsat Time Series
Remote Sens. 2020, 12(18), 2948; https://doi.org/10.3390/rs12182948 - 11 Sep 2020
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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Open AccessArticle
Forest Cover Change Pattern after the Intervention of Community Forestry Management System in the Mid-Hill of Nepal: A Case Study
Remote Sens. 2020, 12(17), 2756; https://doi.org/10.3390/rs12172756 - 25 Aug 2020
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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Open AccessFeature PaperArticle
Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning
Remote Sens. 2020, 12(17), 2672; https://doi.org/10.3390/rs12172672 - 19 Aug 2020
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 (ΔDg), 0.81–2.26 m for changes in basal area-weighted mean height (ΔHg), 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, Dg, Hg, 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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Open AccessArticle
Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion
Remote Sens. 2020, 12(15), 2380; https://doi.org/10.3390/rs12152380 - 24 Jul 2020
Cited by 1
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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Open AccessArticle
Drought Impacts on Vegetation in Southeastern Europe
Remote Sens. 2020, 12(13), 2156; https://doi.org/10.3390/rs12132156 - 06 Jul 2020
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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