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Forest Disturbance Monitoring Using Satellite Remote Sensing

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 31340

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Special Issue Editors

Dr. Janik Deutscher

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Guest Editor

Joanneum Research Forschungsgesellschaft mbH, DIGITAL – Institute for Information and Communication Technologies, Remote Sensing and Geoinformation, Steyrergasse 17, 8010 Graz, Austria
Interests: forest remote sensing; REDD+; forest health; land cover/land use dynamics; optical and SAR data (e.g. Sentinel-1, Sentinel-2); times series analysis; Copernicus services

Dr. Jörg Haarpaintner

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Guest Editor

NORCE—Norwegian Research Centre AS, NORCE Climate, P.O. Box 6434, N-9294 Tromsø, Norway
Interests: application of SAR for environmental and land cover monitoring; time series; time series analysis

Dr. Manuela Hirschmugl

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Guest Editor

1. Institute of Geography and Regional Sciences, University of Graz, Heinrichstr. 36, 8010 Graz, Austria
2. JOANNEUM RESEARCH Forschungsgesellschaft mbH, DIGITAL, Remote Sensing and Geoinformation, Steyrergasse 17, 8010 Graz, Austria
Interests: remote sensing; time series analysis; LiDAR data assessment; forest monitoring; geoinformation technologies
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Dr. Johannes Reiche

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Guest Editor

Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, 6708 PB Wageningen, The Netherlands
Interests: radar remote sensing of forest dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world’s forests host about 80% of terrestrial biodiversity and provide a wide range of economic, social and ecological benefits. Today, we observe a growing pressure on forest ecosystems due to climate change, illegal logging and unsustainable forest management. Monitoring forests by satellite remote sensing allows us to detect forest areas under pressure and helps us to better understand the natural and anthropogenic drivers of forest degradation and deforestation.

In the past few years, we have seen considerable progress in the development of forest monitoring applications based on satellite imagery. The large amount of available open data has fostered the technical development of new methods and the roll-out of near-real-time forest monitoring applications using both optical and SAR data. Over the past ten years, many applications have focused on tropical forests. However, due to drought stress and forest health issues in boreal and temperate forests, the monitoring of these forest areas has gained renewed attention.

This Special Issue invites contributions with a focus on the latest research developments and applications in forest disturbance monitoring using satellite data from the tropics to the boreal region. We especially invite submissions that focus on technical advancements in time series analysis and change detection for forest monitoring, but also manuscripts that focus on operational applications. Submissions shall address any of the following topics:

Technical topics:

Novel methods for time series analysis in forest remote sensing;
Outlier handling in times series analysis;
Change detection methods in forest disturbance monitoring;
AI approaches in forest disturbance monitoring;
Combination of SAR and optical data streams in forest monitoring applications;
Methods to separate and classify different forest disturbance agents (biotic and abiotic);
Operational applications on Earth Observation (EO) platforms.

Thematic topics and applications:

Near-real-time forest monitoring applications;
REDD+ applications;
Disturbance monitoring in different forest and woodland ecosystems (tropical, boreal, temperate forests);
Mapping of forest degradation and selective logging (< 0.1ha);
Forest health monitoring (e.g., drought stress, insect infestation, etc.);
Improved risk modeling (susceptibility to storm damage or bark beetle infestation).

Dr. Janik Deutscher
Dr. Jörg Haarpaintner
Dr. Manuela Hirschmugl
Dr. Johannes Reiche
Guest Editors

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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

forest disturbance
forest degradation
deforestation
forest health
REDD+
near-real-time
time series analysis
change detection

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Published Papers (9 papers)

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Research

19 pages, 5334 KiB

Open AccessArticle

Swidden Agriculture Landscape Mapping Using MODIS Vegetation Index Time Series and Its Spatio-Temporal Dynamics in Northern Laos

by Peng Li and Yin Yang

Remote Sens. 2022, 14(23), 6173; https://doi.org/10.3390/rs14236173 - 6 Dec 2022

Cited by 6 | Viewed by 2641

Abstract

Swidden agriculture or shifting cultivation is still being widely practiced in tropical developing countries and Laos has spared no effort to eradicate it since the mid-1990s. So far, the development of swidden agriculture in this land-locked mountainous country during the 2000–2020 bi-decade remains poorly examined. Moderate-resolution Imaging Spectroradiometer (MODIS) time-series products have shown potential in monitoring vegetative status; however, only extremely limited cases of remote sensing of swidden agriculture landscapes have been reported. Taking northern Laos as a study area and using 2001–2020 MODIS vegetation indices products, the Savitzky–Golay filter, the Mann–Kendall trend test and a threshold method were employed to delineate and monitor annual patterns and dynamics of swidden agriculture landscape at the village level. The results showed that: MODIS Normalized Difference Vegetation Index (NDVI) time series perform better in delineating the temporal development of swidden agriculture. The swidden agriculture landscape has shown a general descending trend in the past decades, especially in the 2010s, with an annual average of 14.70 × 10⁴ ha. The total number of swidden-practicing villages (or districts) also displayed a declining trend and there were 957 villages or 91 districts practicing it continuously between 2001 and 2020. An average of 32 villages per year or two districts per decade highlights the difficulty in ending swidden agriculture in Laos, although the government of Laos has established a number of policies for the eradication of swidden agriculture by 2020. This study provides a necessary methodological reference for monitoring a two-decade evolution and transformation of swidden agriculture in the tropics. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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15 pages, 19659 KiB

Open AccessArticle

Vegetation Landscape Changes and Driving Factors of Typical Karst Region in the Anthropocene

by Mingzhao Yu, Shuai Song, Guizhen He and Yajuan Shi

Remote Sens. 2022, 14(21), 5391; https://doi.org/10.3390/rs14215391 - 27 Oct 2022

Cited by 6 | Viewed by 1871

Abstract

Vegetation degeneration has become a serious ecological problem for karst regions in the Anthropocene. According to the deficiency of long serial and high-resolution analysis of karst vegetation, this paper reconstructed the variation of vegetation landscape changes from 1987 to 2020 in a typical karst region of China. Using Landsat time series data, the dynamic changes and driving factors of natural karst vegetation were identified at the landscape scale. On the premise of considering the time-lag effect, the main climatic factors that influence vegetation growth were presented at the interannual timescale. Then, the approach of residual analysis was adopted to distinguish the dominant factors affecting vegetation growth. Results of trend analysis revealed that 21.5% of the forestland showed an overall significant decline in vegetation growth, while only 1.5% showed an increase in vegetation growth during the study period. Precipitation and radiation were the dominant meteorological factors influencing vegetation at the interannual timescale, as opposed to temperature. More than 70% of the natural vegetation growth was dominated by climatic factors. The area percentage of negative human impact has increased gradually since 2009 and reached 18.5% in 2020, indicating the currently serious situation of vegetation protection; fortunately, in recent years, human disturbances on vegetation have been mitigated in karst areas with the promotion of ecological conservation and restoration projects. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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15 pages, 2767 KiB

Open AccessArticle

Logging Pattern Detection by Multispectral Remote Sensing Imagery in North Subtropical Plantation Forests

by Yue Hu, Zhuna Wang, Yahao Zhang and Yuanyong Dian

Remote Sens. 2022, 14(19), 4987; https://doi.org/10.3390/rs14194987 - 7 Oct 2022

Cited by 1 | Viewed by 2279

Abstract

Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698–713 nm, B5 in Sentinel-2), short-wave infrared band (2100–2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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22 pages, 6241 KiB

Open AccessArticle

Forest Damage by Super Typhoon Rammasun and Post-Disturbance Recovery Using Landsat Imagery and the Machine-Learning Method

by Xu Zhang, Hongbo Jiao, Guangsheng Chen, Jianing Shen, Zihao Huang and Haiyan Luo

Remote Sens. 2022, 14(15), 3826; https://doi.org/10.3390/rs14153826 - 8 Aug 2022

Cited by 3 | Viewed by 2630

Abstract

Typhoon Rammasun landed on the southern coastal region of Guangdong and Hainan Provinces on 18 July 2014, and is the strongest recorded typhoon since the 1970s in China. It caused enormous losses in human lives, property, and crop yields in two provinces; however, its impact on forests and subsequent recovery has not yet been assessed. Here we detected forest damage area and severity from Typhoon Rammasun using Landsat 8 OLI imagery, the Random Forest (RF) machine-learning algorithm, and univariate image differencing (UID) methods, and the controlling factors on damage severity and canopy greenness recovery were further analyzed. The accuracy evaluations against sample plot data indicated that the RF approach can more accurately detect the affected forest area and damage severity than the UID-based methods, with higher overall accuracy (94%), Kappa coefficient (0.92), and regression coefficient (R² = 0.81; p < 0.01). The affected forest area in Guangdong and Hainan was 13,556 km² and 3914 km², accounting for 13.8% and 18.5% total forest area, respectively. The highest affected forest fractions reached 70% in some cities or counties. The proportions of severe damage category accounted for 20.85% and 21.31% of all affected forests in Guangdong and Hainan, respectively. Our study suggests that increasing tree density and choosing less sensitive tree species would reduce damage from typhoons in vulnerable areas such as fringe, scattered, and high-slope forests. The canopy greenness of damaged forests recovered rapidly within three months for both provinces; however, management strategies should still be applied in the severely damaged areas to sustain forest functions since the persistent forest canopy structure and biomass may require a longer time to recover. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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19 pages, 2862 KiB

Open AccessArticle

Mapping Forest Stability within Major Biomes Using Canopy Indices Derived from MODIS Time Series

by Tatiana A. Shestakova, Brendan Mackey, Sonia Hugh, Jackie Dean, Elena A. Kukavskaya, Jocelyne Laflamme, Evgeny G. Shvetsov and Brendan M. Rogers

Remote Sens. 2022, 14(15), 3813; https://doi.org/10.3390/rs14153813 - 8 Aug 2022

Cited by 6 | Viewed by 3479

Abstract

Deforestation and forest degradation from human land use, including primary forest loss, are of growing concern. The conservation of old-growth and other forests with important environmental values is central to many international initiatives aimed at protecting biodiversity, mitigating climate change impacts, and supporting sustainable livelihoods. Current remote-sensing products largely focus on deforestation rather than forest degradation and are dependent on machine learning, calibrated with extensive field measurements. To help address this, we developed a novel approach for mapping forest ecosystem stability, defined in terms of constancy, which is a key characteristic of long-undisturbed (including primary) forests. Our approach categorizes forests into stability classes based on satellite-data time series related to plant water–carbon relationships. Specifically, we used long-term dynamics of the fraction of photosynthetically active radiation intercepted by the canopy (fPAR) and shortwave infrared water stress index (SIWSI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003–2018. We calculated a set of variables from annual time series of fPAR and SIWSI for representative forest regions at opposite ends of Earth’s climatic and latitudinal gradients: boreal forests of Siberia (southern taiga, Russia) and tropical rainforests of the Amazon basin (Kayapó territory, Brazil). Independent validation drew upon high-resolution Landsat imagery and forest cover change data. The results indicate that the proposed approach is accurate and applicable across forest biomes and, thereby, provides a timely and transferrable method to aid in the identification and conservation of stable forests. Information on the location of less stable forests is equally relevant for ecological restoration, reforestation, and proforestation activities. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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16 pages, 10737 KiB

Open AccessArticle

Influence of Charcoal Production on Forest Degradation in Zambia: A Remote Sensing Perspective

by Fernando Sedano, Abel Mizu-Siampale, Laura Duncanson and Mengyu Liang

Remote Sens. 2022, 14(14), 3352; https://doi.org/10.3390/rs14143352 - 12 Jul 2022

Cited by 9 | Viewed by 3576

Abstract

A multitemporal dataset of medium-resolution imagery was used to document a tree cover loss process in three forest reserves of Zambia. This degradation process was attributed to charcoal production with a high degree of certainty, as evidence of kiln scars was found in more than 85% of sites sampled with high-resolution imagery across the study areas. The spatial and temporal pattern of mapped kiln scars exposed an intense and fast-paced degradation process, with kiln densities reaching 2.3 kiln/ha, removal of about 79.3% of the aboveground biomass and reductions of 74.2% of tree cover. The analysis revealed that this forest degradation process progressively moves further away from urban centers. In the year 2010, charcoal production took place around 190 km away from Lusaka, whereas extraction areas in 2020 were located around 350 km from this city. These results underline the negative impact of charcoal production on forest resources and question its characterization as localized and periurban. The post-disturbance LCLUC trajectories of degraded woodlands in forest reserves revealed a partial conversion to agricultural land over time, with less than 25% of these woodlands cultivated seven years after charcoal production. The disaggregation of the supply sources of charcoal for the city of Lusaka based on consumption figures and remote sensing deforestation records showed that the charcoal generated as a byproduct of agricultural expansion is not enough to meet the annual charcoal demand of Lusaka. On the contrary, the majority (65%) of this charcoal is the result of a forest degradation process that alters 197.4 km² of miombo woodlands annually. These findings highlight the role of charcoal production as a direct driver of forest degradation and suggest that forest degradation resulting from charcoal production has surpassed deforestation due to agricultural expansion as the main tree cover loss process in Zambia. These results provide data-driven evidence to improve the characterization of forest degradation resulting from charcoal production across the woodlands of southern Africa and aid the REDD + monitoring, reporting and verification systems in compliance with international reporting commitments. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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18 pages, 1545 KiB

Open AccessArticle

Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data

by Michele Dalponte, Yady Tatiana Solano-Correa, Lorenzo Frizzera and Damiano Gianelle

Remote Sens. 2022, 14(13), 3135; https://doi.org/10.3390/rs14133135 - 29 Jun 2022

Cited by 29 | Viewed by 4340

Abstract

Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle–infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetles. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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19 pages, 5182 KiB

Open AccessArticle

Retrieving the Infected Area of Pine Wilt Disease-Disturbed Pine Forests from Medium-Resolution Satellite Images Using the Stochastic Radiative Transfer Theory

by Xiaoyao Li, Tong Tong, Tao Luo, Jingxu Wang, Yueming Rao, Linyuan Li, Decai Jin, Dewei Wu and Huaguo Huang

Remote Sens. 2022, 14(6), 1526; https://doi.org/10.3390/rs14061526 - 21 Mar 2022

Cited by 13 | Viewed by 2980

Abstract

Pine wilt disease (PWD) is a global destructive threat to forests which has been widely spread and has caused severe tree mortality all over the world. It is important to establish an effective method for forest managers to detect the infected area in a large region. Remote sensing is a feasible tool to detect PWD, but the traditional empirical methods lack the ability to explain the signals and can hardly be extended to large scales. The studies using physically-based models either ignore the within-canopy heterogeneity or rely too much on prior knowledge. In this study, we propose an approach to retrieve PWD infected areas from medium-resolution satellite images of two phases based on the simulations of an extended stochastic radiative transfer model for forests infected by pests (SRTP). A small amount of prior knowledge was used, and a change of background soil was considered in this approach. The performance was evaluated in different study sites. The inversion method performs best in the three-dimensional model LESS simulation sample plots (R² = 0.88, RMSE = 0.059), and the inversion accuracy decreases in the real forest sample plots. For Jiangxi masson pine stand with large coverage and serious damage, R² = 0.57, RMSE = 0.074; and for Shandong black pine stand with sparse and a small number of single plant damage, R² = 0.48, RMSE = 0.063. This study indicates that the SRTP model is more feasible for pest damage inversion over different regions compared with empirical methods. The stochastic radiative transfer theory provides a potential approach for future monitoring of terrestrial vegetation parameters. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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24 pages, 5241 KiB

Open AccessArticle

An Effective Method for InSAR Mapping of Tropical Forest Degradation in Hilly Areas

by Harry Carstairs, Edward T. A. Mitchard, Iain McNicol, Chiara Aquino, Andrew Burt, Médard Obiang Ebanega, Anaick Modinga Dikongo, José-Luis Bueso-Bello and Mathias Disney

Remote Sens. 2022, 14(3), 452; https://doi.org/10.3390/rs14030452 - 18 Jan 2022

Cited by 10 | Viewed by 3674

Abstract

Current satellite remote sensing methods struggle to detect and map forest degradation, which is a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height (

h_{ϕ}

) is a promising [...] Read more.

h_{ϕ}

) is a promising variable for measuring forest disturbances, as it is closely related to the mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrains, despite the fact that much of the world’s remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between

h_{ϕ}

and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising multilooking prior to the calculation of

h_{ϕ}

strengthens this relationship, and that degradation estimates across steep slopes in the surrounding region are improved by selecting data from the most appropriate pass directions on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available. Full article

(This article belongs to the Special Issue Forest Disturbance Monitoring Using Satellite Remote Sensing)

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