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Remote Sensing of Forest Health

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 July 2016) | Viewed by 173501

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Special Issue Editors

Dr. Angela Lausch

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Guest Editor

Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), Permoserstr.15, D-04318 Leipzig, Germany
Interests: remote sensing; functional traits; biodiversity; data mining
Special Issues, Collections and Topics in MDPI journals

Dr. Marco Heurich

E-Mail Website1 Website2
Guest Editor

1. Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, Freyunger Str. 2, 94481 Grafenau, Germany
2.Chair of Wildlife Ecology and Management, University of Freiburg, Tennenbacher Straße 4, 79106 Freiburg, Germany
Interests: lidar applications in forest ecology and management; remote sensing in wildlife ecology; essential biodiversity variables
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The significance of forest ecosystems, their role in ecosystem processes and services, their functioning and impacts on humanity is partially well understood. Increasing anthropogenic pressure on ecosystems, the exploitation of natural resources as well as pressure from a constantly expanding population and economic growth continue to put a strain on and irretrievably threaten global forest ecosystems.

Traditional approaches to forest monitoring have not been able to deliver a comprehensive, global and comparable monitoring system of forest ecosystems, their state and changes to such systems on different spatial, temporal and scaling levels.

In our rapidly changing environment and landscape, there is an increased focus on measuring, quantifying and modeling the state of our forests in a high temporal resolution based on space and airborne remote-sensing techniques. The great interest in using EO for mapping forest health is also driven by the fact that novel EO sensors will soon be available, such as the hyperspectral satellite EnMAP (to be launched in 2018), the ESA satellite FLEX (Fluorescence Explorer, to be launched in 2018), the laser-based instrument GEDI – Global Ecosystem Dynamics Investigation or LiDAR from NASA (to be launched in 2019), that will enable large-scale, long-term, standardized and spatially complete, continuous as well as affordable information that can be used for mapping, modelling and forecasting forest health, which is currently covered by comparable airborne-based sensors such as HySpex, AISA, APEX, LiDAR or thermal infrared sensors in space. Simultaneously, as a result of the increasing openness of Landsat data and Spot data archives, there is also the immediate and freely available remote-sensing data from the Copernicus Mission (sentinel 1-5) or EnMAP data and remote-sensing data products.

In order to compile existing research using remote-sensing techniques in the field of forest mapping, we would like to invite you to submit articles about your recent research with respect to the following topics:

Remote Sensing of forest health: Methods for assessment and monitoring of forest mortality
Remote Sensing: Spectral indicators for assessing of forest health
Remote Sensing of forest health and protection
Remote Sensing of forest health: Utilitarian and ecosystem services perspective
Remote Sensing of forest health: Modelling and prognosis of pest infestation
Monitoring structural and functional forest biodiversity indicators in context of forest heath with Remote Sensing
Remote Sensing of forest structure: Physically based modelling for better mapping and quantification forest heath
Remote Sensing: Roles of climate, air pollution, anthropogen pressures and disturbances on forest heath by remote sensing
Remote Sensing of forest health: Enhanced forest inventory by remote sensing
Comparison and evaluation of different remote sensing sensors and methods for assessing forest health.
Improvement and evaluation of input data needed for the retrieval of forest health by remote sensing
Review articles covering one or more of these topics are also welcome.
Experiments for mapping forest health by remote sensing
Forest resilience and global change processes by remote sensing: Monitoring the effects of air pollution and soil acidification

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Dr. Angela Lausch
Dr. Marco Heurich
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.

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Published Papers (13 papers)

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10960 KiB

Open AccessArticle

Multisource Remote Sensing Imagery Fusion Scheme Based on Bidimensional Empirical Mode Decomposition (BEMD) and Its Application to the Extraction of Bamboo Forest

by Guang Liu, Lei Li, Hui Gong, Qingwen Jin, Xinwu Li, Rui Song, Yun Chen, Yu Chen, Chengxin He, Yuqing Huang and Yuefeng Yao

Remote Sens. 2017, 9(1), 19; https://doi.org/10.3390/rs9010019 - 29 Dec 2016

Cited by 20 | Viewed by 6867

Abstract

Most bamboo forests grow in humid climates in low-latitude tropical or subtropical monsoon areas, and they are generally located in hilly areas. Bamboo trunks are very straight and smooth, which means that bamboo forests have low structural diversity. These features are beneficial to synthetic aperture radar (SAR) microwave penetration and they provide special information in SAR imagery. However, some factors (e.g., foreshortening) can compromise the interpretation of SAR imagery. The fusion of SAR and optical imagery is considered an effective method with which to obtain information on ground objects. However, most relevant research has been based on two types of remote sensing image. This paper proposes a new fusion scheme, which combines three types of image simultaneously, based on two fusion methods: bidimensional empirical mode decomposition (BEMD) and the Gram-Schmidt transform. The fusion of panchromatic and multispectral images based on the Gram-Schmidt transform can enhance spatial resolution while retaining multispectral information. BEMD is an adaptive decomposition method that has been applied widely in the analysis of nonlinear signals and to the nonstable signal of SAR. The fusion of SAR imagery with fused panchromatic and multispectral imagery using BEMD is based on the frequency information of the images. It was established that the proposed fusion scheme is an effective remote sensing image interpretation method, and that the value of entropy and the spatial frequency of the fused images were improved in comparison with other techniques such as the discrete wavelet, à-trous, and non-subsampled contourlet transform methods. Compared with the original image, information entropy of the fusion image based on BEMD improves about 0.13–0.38. Compared with the other three methods it improves about 0.06–0.12. The average gradient of BEMD is 4%–6% greater than for other methods. BEMD maintains spatial frequency 3.2–4.0 higher than other methods. The experimental results showed the proposed fusion scheme could improve the accuracy of bamboo forest classification. Accuracy increased by 12.1%, and inaccuracy was reduced by 11.0%. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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4065 KiB

Open AccessArticle

Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

by Feng R. Zhao, Ran Meng, Chengquan Huang, Maosheng Zhao, Feng A. Zhao, Peng Gong, Le Yu and Zhiliang Zhu

Remote Sens. 2016, 8(11), 898; https://doi.org/10.3390/rs8110898 - 29 Oct 2016

Cited by 40 | Viewed by 9979

Abstract

Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. Although the disturbance products (tracking the conversion from forest to non-forest type) derived using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been validated extensively for mapping forest disturbances across the United States, the ability of this approach to characterize long-term post-disturbance recovery (the conversion from non-forest to forest) has yet to be assessed. In this study, the LTSS-VCT approach was applied to examine long-term (up to 24 years) post-disturbance forest spectral recovery following stand-clearing disturbances (fire and harvests) in the Greater Yellowstone Ecosystem (GYE). Using high spatial resolution images from Google Earth, we validated the detectable forest recovery status mapped by VCT by year 2011. Validation results show that the VCT was able to map long-term post-disturbance forest recovery with overall accuracy of ~80% for different disturbance types and forest types in the GYE. Harvested areas in the GYE have higher percentages of forest recovery than burned areas by year 2011, and National Forests land generally has higher recovery rates compared with National Parks. The results also indicate that forest recovery is highly related with forest type, elevation and environmental variables such as soil type. Findings from this study can provide valuable insights for ecosystem modeling that aim to predict future carbon dynamics by integrating fine scale forest recovery conditions in GYE, in the face of climate change. With the availability of the VCT product nationwide, this approach can also be applied to examine long-term post-disturbance forest recovery in other study regions across the U.S. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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8546 KiB

Open AccessArticle

Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images

by Jinghui Meng, Shiming Li, Wei Wang, Qingwang Liu, Shiqin Xie and Wu Ma

Remote Sens. 2016, 8(9), 719; https://doi.org/10.3390/rs8090719 - 31 Aug 2016

Cited by 39 | Viewed by 10977

Abstract

Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson’s correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R²) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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Open AccessFeature PaperArticle

Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

by Martin Hais, Jan Wild, Luděk Berec, Josef Brůna, Robert Kennedy, Justin Braaten and Zdeněk Brož

Remote Sens. 2016, 8(8), 687; https://doi.org/10.3390/rs8080687 - 22 Aug 2016

Cited by 39 | Viewed by 9857

Abstract

Tree mortality caused by bark beetle infestation has significant effects on the ecology and value of both natural and commercial forests. Therefore, prediction of bark beetle infestations is critical in forest management. Existing predictive models, however, rarely consider the influence of long-term stressors on forest susceptibility to bark beetle infestation. In this study we introduce pre-disturbance spectral trajectories from Landsat Thematic Mapper (TM) imagery as an indicator of long-term stress into models of bark beetle infestation together with commonly used environmental predictors. Observations for this study come from forests in the central part of the Šumava Mountains, in the border region between the Czech Republic and Germany, Central Europe. The areas of bark beetle-infested forest were delineated from aerial photographs taken in 1991 and in every year from 1994 to 2000. The environmental predictors represent forest stand attributes (e.g., tree density and distance to the infested forest from previous year) and common abiotic factors, such as topography, climate, geology, and soil. Pre-disturbance spectral trajectories were defined by the linear regression slope of Tasseled Cap components (Wetness, Brightness and Greenness) calculated from a time series of 16 Landsat TM images across years from 1984 until one year before the bark beetle infestation. Using logistic regression and multimodel inference, we calculated predictive models separately for each single year from 1994 to 2000 to account for a possible shift in importance of individual predictors during disturbance. Inclusion of two pre-disturbance spectral trajectories (Wetness slope and Brightness slope) significantly improved predictive ability of bark beetle infestation models. Wetness slope had the greatest predictive power, even relative to environmental predictors, and was relatively stable in its power over the years. Brightness slope improved the model only in the middle of the disturbance period (1996). Importantly, these pre-disturbance predictors were not correlated with other predictors, and therefore bring additional explanatory power to the model. Generally, the predictive power of most fitted model decreases as time progresses and models describing the initial phase of bark beetle outbreaks appear more reliable for conducting near-future predictions. The pre-disturbance spectral trajectories are valuable not only for assessing the risk of bark beetle infestation, but also for detection of long-term gradual changes even in non-forest ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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Open AccessArticle

Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series

by Katarzyna Ewa Lewińska, Eva Ivits, Mathias Schardt and Marc Zebisch

Remote Sens. 2016, 8(8), 639; https://doi.org/10.3390/rs8080639 - 5 Aug 2016

Cited by 18 | Viewed by 7898

Abstract

Observed alternation of global and local meteorological patterns governs increasing drought impact, which puts at risk ecological balance and biodiversity of the alpine forest. Despite considerable attention, drought impact on forest ecosystems is still not entirely understood, and comprehensive forest drought monitoring has not been implemented. In this study, we proposed to bridge this gap exploiting a time-domain synergetic use of medium resolution MODSI NDVI (Normalized Difference Vegetation Index) and NDII7 (Normalized Difference Infrared Index band 7) time series as well as on-station temperature and precipitation measures combined in the scPDSI (self-calibrated Palmer Drought Severity Index) datasets. Analysis employed the S-mode Principal Component Analysis (PCA) examined under multiple method settings and data setups. The investigation performed for South Tyrol (2001–2012) indicated prolonged meteorological drought condition between 2003 and 2007, as well as general drying tendencies. Corresponding temporal variability was identified for local mountain forest. The former response was fostered more often by NDII7, which is related to foliage water content, whereas NDVI was more prone to report on an overall downturn and implied drop in forest photosynthetic activity. Among tested approaches, the covariance-matrix based S-mode PCA of z-score normalized vegetation season NDVI and NDII7 time series ensured the most prominent identification of drought impact. Consistency in recognized temporal patterns confirms integrity of the approach and aptness of used remote-sensed datasets, suggesting great potential for drought oriented environmental analyses. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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3160 KiB

Open AccessArticle

Comparison of Canopy Volume Measurements of Scattered Eucalypt Farm Trees Derived from High Spatial Resolution Imagery and LiDAR

by Niva Kiran Verma, David W. Lamb, Nick Reid and Brian Wilson

Remote Sens. 2016, 8(5), 388; https://doi.org/10.3390/rs8050388 - 5 May 2016

Cited by 41 | Viewed by 14602

Abstract

Studies estimating canopy volume are mostly based on laborious and time-consuming field measurements; hence, there is a need for easier and convenient means of estimation. Accordingly, this study investigated the use of remotely sensed data (WorldView-2 and LiDAR) for estimating tree height, canopy height and crown diameter, which were then used to infer the canopy volume of remnant eucalypt trees at the Newholme/Kirby ‘SMART’ farm in north-east New South Wales. A regression model was developed with field measurements, which was then applied to remote-sensing-based measurements. LiDAR estimates of tree dimensions were generally lower than the field measurements (e.g., 6.5% for tree height) although some of the parameters (such as tree height) may also be overestimated by the clinometer/rangefinder protocols used. The WorldView-2 results showed both crown projected area and crown diameter to be strongly correlated to canopy volume, and that crown diameter yielded better results (Root Mean Square Error RMSE 31%) than crown projected area (RMSE 42%). Although the better performance of LiDAR in the vertical dimension cannot be dismissed, as suggested by results obtained from this study and also similar studies conducted with LiDAR data for tree parameter measurements, the high price and complexity associated with the acquisition and processing of LiDAR datasets mean that the technology is beyond the reach of many applications. Therefore, given the need for easier and convenient means of tree parameters estimation, this study filled a gap and successfully used 2D multispectral WorldView-2 data for 3D canopy volume estimation with satisfactory results compared to LiDAR-based estimation. The result obtained from this study highlights the usefulness of high resolution data for canopy volume estimations at different locations as a possible alternative to existing methods. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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5664 KiB

Open AccessArticle

Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images

by Justin Murfitt, Yuhong He, Jian Yang, Amy Mui and Kevin De Mille

Remote Sens. 2016, 8(3), 256; https://doi.org/10.3390/rs8030256 - 17 Mar 2016

Cited by 30 | Viewed by 11419

Abstract

The invasive emerald ash borer (EAB, Agrilus planipennis Fairmaire) infects and eventually kills endemic ash trees and is currently spreading across the Great Lakes region of North America. The need for early detection of EAB infestation is critical to managing the spread of this pest. Using WorldView-2 (WV2) imagery, the goal of this study was to establish a remote sensing-based method for mapping ash trees undergoing various infestation stages. Based on field data collected in Southeastern Ontario, Canada, an ash health score with an interval scale ranging from 0 to 10 was established and further related to multiple spectral indices. The WV2 image was segmented using multi-band watershed and multiresolution algorithms to identify individual tree crowns, with watershed achieving higher segmentation accuracy. Ash trees were classified using the random forest classifier, resulting in a user’s accuracy of 67.6% and a producer’s accuracy of 71.4% when watershed segmentation was utilized. The best ash health score-spectral index model was then applied to the ash tree crowns to map the ash health for the entire area. The ash health prediction map, with an overall accuracy of 70%, suggests that remote sensing has potential to provide a semi-automated and large-scale monitoring of EAB infestation. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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5023 KiB

Open AccessArticle

Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets

by Qiao Jiao, Rui Li, Fei Wang, Xingmin Mu, Pengfei Li and Chunchun An

Remote Sens. 2016, 8(2), 156; https://doi.org/10.3390/rs8020156 - 19 Feb 2016

Cited by 36 | Viewed by 7300

Abstract

A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998–2000 and 2008–2010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998–2000 was close to that of 2008–2010 (the difference being 13.10 mm). From 1998–2000 to 2008–2010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China’s arid and semi-arid regions. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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669 KiB

Open AccessCommunication

Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test

by Samuli Junttila, Sanna Kaasalainen, Mikko Vastaranta, Teemu Hakala, Olli Nevalainen and Markus Holopainen

Remote Sens. 2015, 7(10), 13863-13877; https://doi.org/10.3390/rs71013863 - 22 Oct 2015

Cited by 24 | Viewed by 6700

Abstract

Global warming is posing a threat to the health and condition of forests as the amount and length of biotic and abiotic disturbances increase. Most methods for detecting disturbances and measuring forest health are based on multi- and hyperspectral imaging. We conducted a test with spruce and pine trees using a hyperspectral Lidar instrument in a laboratory to determine the capability of combined range and reflectance measurements to investigate forest health. A simple drought treatment was conducted by leaving the harvested trees outdoors without a water supply for 12 days. The results showed statistically significant variation in reflectance after the drought treatment for both species. However, the changes differed between the species, indicating that drought-induced alterations in spectral characteristics may be species-dependent. Based on our results, hyperspectral Lidar has the potential to detect drought in spruce and pine trees. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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Review

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52 pages, 24142 KiB

Open AccessReview

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

by Angela Lausch, Erik Borg, Jan Bumberger, Peter Dietrich, Marco Heurich, Andreas Huth, András Jung, Reinhard Klenke, Sonja Knapp, Hannes Mollenhauer, Hendrik Paasche, Heiko Paulheim, Marion Pause, Christian Schweitzer, Christiane Schmulius, Josef Settele, Andrew K. Skidmore, Martin Wegmann, Steffen Zacharias, Toralf Kirsten and Michael E. Schaepman Show full author list Hide full author list

Remote Sens. 2018, 10(7), 1120; https://doi.org/10.3390/rs10071120 - 15 Jul 2018

Cited by 75 | Viewed by 19409

Abstract

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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1356 KiB

Open AccessReview

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

by Angela Lausch, Stefan Erasmi, Douglas J. King, Paul Magdon and Marco Heurich

Remote Sens. 2017, 9(2), 129; https://doi.org/10.3390/rs9020129 - 5 Feb 2017

Cited by 123 | Viewed by 19470

Abstract

Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time- and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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3812 KiB

Open AccessReview

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

by Angela Lausch, Stefan Erasmi, Douglas J. King, Paul Magdon and Marco Heurich

Remote Sens. 2016, 8(12), 1029; https://doi.org/10.3390/rs8121029 - 18 Dec 2016

Cited by 158 | Viewed by 27196

Abstract

Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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818 KiB

Open AccessReview

In Situ/Remote Sensing Integration to Assess Forest Health—A Review

by Marion Pause, Christian Schweitzer, Michael Rosenthal, Vanessa Keuck, Jan Bumberger, Peter Dietrich, Marco Heurich, András Jung and Angela Lausch

Remote Sens. 2016, 8(6), 471; https://doi.org/10.3390/rs8060471 - 3 Jun 2016

Cited by 98 | Viewed by 17679

Abstract

For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Health)

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