Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Quantifying Forest Health Using RS <s> Abstract A variety of ecological applications require data from broad spatial extents that cannot be collected using field-based methods. Remote sensing data and techniques address these needs, which include identifying and detailing the biophysical characteristics of species' habitats, predicting the distribution of species and spatial variability in species richness, and detecting natural and human-caused change at scales ranging from individual landscapes to the entire world. Such measurements are subject to substantial errors that can be difficult to overcome, but corrected data are readily available and can be of sufficiently high resolution to be integrated into traditional field-based studies. Ecologists and conservation biologists are finding new ways to approach their research with the powerful suite of tools and data from remote sensing. <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Quantifying Forest Health Using RS <s> The severity and large spatial extent of the current mountain pine beetle outbreak in western North America has prompted research into methods for surveying the location and extent of beetle impacts using remotely sensed data. New forms of remotely sensed data and methods of image analysis have emerged in recent years, which have the potential to provide information on mountain pine beetle red-attack damage that complements information gathered from existing survey methods. The main objective of this review is to summarize previous and current contributions of remote sensing to the survey of mountain pine beetle impacts. The potential and limits of remotely sensed data for the detection and mapping of mountain pine beetle impacts, over a range of attack stages, are identified and synthesized. The secondary objective of this review is to highlight those methods or data sources that currently have operational potential and that the authors believe warrant further research in support of ongoing planning and management activities. Emphasis is placed on the different information needs associated with the various spatial scales of forest management: regional, landscape, and local. The findings of this review indicate that remotely sensed data is useful for mapping red-attack damage across a range of image data types and scales of inquiry. The review concludes with recommendations for future research, and suggestions for operational standards to guide the use of remotely sensed data for the survey of mountain pine beetle impacts. <s> BIB002 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Quantifying Forest Health Using RS <s> Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS). <s> BIB003 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Quantifying Forest Health Using RS <s> Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high- and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR) data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove ecosystem mapping, as well as the numerous methods and techniques used for data analyses, and to further discuss their potential and limitations. <s> BIB004 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Quantifying Forest Health Using RS <s> Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil’s well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change. <s> BIB005 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Quantifying Forest Health Using RS <s> Mapping anthropogenic forest disturbances has largely been focused on distinct delineations of events of deforestation using optical satellite images. In the tropics, frequent cloud cover and the challenge of quantifying forest degradation remain problematic. In this study, we detect processes of deforestation, forest degradation and successional dynamics, using long-wavelength radar (L-band from ALOS PALSAR) backscatter. We present a detection algorithm that allows for repeated disturbances on the same land, and identifies areas with slow- and fast-recovering changes in backscatter in close spatial and temporal proximity. In the study area in Madre de Dios, Peru, 2.3% of land was found to be disturbed over three years, with a false positive rate of 0.3% of area. A low, but significant, detection rate of degradation from sparse and small-scale selective logging was achieved. Disturbances were most common along the tri-national Interoceanic Highway, as well as in mining areas and areas under no land use allocation. A continuous spatial gradient of disturbance was observed, highlighting artefacts arising from imposing discrete boundaries on deforestation events. The magnitude of initial radar backscatter, and backscatter decrease, suggested that large-scale deforestation was likely in areas with initially low biomass, either naturally or since already under anthropogenic use. Further, backscatter increases following disturbance suggested that radar can be used to characterize successional disturbance dynamics, such as biomass accumulation in lands post-abandonment. The presented radar-based detection algorithm is spatially and temporally scalable, and can support monitoring degradation and deforestation in tropical rainforests with the use of products from ALOS-2 and the future SAOCOM and BIOMASS missions. <s> BIB006
There is a wide range of remote sensing sensors, models and applications that deal with measuring, explaining and predicting FH  BIB004 BIB006 BIB001 BIB002 BIB003 BIB005  . Table 1 summarizes the RS applications, which can only be partially described in this paper. In addition to the sensors and platforms used, FH indicators are also categorized as indicators of taxonomic, functional or structural diversity of FES. The ability to monitor FH indicators with RS data is dependent on the following factors: (i) the characteristics of forest traits and the shape, density and distribution of forest traits in space and time; (ii) the spatial, spectral, radiometric, angular and temporal resolutions of RS sensors or multi-sensor systems; (iii) the choice of the modelling technique (classification or biophysical/chemical variable estimation) and entity representation (pixel-based or (geographic) object-based); and (iv) how well the RS algorithm and its assumptions fit the RS data and the plant traits and trait variations in FES. 