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

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.

Mapping Spatial Variations of Structure and Function Parameters for Forest Condition Assessment of the Changbai Mountain National Nature Reserve

“…Specifically, canopy closure, and stand density were modeled by Sentinel-2 and stand volume was modeled by L band SAR. It was accorded from previous studies that SAR data were sensitive to vertical structure and function while MSI were primary in horizontal canopy modeling [18,21]. L band SAR penetrated into the canopy and scatters back from leaves, branches, and stems [114].…”

“…Remote sensing modeling combined sample plot data has become a well adopted method to generate spatially explicit estimates of forest parameters [15,16]. The selection of predictor variables from various sensors and algorithms can affect the results considerably [17,18]. Variables from optical sensors are commonly applied to predict horizontal forest structure such as canopy closure and density [19,20].…”

“…However, synthetic aperture radar (SAR) and light detection and ranging (LiDAR) sensors are capable of penetrating cloud and canopies and are suitable for mapping vertical forest parameters such as tree height and stand volume [23][24][25]. Whereas, complex forest parameters such as biomass, soil fertility, and forest age are generally estimated by multi-sensor data [26][27][28][29].Modeling vegetation parameters based on remote sensing can be divided into physically based models and empirical regression algorithms [18,21,30]. Physically based models depend on numerous factors to simulate canopy reflectance, such as leaf geometry, chlorophyll concentration, water and matter contents, soil reflectance, and bidirectional reflectance distribution function, which may not be readily available [31,32].…”

“…Modeling vegetation parameters based on remote sensing can be divided into physically based models and empirical regression algorithms [18,21,30]. Physically based models depend on numerous factors to simulate canopy reflectance, such as leaf geometry, chlorophyll concentration, water and matter contents, soil reflectance, and bidirectional reflectance distribution function, which may not be readily available [31,32].…”

Traditional and Novel Indicators of Climate Change Impacts on European Forest Trees

“…Through the analysis of the optical properties of leaves and canopies, remote sensing can estimate several functional attributes such as the biochemical, structural, and physiological traits of leaves and canopies, including nitrogen concentration, photosynthetic pigment and water content, leaf mass per area, carbon isotope composition, leaf area index, and biomass [31,[73][74][75]. The overall analysis of spectral traits is fundamental for aerial forest health surveys [76,77]. Remote sensing needs to be validated with field-collected data, and making the connections between terrestrial and aerial surveys is a very challenging task [78] since the two approaches use different criteria that are hardly connected.…”