Composite indicator for monitoring of Norway spruce stand decline

Abstract

The study is aimed to explore the potential of time-series airborne hyperspectral and satellite multispectral data to track the changes in spruce forest decline expressed by a composite spruce decline indicator. Vegetation indices and exergy of solar radiation extracted from remote sensing data are used to predict the development of the composite spruce health indicator. The canopy-level spectral reflectance properties of spruce stands are investigated to identify categories of spruce stand decline: healthy, initial decline, and initial to moderate decline. The sensitivity peaks for initial decline and initial to moderate decline of spruce are shown. The highest potential for the estimation of the composite spruce health indicator is demonstrated by vegetation indices WBI and NDVI red_edge from airborne hyperspectral data, and by PSRI, NDII and exergy of solar radiation from Landsat and Sentinel-2 satellite multispectral data. MODIS data show only a poor correlation between the composite spruce stand health indicator and NDII index. The proposed methodology to obtain the distribution of the composite spruce decline indicator using remote sensing (RS) data promisingly suggests its applicability over a large forest area with potential time and economic benefits, since foliar spectral measurements, canopy chemistry, and laboratory analysis are not required.

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data

“…Monitoring of forests is one of the relevant and practical tasks effectively carried out on the basis of recent satellite optical-electronic multispectral (MS) and hyperspectral (HS) means (Boyd et al 2005;Banskota et al 2014;Transon et al 2017). MS and HS data referencing spectra of woody vegetation and algorithms of data processing in (semi)automatic mode allow separation of forest from non-forest lands (Connette et al 2016;Zhou et al 2018); prediction of forest inventory characteristics (McRoberts et al 2007); and detection of forest damage due to fires, degradation, and pathological changes (Grigorieva 2014;Lausch et al 2016;Brovkina et al 2017).…”

Detection of Annual Spruce Budworm Defoliation and Severity Classification Using Landsat Imagery

“…Airborne and space-borne optical remote sensing (RS) sensors have been explored in various research in the past for detecting and quantifying insect-induced forest defoliation and mortality, as well as for forecasting future outbreak patterns [11,15,16]. Among them, multi-spectral satellite data, such as Land Remote Sensing Satellite (Landsat), Satellite Pour l'Observation de la Terre (SPOT), Moderate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 have been used to detect and characterize defoliators like gypsy moth [17][18][19][20], forest tent caterpillar (Malacosoma disstria) [21], jack pine budworm [22,23], hemlock looper (Lambdina fiscellaria (Guenée)) [24], pine-tree lappet (Dendrolimus pini L.) [25], and SBW [6,12,[26][27][28].…”

Norway spruce (Picea abies/L./Karst.) health status on various forest soil ecological series in Silesian Beskids obtained by grid or selective survey

“…The observation of specific influences on FHS is based on connection of data about soil organic matter and ecosystem water content (Samec et al 2012). Although support for the observation of ecosystem water content by remote-sensing relates with detailed grid use, the observation of soil properties under stand cover needs selection of good representative pits (Ziche, Seidling 2010;Fattorini et al 2015;Brovkina et al 2017).…”