Browsing by Subject "REFLECTANCE"

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  • Tang, Zhipeng; Adhikari, Hari; Pellikka, Petri; Heiskanen, Janne (2021)
    Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.
  • Tian, Wenxin; Tang, Lingli; Chen, Yuwei; Li, Ziyang; Zhu, Jiajia; Jiang, Changhui; Hu, Peilun; He, Wenjing; Wu, Haohao; Pan, Miaomiao; Lu, Jing; Hyyppa, Juha (2021)
    Hyperspectral LiDAR (HSL) is a new remote sensing detection method with high spatial and spectral information detection ability. In the process of laser scanning, the laser echo intensity is affected by many factors. Therefore, it is necessary to calibrate the backscatter intensity data of HSL. Laser incidence angle is one of the important factors that affect the backscatter intensity of the target. This paper studied the radiometric calibration method of incidence angle effect for HSL. The reflectance of natural surfaces can be simulated as a combination of specular reflection and diffuse reflection. The linear combination of the Lambertian model and Beckmann model provides a comprehensive theory that can be applied to various surface conditions, from glossy to rough surfaces. Therefore, an adaptive threshold radiometric calibration method (Lambertian-Beckmann model) is proposed to solve the problem caused by the incident angle effect. The relationship between backscatter intensity and incident angle of HSL is studied by combining theory with experiments, and the model successfully quantifies the difference between diffuse and specular reflectance coefficients. Compared with the Lambertian model, the proposed model has higher calibration accuracy, and the average improvement rate to the samples in this study was 22.67%. Compared with the results before calibration with the incidence angle of less than 70 degrees, the average improvement rate of the Lambertian-Beckmann model was 62.26%. Moreover, we also found that the green leaves have an obvious specular reflection effect near 650-720 nm, which might be related to the inner microstructure of chlorophyll. The Lambertian-Beckmann model was more helpful to the calibration of leaves in the visible wavelength range. This is a meaningful and a breakthrough exploration for HSL.
  • White, Joanne C.; Saarinen, Ninni; Wulder, Michael A.; Kankare, Ville; Hermosilla, Txomin; Coops, Nicholas C.; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko (2019)
    Information regarding the nature and rate of forest recovery is required to inform forest management, monitoring, and reporting activities. Delayed establishment or return of forests has implications to harvest rotations and carbon uptake, among others, creating a need for spatially-explicit, large-area, characterizations of forest recovery. Landsat time series (LTS) has been demonstrated as a means to quantitatively relate forest recovery, noting that there are gaps in our understanding of the linkage between spectral measures of forest recovery and manifestations of forest structure and composition. Field plots provide a means to better understand the linkage between forest characteristics and spectral recovery indices. As such, from a large set of existing field plots, we considered the conditions present for the year in which the co-located pixel was considered spectrally recovered using the Years to Recovery (Y2R) metric. Y2R is a long-term metric of spectral recovery that indicates the number of years required for a pixel to return to 80% of its pre-disturbance Normalized Burn Ratio value. Absolute and relative metrics of recovery at 5 years post-disturbance were also considered. We used these three spectral recovery metrics to predict the stand development class assigned by the field crew for 284 seedling plots with an overall accuracy of 73.59%, with advanced seedling stands more accurately discriminated (omission error, OE = 15.74%) than young seedling stands (OE = 49.84%). We then used field-measured attributes (e.g. height, stem density, dominant species) from the seedling plots to classify the plots into three spectral recovery groups, which were defined using the Y2R metric: spectral recovery in (1) 1–5 years, (2) 6–10 years, or (3) 11–15 years. Overall accuracy for spectral recovery groups was 61.06%. Recovery groups 1 and 3 were discriminated with greater accuracy (producer’s and user’s accuracies > 66%) than recovery group 2 (<50%). The top field-measured predictors of spectral recovery were mean height, dominant species, and percentage of stems in the plot that were deciduous. Variability in stand establishment and condition make it challenging to accurately discriminate among recovery rates within 10 years post-harvest. Our results indicate that the long-term metric Y2R relates to forest structure and composition attributes measured in the field and that spectral development post-disturbance corresponds with expectations of structural development, particularly height, for different species, site types, and deciduous abundance. These results confirm the utility of spectral recovery measures derived from LTS data to augment landscape-level assessments of post-disturbance recovery.
  • Junttila, Samuli; Sugano, Junko; Vastaranta, Mikko; Linnakoski, Riikka; Kaartinen, Harri; Kukko, Antero; Holopainen, Markus; Hyyppa, Hannu; Hyyppa, Juha (2018)
    Changing climate is increasing the amount and intensity of forest stress agents, such as drought, pest insects, and pathogens. Leaf water content, measured here in terms of equivalent water thickness (EWT), is an early indicator of tree stress that provides timely information about the health status of forests. Multispectral terrestrial laser scanning (MS-TLS) measures target geometry and reflectance simultaneously, providing spatially explicit reflectance information at several wavelengths. EWT and leaf internal structure affect leaf reflectance in the shortwave infrared region that can be used to predict EWT with MS-TLS. A second wavelength that is sensitive to leaf internal structure but not affected by EWT can be used to normalize leaf internal effects on the shortwave infrared region and improve the prediction of EWT. Here we investigated the relationship between EWT and laser intensity features using multisensor MS-TLS at 690, 905, and 1,550 nm wavelengths with both drought-treated and Endoconidiophora polonica inoculated Norway spruce seedlings to better understand how MS-TLS measurements can explain variation in EWT. In our study, a normalized ratio of two wavelengths at 905 and 1,550 nm and length of seedling explained 91% of the variation (R-2) in EWT as the respective prediction accuracy for EWT was 0.003 g/cm(2) in greenhouse conditions. The relation between EWT and the normalized ratio of 905 and 1,550 nm wavelengths did not seem sensitive to a decreased point density of the MS-TLS data. Based on our results, different EWTs in Norway spruce seedlings show different spectral responses when measured using MS-TLS. These results can be further used when developing EWT monitoring for improving forest health assessments.
  • Abera, Temesgen; Heiskanen, Janne; Pellikka, Petri; Adhikari, Hari; Maeda, Eduardo (2020)
    Bushlands (Acacia-Commiphora) constitute the largest and one of the most threatened ecosystems in East Africa. Although several studies have investigated the climatic impacts of land changes on local and global climate, the main focus has been on forest loss and the impacts of bushland clearing thus remain poorly understood. Measuring the impacts of bushland loss on local climate is challenging given that changes often occur at fragmented and small patches. Here, we apply high-resolution satellite imagery and land surface flux modeling approaches to unveil the impacts of bushland clearing on surface biophysical properties and its associated effects on surface energy balance and land surface temperature. Our results show that bushland clearing leads to an average reduction in evapotranspiration of 0.4 mm day(-1). The changes in surface biophysical properties affected the surface energy balance components with different magnitude. The reduction in latent heat flux was stronger than other surface energy fluxes and resulted in an average net increase in daytime land surface temperature (LST) of up to 1.75 K. These results demonstrate the important impact of bushland-to-cropland conversion on the local climate, as they reveal increases in LST of a magnitude comparable to those caused by forest loss. This finding highlights the necessity of bushland conservation for regulating the land surface temperature in East Africa and, at the same time, warns of the climatic impacts of clearing bushlands for agriculture. (c) 2020 The Authors. Published by Elsevier B.V.
  • Sabater, Neus; Vicent, Jorge; Alonso, Luis; Verrelst, Jochem; Middleton, Elizabeth M.; Porcar-Castell, Albert; Moreno, José (2018)
    Estimates of Sun–Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O 2 ) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal–sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (1) the sensor height above the vegetated canopy; (2) the SIF retrieval technique used, e.g., Fraunhofer Line Discriminator (FLD) family or Spectral Fitting Methods (SFM); and (3) the instrument’s spectral resolution. We demonstrate that for proximal–sensing scenarios compensating for atmospheric effects by simply introducing the O 2 transmittance function into the FLD or SFM formulations improves SIF estimations. However, these simplistic corrections still lead to inaccurate SIF estimations due to the multiplication of spectrally convolved atmospheric transfer functions with absorption features. Consequently, a more rigorous oxygen compensation strategy is proposed and assessed by following a classic airborne atmospheric correction scheme adapted to proximal sensing. This approach allows compensating for the O 2 absorption effects and, at the same time, convolving the high spectral resolution data according to the corresponding Instrumental Spectral Response Function (ISRF) through the use of an atmospheric radiative transfer model. Finally, due to the key role of O 2 absorption on the evaluated proximal–sensing SIF retrieval strategies, its dependency on surface pressure (p) and air temperature (T) was also assessed. As an example, we combined simulated spectral data with p and T measurements obtained for a one–year period in the Hyytiälä Forestry Field Station in Finland. Of importance hereby is that seasonal dynamics in terms of T and p, if not appropriately considered as part of the retrieval strategy, can result in erroneous SIF seasonal trends that mimic those of known dynamics for temperature–dependent physiological responses of vegetation.
  • White, Joanne C.; Saarinen, Ninni; Kankare, Ville; Wulder, Michael A.; Hermosilla, Txomin; Coops, Nicholas C.; Pickell, Paul D.; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko (2018)
    Landsat time series (LTS) enable the characterization of forest recovery post-disturbance over large areas; however, there is a gap in our current knowledge concerning the linkage between spectral measures of recovery derived from LTS and actual manifestations of forest structure in regenerating stands. Airborne laser scanning (ALS) data provide useful measures of forest structure that can be used to corroborate spectral measures of forest recovery. The objective of this study was to evaluate the utility of a spectral index of recovery based on the Normalized Burn Ratio (NBR): the years to recovery, or Y2R metric, as an indicator of the return of forest vegetation following forest harvest (clearcutting). The Y2R metric has previously been defined as the number of years required for a pixel to return to 80% of its pre-disturbance NBR (NBRpre) value. In this study, the Composite2Change (C2C) algorithm was used to generate a time series of gap-free, cloud-free Landsat surface reflectance composites (1985–2012), associated change metrics, and a spatially-explicit dataset of detected changes for an actively managed forest area in southern Finland (5.3 Mha). The overall accuracy of change detection, determined using independent validation data, was 89%. Areas of forest harvesting in 1991 were then used to evaluate the Y2R metric. Four alternative recovery scenarios were evaluated, representing variations in the spectral threshold used to define Y2R: 60%, 80%, and 100% of NBRpre, and a critical value of z (i.e. the year in which the pixel's NBR value is no longer significantly different from NBRpre). The Y2R for each scenario were classified into five groups: recovery within 17 years, and not recovered. Measures of forest structure (canopy height and cover) were obtained from ALS data. Benchmarks for height (>5 m) and canopy cover (>10%) were applied to each recovery scenario, and the percentage of pixels that attained both of these benchmarks for each recovery group, was determined for each Y2R scenario. Our results indicated that the Y2R metric using the 80% threshold provided the most realistic assessment of forest recovery: all pixels considered in our analysis were spectrally recovered within the analysis period, with 88.88% of recovered pixels attaining the benchmarks for both cover and height. Moreover, false positives (pixels that had recovered spectrally, but not structurally) and false negatives (pixels that had recovered structurally, but not spectrally) were minimized with the 80% threshold. This research demonstrates the efficacy of LTS-derived assessments of recovery, which can be spatially exhaustive and retrospective, providing important baseline data for forest monitoring.
  • Viinikka, Arto; Hurskainen, Pekka; Keski-Saari, Sarita; Kivinen, Sonja; Tanhuanpää, Topi; Mäyrä, Janne; Poikolainen, Laura; Vihervaara, Petteri; Kumpula, Timo (2020)
    Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremulaL.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455-2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers-support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724-727 nm) and shortwave infrared (1520-1564 nm and 1684-1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests.
  • Popp, Thomas; De Leeuw, Gerrit; Bingen, Christine; Bruehl, Christoph; Capelle, Virginie; Chedin, Alain; Clarisse, Lieven; Dubovik, Oleg; Grainger, Roy; Griesfeller, Jan; Heckel, Andreas; Kinne, Stefan; Klueser, Lars; Kosmale, Miriam; Kolmonen, Pekka; Lelli, Luca; Litvinov, Pavel; Mei, Linlu; North, Peter; Pinnock, Simon; Povey, Adam; Robert, Charles; Schulz, Michael; Sogacheva, Larisa; Stebel, Kerstin; Zweers, Deborah Stein; Thomas, Gareth; Tilstra, Lieuwe Gijsbert; Vandenbussche, Sophie; Veefkind, Pepijn; Vountas, Marco; Xue, Yong (2016)
    Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption).
  • Saponaro, Giulia; Kolmonen, Pekka; Sogacheva, Larisa; Rodriguez, Edith; Virtanen, Timo; De Leeuw, Gerrit (2017)
    Retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Aqua satellite, 12 years (2003-2014) of aerosol and cloud properties were used to statistically quantify aerosol-cloud interaction (ACI) over the Baltic Sea region, including the relatively clean Fennoscandia and the more polluted central-eastern Europe. These areas allowed us to study the effects of different aerosol types and concentrations on macro-and microphysical properties of clouds: cloud effective radius (CER), cloud fraction (CF), cloud optical thickness (COT), cloud liquid water path (LWP) and cloud-top height (CTH). Aerosol properties used are aerosol optical depth (AOD), Angstrom exponent (AE) and aerosol index (AI). The study was limited to low-level water clouds in the summer. The vertical distributions of the relationships between cloud properties and aerosols show an effect of aerosols on low-level water clouds. CF, COT, LWP and CTH tend to increase with aerosol loading, indicating changes in the cloud structure, while the effective radius of cloud droplets decreases. The ACI is larger at relatively low cloud-top levels, between 900 and 700 hPa. Most of the studied cloud variables were unaffected by the lower-tropospheric stability (LTS), except for the cloud fraction. The spatial distribution of aerosol and cloud parameters and ACI, here defined as the change in CER as a function of aerosol concentration for a fixed LWP, shows positive and statistically significant ACI over the Baltic Sea and Fennoscandia, with the former having the largest values. Small negative ACI values are observed in central-eastern Europe, suggesting that large aerosol concentrations saturate the ACI.
  • Korpela, Ilkka; Haapanen, R.; Korrensalo, A.; Tuittila, E-S; Vesala, T. (2020)
    Boreal bogs are important stores and sinks of atmospheric carbon whose surfaces are characterised by vegetation microforms. Efficient methods for monitoring their vegetation are needed because changes in vegetation composition lead to alteration in their function such as carbon gas exchange with the atmosphere. We investigated how airborne image and waveform-recording LiDAR data can be used for 3D mapping of microforms in an open bog which is a mosaic of pools, hummocks with a few stunted pines, hollows, intermediate surfaces and mud-bottom hollows. The proposed method operates on the bog surface, which is reconstructed using LiDAR. The vegetation was classified at 20 cm resolution. We hypothesised that LiDAR data describe surface topography, moisture and the presence and depth of field-layer vegetation and surface roughness; while multiple images capture the colours and texture of the vegetation, which are influenced by directional reflectance effects. We conclude that geometric LiDAR features are efficient predictors of microforms. LiDAR intensity and echo width were specific to moisture and surface roughness, respectively. Directional reflectance constituted 4-34 % of the variance in images and its form was linked to the presence of the field layer. Microform-specific directional reflectance patterns were deemed to be of marginal value in enhancing the classification, and RGB image features were inferior to LiDAR variables. Sensor fusion is an attractive option for fine-scale mapping of these habitats. We discuss the task and propose options for improving the methodology.
  • Lukes, Petr; Rautiainen, Miina; Manninen, Terhikki; Stenberg, Pauline; Mottus, Matti (2014)
    Land surface albedo is an essential climate variable controlling the planetary radiative energy budget, yet it is still among the main uncertainties of the radiation budget in the current climate modeling. To date, albedo satellite products have not been linked to extensive forest inventory data sets due to the lack of ground reference data. Here, we used comprehensive and detailed maps of forest inventory variables to couple forest structure and MODIS albedo products for both winter and summer conditions. We investigated how the relationships between forest variables and albedo change seasonally and along latitudinal gradients in the forest biomes of Finland between 60° and 70° N. We observed an increase in forest albedo with increasing latitude in winter but not in summer. Also, relationships between forest variables and the black-sky albedo or directional–hemispherical reflectance (DHR) at different latitudes were tighter in winter than in summer, especially for forest biomass. Summer albedo was only weakly correlated with the traditional inventory variables. Our findings suggest that the relationships between forest variables and DHR depend on latitude.
  • Knyazikhin, Yuri; Schull, Mitchell A.; Stenberg, Pauline; Mõttus, Matti; Rautiainen, Miina; Yang, Yan; Marshak, Alexander; Latorre Carmona, Pedro; Kaufmann, Robert K.; Lewis, Philip; Disney, Mathias I.; Vanderbilt, Vern; Davis, Anthony B.; Baret, Frederic; Jacquemoud, Stephane; Lyapustin, Alexei; Myneni, Ranga B. (2013)
  • Jenal, Alexander; Hueging, Hubert; Ahrends, Hella Ellen; Bolten, Andreas; Bongartz, Jens; Bareth, Georg (2021)
    UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R-2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R-2: 0.75 to 0.84). These results indicate the imaging system's potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.
  • Zou, Xiaochen; Mottus, Matti; Tammeorg, Priit; Lizarazo Torres, Clara; Takala, Tuure; Pisek, Jan; Makela, Pirjo; Stoddard, F. L.; Pellikka, Petri (2014)
  • Näsi, Roope; Honkavaara, Eija; Blomqvist, Minna; Lyytikäinen-Saarenmaa, Päivi Marja Emilia; Hakala, Teemu; Viljanen, Niko; Kantola, Tuula Anneli; Holopainen, Markus Edvard (2018)
    Climate-related extended outbreaks and range shifts of destructive bark beetle species pose a serious threat to urban boreal forests in North America and Fennoscandia. Recent developments in low-cost remote sensing technologies offer an attractive means for early detection and management of environmental change. They are of great interest to the actors responsible for monitoring and managing forest health. The objective of this investigation was to develop, assess, and compare automated remote sensing procedures based on novel, low-cost hyperspectral imaging technology for the identification of bark beetle infestations at the individual tree level in urban forests. A hyperspectral camera based on a tunable Fabry-Perot interferometer was operated from a small, unmanned airborne vehicle (UAV) platform and a small Cessna-type aircraft platform. This study compared aspects of using UAV datasets with a spatial extent of a few hectares (ha) and a ground sample distance (GSD) of 10-12 cm to the aircraft data covering areas of several km(2) and having a GSD of 50 cm. An empirical assessment of the automated identification of mature Norway spruce (Picea abies L. Karst.) trees suffering from infestation (representing different colonization phases) by the European spruce bark beetle (Ips typographus L.) was carried out in the urban forests of Lahti, a city in southern Finland. Individual spruces were classified as healthy, infested, or dead. For the entire test area, the best aircraft data results for overall accuracy were 79% (Cohen's kappa: 0.54) when using three crown color classes (green as healthy, yellow as infested, and gray as dead). For two color classes (healthy, dead) in the same area, the best overall accuracy was 93% (kappa: 0.77). The finer resolution UAV dataset provided better results, with an overall accuracy of 81% (kappa: 0.70), compared to the aircraft results of 73% (kappa: 0.56) in a smaller sub-area. The results showed that novel, low-cost remote sensing technologies based on individual tree analysis and calibrated remote sensing imagery offer great potential for affordable and timely assessments of the health condition of vulnerable urban forests.
  • Atlaskina, K.; Berninger, F.; de Leeuw, G. (2015)
    Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March-May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50 degrees N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56% of variation of albedo in March, 76% in April and 92% in May. Therefore the effects of other parameters were investigated only for areas with 100% SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between -15 and -10 degrees C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100% SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.
  • Junttila, S.; Holopainen, M.; Vastaranta, M.; Lyytikäinen-Saarenmaa, P.; Kaartinen, H.; Hyyppä, J.; Hyyppä, H. (2019)
    Climate change is causing novel forest stress around the world due to changes in environmental conditions. Forest pest insects, such as Ips typographus (L.), are spreading toward the northern latitudes and are now able to produce more generations in their current range; this has increased forest disturbances. Timely information on tree decline is critical in allowing forest managers to plan effective countermeasures and to forecast potential infestation areas. Field-based infestation surveys of bark beetles have traditionally involved visual estimates of entrance holes, resin flow, and maternal-gallery densities; such estimates are prone to error and bias. Thus, objective and automated methods for estimating tree infestation status are required. In this study, we investigated the feasibility of dual-wavelength terrestrial lidar in the estimation and detection of I. typographus infestation symptoms. In addition, we examined the relationship between leaf water content (measured as gravimetric water content and equivalent water thickness) and infestation severity. Using two terrestrial lidar systems (operating at 905 nm and 1550 nm), we measured 29 mature Norway spruce (Picea abies [L.] Karst.) trees that exhibited low or moderate infestation symptoms. We calculated single and dual-wavelength lidar intensity metrics from stem and crown points to test these metrics' ability to discriminate I. typographus infestation levels using regressions and linear discriminant analyses. Across the various I. typographus infestation levels, we found significant differences (p 
  • Majasalmi, Titta; Rautiainen, Miina; Stenberg, Pauline; Manninen, Terhikki (2015)
    Remote sensing of the fraction of absorbed Photosynthetically Active Radiation (fPAR) has become a timely option to monitor forest productivity. However, only a few studies have had ground reference fPAR datasets containing both forest canopy and understory fPAR from boreal forests for the validation of satellite products. The aim of this paper was to assess the performance of two currently available satellite-based fPAR products: MODIS fPAR (MOD15A2, C5) and GEOV1 fPAR (g2_BIOPAR_FAPAR), as well as an NDVI-fPAR relationship applied to the MODIS surface reflectance product and a Landsat 8 image, in a boreal forest site in Finland. Our study area covered 16 km(2) and field data were collected from 307 forest plots. For all plots, we obtained both forest canopy fPAR and understory fPAR. The ground reference total fPAR agreed better with GEOV1 fPAR than with MODIS fPAR, which showed much more temporal variation during the peak-season than GEOV1 fPAR. At the chosen intercomparison date in peak growing season, MODIS NDVI based fPAR estimates were similar to GEOV1 fPAR, and produced on average 0.01 fPAR units smaller fPAR estimates than ground reference total fPAR. MODIS fPAR and Landsat 8 NDVI based fPAR estimates were similar to forest canopy fPAR.