Search

Abstract We compared the performance of Sentinel-2 and Landsat 8 data for forest variable prediction in the boreal forest of Southern Finland. We defined twelve modelling setups to train multivariable prediction models with either multilayer perceptron (MLP) or regression tree models with the brute force forward selection method. The reference data consisted of 739 circular field plots that had been collected by the Finnish Forest Centre concurrently with the Sentinel-2 and Landsat 8 acquisitions. The input data were divided into training, validation and test sets of equal sizes for 100 iterations in each modelling setup. The predicted forest variables were stem volume (V), stem diameter (D), tree height (H) and basal area (G), and their species-wise components for pine (Pine), spruce (Spr) and broadleaved (BL) trees. We recorded the performance figures and the best predictive image bands for each modelling setup. The best average performance over the 100 modelling iterations was obtained using all Sentinel-2 bands. The plot-level relative root mean square errors (RMSE%) of the field observed mean were 38.4% for average stem diameter, 42.5% for stem basal area/ha, 30.4% for average tree height, and 59.3% for growing stock volume/ha with variables including all tree species. The corresponding best figures with all Landsat 8 bands were RMSE% = 44.6%, 50.2%, 36.6% and 72.2%, respectively. The Sentinel-2 outperformed Landsat 8 also when using near-equivalent image bands and Sentinel-2 data down-sampled to 30 m pixel resolution. The relative systematic error (bias%) did not show any significant differences between Sentinel-2 and Landsat 8 data: the average of the absolute value of bias% was 0.8% for Sentinel-2 and 1.2% for Landsat 8. The best predictive Sentinel-2 image band was the red-edge 1 (B05_RE1), when variable totals including all species were estimated. The short-wave infrared bands (B11_SWIR1 & B12_SWIR2) and the visible green band (B03_Green) were also among the best predictors. The median number of predictors in the best performing models was 4–6 for the Sentinel-2 and 4–5 for the Landsat 8 models, respectively. We conclude that Sentinel-2 Multispectral Instrument (MSI) data can be recommended as the principal Earth observation data source in forest resources assessment. Highlights • Sentinel-2 (S2) outperformed Landsat 8 in forest variable prediction. • The S2 red-edge bands and the better spatial resolution are explaining factors. • The best predictive S2 image band was red-edge 1 for the main four variables tested. [ABSTRACT FROM AUTHOR]

European forests are among the most extensively studied ecosystems in the world, yet there are still debates about their recent dynamics. We modeled the changes in tree canopy height across Europe from 2001 to 2021 using the multidecadal spectral data from the Landsat archive and calibration data from Airborne Laser Scanning (ALS) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidars. Annual tree canopy height was modeled using regression tree ensembles and integrated with annual tree canopy removal maps to produce harmonized tree height map time series. From these time series, we derived annual tree canopy extent maps using a ≥ 5 m tree height threshold. The root-mean-square error (RMSE) for both ALS-calibrated and GEDI-calibrated tree canopy height maps was ≤4 m. The user's and producer's accuracies estimated using reference sample data are ≥94% for the tree canopy extent maps and ≥ 80% for the annual tree canopy removal maps. Analyzing the map time series, we found that the European tree canopy extent area increased by nearly 1% overall during the past two decades, with the largest increase observed in Eastern Europe, Southern Europe, and the British Isles. However, after the year 2016, the tree canopy extent in Europe declined. Some regions reduced their tree canopy extent between 2001 and 2021, with the highest reduction observed in Fennoscandia (3.5% net decrease). The continental extent of tall tree canopy forests (≥ 15 m height) decreased by 3% from 2001 to 2021. The recent decline in tree canopy extent agrees with the FAO statistics on timber harvesting intensification and with the increasing extent and severity of natural disturbances. The observed decreasing tree canopy height indicates a reduction in forest carbon storage capacity in Europe. • European tree canopy extent and height change 2001–2021 mapped with Landsat archive. • Continental multidecadal tree height mapping supported by lidar calibration data. • Tree canopy extent increased in Europe by 1%, decreased in Fennoscandia. • Tall forests (height ≥ 15 m) lost 3% of their area. • The annual tree canopy removal area increased by 18% from 2001–2011 to 2012–2021. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) collected in a needle-leaf forest site near Ruokolahti, Finland, during a field campaign in June 14–21, 2000, was used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm. The field LAI data was first related to 30-m resolution Enhanced Thermal Mapper Plus (ETM+) images using empirical methods to create a high-resolution LAI map. The analysis of empirical approaches indicates that preliminary segmentation of the image followed by empirical modeling with the resulting patches, was an effective approach to developing an LAI validation surface. Comparison of the aggregated high-resolution LAI map and corresponding MODIS LAI retrievals suggests satisfactory behavior of the MODIS LAI algorithm although variation in MODIS LAI product is higher than expected. The MODIS algorithm, adjusted to high resolution, generally overestimates the LAI due to the influence of the understory vegetation. This indicates the need for improvements in the algorithm. An improved correlation between field measurements and the reduced simple ratio (RSR) suggests that the shortwave infrared (SWIR) band may provide valuable information for needle-leaf forests. [Copyright &y& Elsevier]

A small set of independent variables generally seems to suffice when attempting to describe the spectral response of a vegetation canopy to incident solar radiation. This set includes the soil reflectance, the single-scattering albedo, canopy transmittance, reflectance and interception, the portion of uncollided radiation in the total incident radiation, and portions of collided canopy transmittance and interception. All of these are measurable; they satisfy a simple system of equations and constitute a set that fully describes the law of energy conservation in vegetation canopies at any wavelength in the visible and near-infrared part of the solar spectrum. Further, the system of equations specifies the relationship between the optical properties at the leaf and the canopy scales. Thus, the information content of hyperspectral data can be fully exploited if these variables can be retrieved, for they can be more directly related to some of the physical properties of the canopy (e.g. leaf area index). This paper demonstrates this concept through retrievals of single-scattering albedo, canopy absorptance, portions of uncollided and collided canopy transmittance, and interception from hyperspectral data collected during a field campaign in Ruokolahti, Finland, June 14–21, 2000. The retrieved variables are then used to estimate canopy leaf area index, vegetation ground cover, and also the ratio of direct to total incident solar radiation at blue, green, red, and near-infrared spectral intervals. [Copyright &y& Elsevier]

The development of appropriate ground-based validation techniques is critical to assessing uncertainties associated with satellite data-based products. In this paper, the second of a two-part series, we present a method for validation of the Moderate Resolution Imaging Spectroradiometer Leaf Area Index (MODIS LAI) product with emphasis on the sampling strategy for field data collection. Using a hierarchical scene model, we divided 30-m resolution LAI and NDVI images from Maun (Botswana), Harvard Forest (USA) and Ruokulahti Forest (Finland) into individual scale images of classes, region and pixel. Isolating the effects associated with different landscape scales through decomposition of semivariograms not only shows the relative contribution of different characteristic scales to the overall variation, but also displays the spatial structure of the different scales within a scene. We find that (1) patterns of variance at the class, region and pixel scale at these sites are different with respect to the dominance in order of the three levels of landscape organization within a scene; (2) the spatial structure of LAI shows similarity across the three sites, that is, ranges of semivariograms from scale of pixel, region and class are less than 1000 m. Knowledge gained from these analyses aids in formulation of sampling strategies for validation of biophysical products derived from moderate resolution sensors such as MODIS. For a homogeneous (within class) site, where the scales of class and region account for most of the spatial variation, a sampling strategy should focus more on using accurate land cover maps and selection of regions. However, for a heterogeneous (within class) site, accurate point measurements and GPS readings are needed. [Copyright &y& Elsevier]

The development of appropriate ground-based validation techniques is critical to assessing uncertainties associated with satellite data-based products. Here we present a method for validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) product with emphasis on the sampling strategy for field data collection. This paper, the first of two-part series, details the procedures used to assess uncertainty of the MODIS LAI product. LAI retrievals from 30 m ETM+ data were first compared to field measurements from the SAFARI 2000 wet season campaign. The ETM+ based LAI map was thus as a reference to specify uncertainties in the LAI fields produced from MODIS data (250-, 500-, and 1000-m resolutions) simulated from ETM+. Because of high variance of LAI measurements over short distances and difficulties of matching measurements and image data, a patch-by-patch comparison method, which is more realistically implemented on a routine basis for validation, is proposed. Consistency between LAI retrievals from 30 m ETM+ data and field measurements indicates satisfactory performance of the algorithm. Values of LAI estimated from a spatially heterogeneous scene depend strongly on the spatial resolution of the image scene. The results indicate that the MODIS algorithm will underestimate LAI values by about 5% over the Maun site if the scale of the algorithm is not matched to the resolution of the data. [Copyright &y& Elsevier]