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Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25–0.32), to moderate (0.48–0.64) and high (0.72–0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model. [ABSTRACT FROM AUTHOR]

Highlights • TLS can be used to estimate the impact of management practices on forest structure. • TLS can complement conventional measures of forest structure. • In Ethiopia, forest structure measures differed between the forest management types. • Coffee forest was similar to silvopasture, indicating significant forest structure loss. • Thresholds, e.g. canopy openness, may improve sustainable coffee certification schemes. Abstract Increasing anthropogenic pressure leads to loss of habitat through deforestation and degradation in tropical forests. While deforestation can be monitored relatively easily, forest management practices are often subtle processes, that are difficult to capture with for example satellite monitoring. Conventional measurements are well established and can be useful for management decisions, but it is believed that Terrestrial Laser Scanning (TLS) has a role in quantitative monitoring and continuous improvement of methods. In this study we used a combination of TLS and conventional forest inventory measures to estimate forest structural parameters in four different forest types in a tropical montane cloud forest in Kafa, Ethiopia. Here, the four forest types (intact forest, coffee forest, silvopasture, and plantations) are a result of specific management practices (e.g. clearance of understory in coffee forest), and not different forest communities or tree types. Both conventional and TLS derived parameters confirmed our assumptions that intact forest had the highest biomass, silvopasture had the largest canopy gaps, and plantations had the lowest canopy openness. Contrary to our expectations, coffee forest had higher canopy openness and similar biomass as silvopasture, indicating a significant loss of forest structure. The 3D vegetation structure (PAVD – Plant area vegetation density) was different between the forest types with the highest PAVD in intact forest and plantation canopy. Silvopasture was characterised by a low canopy but high understorey PAVD, indicating regeneration of the vegetation and infrequent fuelwood collection and/or non-intensive grazing. Coffee forest canopy had low PAVD, indicating that many trees had been removed, despite coffee needing canopy shade. These findings may advocate for more tangible criteria such as canopy openness thresholds in sustainable coffee certification schemes. TLS as tool for monitoring forest structure in plots with different forest types shows potential as it can capture the 3D position of the vegetation volume and open spaces at all heights in the forest. To quantify changes in different forest types, consistent monitoring of 3D structure is needed and here TLS is an add-on or an alternative to conventional forest structure monitoring. However, for the tropics, TLS-based automated segmentation of trees to derive DBH and biomass is not widely operational yet, nor is species richness determination in forest monitoring. Integration of data sources is needed to fully understand forest structural diversity and implications of forest management practices on different forest types. [ABSTRACT FROM AUTHOR]

Historic land-cover/use change is important for studies on climate change, soil carbon, and biodiversity assessments. Available reconstructions focus on the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). This leads to a serious underestimation of land-cover/use dynamics with impacts on the biogeochemical and environmental assessments based on these reconstructions. In this study, we quantified to what extent land-cover/use reconstructions underestimate land-cover/use changes in Europe for the 1900-2010 period by accounting for net changes only. We empirically analyzed available historic land-change data, quantified their uncertainty, corrected for spatial-temporal effects and identified underlying processes causing differences between gross and net changes. Gross changes varied for different land classes (largest for forest and grassland) and led to two to four times the amount of net changes. We applied the empirical results of gross change quantities in a spatially explicit reconstruction of historic land change to reconstruct gross changes for the EU27 plus Switzerland at 1 km spatial resolution between 1950 and 2010. In addition, the reconstruction was extended back to 1900 to explore the effects of accounting for gross changes on longer time scales. We created a land-change reconstruction that only accounted for net changes for comparison. Our two model outputs were compared with five commonly used global reconstructions for the same period and area. In our reconstruction, gross changes led in total to a 56% area change (ca. 0.5% yr−1) between 1900 and 2010 and cover twice the area of net changes. All global reconstructions used for comparison estimated fewer changes than our gross change reconstruction. Main land-change processes were cropland/grassland dynamics and afforestation, and also deforestation and urbanization. [ABSTRACT FROM AUTHOR]

• SIF parameters from a UAV were used to detect crop water stress. • UAV-based hyperspectral and thermal data assisted the interpretation of SIF response. • The selected SIF indicators were able to track drought recovery after irrigation. • The fluorescence emission yield was more sensitive to drought than the apparent SIF yield. Sun-induced chlorophyll fluorescence (SIF) is a direct indicator of plant photosynthetic activities and can potentially indicate plant physiological changes caused by water stress. However, the direct effect of water stress on the physiological SIF responses in crops at the field level still needs further research to clearly understand the involved mechanisms. To study this relationship, we made use of Unmanned Aerial Vehicles (UAVs), which are flexible and cost-effective to acquire SIF data at a high temporal resolution. We acquired near-infrared SIF (760 nm) and red SIF (687 nm) measurements using a UAV platform over irrigated and non-irrigated sugar beet plots. To represent physiological changes in crops, we calculated the apparent SIF yield (SIF normalized by the absorbed photosynthetically active radiation) at 760 and 687 nm (SIF 760yield and SIF 687yield), the fluorescence emission yield at 760 nm (ΦF 760), and the SIF ratio (the ratio between SIF 687 and SIF 760). ΦF 760 was estimated using the recently developed NIRvH approach. For an improved interpretation of the response of these SIF indicators, we also acquired additional UAV-based hyperspectral and thermal data. We found that on June 28, when sugar beets were experiencing water stress, SIF 687yield , ΦF 760 , and SIF ratio all showed a significant response to the recovery of the irrigated sugar beets (p-value < 0.05). On the other hand, on July 24 when both water stress and heat stress affected the crop, only ΦF 760 and SIF ratio weakly tracked the changes induced by the irrigation (p-value < 0.1). ΦF 760 had similar changes to SIF 760yield in both June and July, but ΦF 760 was more sensitive to irrigation. This indicates the importance of correcting for the structural effect when interpreting the SIF response. The findings suggest that SIF indicators can indicate water stress at the field level, but its value to detect the changes of photosynthetic activities under severe stress needs more investigation. [ABSTRACT FROM AUTHOR]

Highlights: [•] We review up-to-date remote sensing methods to estimate important plant traits. [•] We introduce basic concepts of remote sensing of vegetation and scaling mechanisms. [•] Plant height and nitrogen are retrieved with high accuracy from remote sensing. [•] Phosphorus and leaf mass per area are retrieved with lower accuracies. [Copyright &y& Elsevier]

Abstract: Since the launch of sensors with angular observation capabilities, such as CHRIS and MISR, the additional potential of multi-angular observations for vegetation structural and biochemical variables has been widely recognised. Various methods have been successfully implemented to estimate forest biochemical and biophysical variables from atmospherically-corrected multi-angular data, but the use of physically based radiative transfer (RT) models is still limited. Because both canopy and atmosphere have an anisotropic behaviour, it is important to understand the multi-angular signal measured by the sensor at the top of the atmosphere (TOA). Coupled canopy–atmosphere RT models allow linking surface variables directly to the TOA radiance measured by the sensor and are therefore very interesting tools to use for estimating forest variables from multi-angular data. We investigated the potential of TOA multi-angular radiance data for estimating forest variables by inverting a coupled canopy–atmosphere physical RT model. The case study focussed on three Norway spruce stands located at the Bily Kriz experimental site (Czech Republic), for which multi-angular CHRIS and field data were acquired in September 2006. The soil–leaf–canopy RT model SLC and the atmospheric model MODTRAN4 were coupled using a method allowing to make full use of the four canopy angular reflectance components provided by SLC. The TOA radiance simulations were in good agreement with the spectral and angular signatures measured by CHRIS. Singular value decompositions of the Jacobian matrices showed that the dimensionality of the variable estimation problem increased from 3 to 6 when increasing the number of observation angles from 1 to 4. The model inversion was conducted for two cases: 4 and 7 variables. The most influential parameters were chosen as free variables in the look-up tables, namely: vertical crown cover (Cv), fraction of bark material (fB), needle chlorophyll content (needleCab), needle dry matter content (needleCdm) for the 4-variable case, and additionally, tree shape factor (Zeta), dissociation factor (D), and needle brown pigments content (needleCs) in the 7-variable case. All angular combinations were tested, and the best estimates were obtained with combinations using two or three angles, depending on the number of variables and on the stand used. Overall, this case study showed that, although making use of its full potential is still a challenge, TOA multi-angular radiance data do have a higher potential for variable estimation than mono-angular data. [Copyright &y& Elsevier]

Abstract: Traditionally, it is necessary to pre-process remote sensing data to obtain top of canopy (TOC) reflectances before applying physically-based model inversion techniques to estimate forest variables. Corrections for atmospheric, adjacency, topography, and surface directional effects are applied sequentially and independently, accumulating errors into the TOC reflectance data, which are then further used in the inversion process. This paper presents a proof of concept for demonstrating the direct use of measured top-of-atmosphere (TOA) radiance data to estimate forest biophysical and biochemical variables, by using a coupled canopy–atmosphere radiative transfer model. Advantages of this approach are that no atmospheric correction is needed and that atmospheric, adjacency, topography, and surface directional effects can be directly and more accurately included in the forward modelling. In the case study, we applied both TOC and TOA approaches to three Norway spruce stands in Eastern Czech Republic. We used the SLC soil–leaf–canopy model and the MODTRAN4 atmosphere model. For the TOA approach, the physical coupling between canopy and atmosphere was performed using a generic method based on the 4-stream radiative transfer theory which enables full use of the directional reflectance components provided by SLC. The method uses three runs of the atmosphere model for Lambertian surfaces, and thus avoids running the atmosphere model for each new simulation. We used local sensitivity analysis and singular value decomposition to determine which variables could be estimated, namely: canopy cover, fraction of bark, needle chlorophyll, and dry matter content. TOC and TOA approaches resulted in different sets of estimates, but had comparable performance. The TOC approach, however, was at its best potential because of the flatness and homogeneity of the area. On the contrary, the capacities of the TOA approach would be better exploited in heterogeneous rugged areas. We conclude that, having similar performance, the TOA approach should be preferred in situations where minimizing the pre-processing is important, such as in data assimilation and multi-sensor studies. [Copyright &y& Elsevier]

• Pronounced diurnal patterns found for both UAV- and Airborne-based far-red SIF. • Consistent spatial patterns of UAV- and Airborne-based far-red SIF over different crops. • FluorSpec far-red SIF is linearly related to HyPlant far-red SIF. • Far-red sun-induced chlorophyll fluorescence can be reliably measured from a UAV. Sun-induced chlorophyll fluorescence (SIF) is a promising proxy of the dynamic photosynthetic process. Unmanned Aerial Vehicles (UAVs) are flexible and cost-effective for acquiring SIF data at high temporal and spatial resolution. The UAV-based point spectrometer FluorSpec was designed to measure SIF within agricultural fields. To correctly understand SIF values and further photosynthetic research, the ability of the UAV-based FluorSpec to provide reliable SIF information within agricultural fields needs evaluation. In this paper, the UAV-based FluorSpec was compared with the high-performance airborne imaging spectrometer HyPlant using diurnal far-red SIF measurements over different crop types (i.e. two varieties of winter wheat, two varieties of spring barley, bean, and maize), which were acquired by almost simultaneous airborne and UAV flights during a clear sky day in 2019. After improving the footprint geolocation of FluorSpec measurements using concurrent red-green-blue (RGB) images, we compared the FluorSpec and HyPlant SIF measurements, their diurnal developments, and spatial distributions for different crop types. The results from both systems show consistent, clear diurnal patterns that are positively correlated with photosynthetically active radiation (PAR) over most crop types. Similar SIF spatial patterns were shown within crop fields as well. UAV-based FluorSpec SIF showed a good linear correlation with HyPlant SIF with an R 2 up to 0.76. The good agreement confirms that the UAV-based FluorSpec system is able to measure meaningful SIF values at the field scale and thus stimulates SIF applications in agriculture. The systematic errors up to 0.3 mW m−2 sr−1 nm−1 from the linear regression between the two systems indicate that the UAV-based FluorSpec system should be improved by considering the main sources of uncertainty discussed in this paper. Future studies with dedicated experiments are recommended to assess the systematic uncertainties of UAV-based FluorSpec derived SIF information. [ABSTRACT FROM AUTHOR]

Abstract: An important bio-indicator of actual plant health status, the foliar content of chlorophyll a and b (Cab), can be estimated using imaging spectroscopy. For forest canopies, however, the relationship between the spectral response and leaf chemistry is confounded by factors such as background (e.g. understory), canopy structure, and the presence of non-photosynthetic vegetation (NPV, e.g. woody elements)—particularly the appreciable amounts of standing and fallen dead wood found in older forests. We present a sensitivity analysis for the estimation of chlorophyll content in woody coniferous canopies using radiative transfer modeling, and use the modeled top-of-canopy reflectance data to analyze the contribution of woody elements, leaf area index (LAI), and crown cover (CC) to the retrieval of foliar Cab content. The radiative transfer model used comprises two linked submodels: one at leaf level (PROSPECT) and one at canopy level (FLIGHT). This generated bidirectional reflectance data according to the band settings of the Compact High Resolution Imaging Spectrometer (CHRIS) from which chlorophyll indices were calculated. Most of the chlorophyll indices outperformed single wavelengths in predicting Cab content at canopy level, with best results obtained by the Maccioni index ([R 780 − R 710]/[R 780 − R 680]). We demonstrate the performance of this index with respect to structural information on three distinct coniferous forest types (young, early mature and old-growth stands). The modeling results suggest that the spectral variation due to variation in canopy chlorophyll content is best captured for stands with medium dense canopies. However, the strength of the up-scaled Cab signal weakens with increasing crown NPV scattering elements, especially when crown cover exceeds 30%. LAI exerts the least perturbations. We conclude that the spectral influence of woody elements is an important variable that should be considered in radiative transfer approaches when retrieving foliar pigment estimates in heterogeneous stands, particularly if the stands are partly defoliated or long-lived. [Copyright &y& Elsevier]

Abstract: We use the Li-Strahler geometric-optical model combined with a scaling-based approach to detect forest structural changes in the Three Gorges region of China. The physical-based Li-Strahler model can be inverted to retrieve forest structural properties. One of the main input variables for the inverted model is the fractional component of sunlit background, which is calculated by using pure reflectance spectra (endmembers) of surface components. In this study, we extract these endmembers from moderate spatial resolution MODIS data using two scaling-based methods (namely, a regional based linear unmixing and a purest-pixel approach) relying on corresponding high spatial resolution Landsat TM images. Then, the forest structural property crown closure (CC) is estimated by inverting the Li-Strahler model based on the extracted endmembers. Changes in CC are mapped using MODIS mosaics dated 2002 and 2004 for the whole Three Gorges region. Validation of the estimated CC using 25 sample sites indicates that the regional scaling-based endmembers extracted using linear unmixing are more suitable to be used in combination with the inverted Li-Strahler model for monitoring the forest CC than the purest-pixel approach, and results in significantly better estimates in both years (R 2 2002 =0.614, RMSE2002 =6%, R 2 2004 =0.631 and RMSE2004 =5.2%). A change detection map of the model derived CC in 2002 and 2004 shows a decrease in CC in the eastern counties of the Three Gorges region located close to the Three Gorges Dam. An increase in CC has been observed in other counties of the Three Gorges region, implying a preliminary positive feedback on certain policy measures taken safeguarding forest structure. [Copyright &y& Elsevier]

A detailed sensitivity analysis investigating the effect of woody elements introduced into the Discrete Anisotropic Radiative Transfer (DART) model on the nadir bidirectional reflectance factor (BRF) for a simulated Norway spruce canopy was performed at a very high spatial resolution (modelling resolution 0.2 m, output pixel size 0.4 m). We used such a high resolution to be able to parameterize DART in an appropriate way and subsequently to gain detailed understanding of the influence of woody elements contributing to the radiative transfer within heterogeneous canopies. Three scenarios were studied by modelling the Norway spruce canopy as being composed of i) leaves, ii) leaves, trunks and first order branches, and finally iii) leaves, trunks, first order branches and small woody twigs simulated using mixed cells (i.e. cells approximated as composition of leaves and/or twigs turbid medium, and large woody constituents). The simulation of each scenario was performed for 10 different canopy closures (CC=50–95%, in steps of 5%), 25 leaf area index (LAI=3.0–15.0 m2 m−2, in steps of 0.5 m2 m−2), and in four spectral bands (centred at 559, 671, 727, and 783 nm, with a FWHM of 10 nm). The influence of woody elements was evaluated separately for both, sunlit and shaded parts of the simulated forest canopy, respectively. The DART results were verified by quantifying the simulated nadir BRF of each scenario with measured Airborne Imaging Spectroradiometer (AISA) Eagle data (pixel size of 0.4 m). These imaging spectrometer data were acquired over the same Norway spruce stand that was used to parameterise the DART model. The Norway spruce canopy modelled using the DART model consisted of foliage as well as foliage including robust woody constituents (i.e. trunks and branches). All results showed similar nadir BRF for the simulated wavelengths. The incorporation of small woody parts in DART caused the canopy reflectance to decrease about 4% in the near-infrared (NIR), 2% in the red edge (RE) and less than 1% in the green band. The canopy BRF of the red band increased by about 2%. Subsequently, the sensitivity on accounting for woody elements for two spectral vegetation indices, the normalized difference vegetation index (NDVI) and the angular vegetation index (AVI), was evaluated. Finally, we conclude on the importance of including woody elements in radiative transfer based approaches and discuss the applicability of the vegetation indices as well as the physically based inversion approaches to retrieve the forest canopy LAI at very high spatial resolution. [Copyright &y& Elsevier]