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Forests are one of the most significant and major carbon sinks. Understanding the global carbon cycle requires assessing the quantity of carbon stored in forests. Remote sensing has been widely employed for estimating forest biomass from local to global. Several forest factors have been mapped or simulated using remote sensing data alone or in conjunction with field data, making forest mapping a broad field. The use of hyperspectral sensors has dominated the past ten years. The potential of the spaceborne hyperspectral sensor PRISMA is the main focus of this study. The goal of this study is to provide the most up-to-date information on hyperspectral remote sensing from space by focusing on estimating aboveground biomass (AGB) and the feasibility of PRISMA in the challenging phenological circumstances of a tropical dry deciduous forest. The objective of the study was to estimate AGB using PRISMA data and to check the phenological variations of AGB. Findings showed that the employed vegetation indices (c) and phenological conditions significantly impacted the predicted accuracy. Results conclude that the atmospherically resistant vegetation index outperforms the enhanced vegetation index (EVI), normalized difference vegetation index, and simple ratio index for PRISMA, with MAE = 5.42 t/ha, RMSE = 6.43 t/ha, and R2 = 0.34. It is also predicted that adverse phenological circumstances were the cause of the poor correlation between field biomass and predicted biomass. To check the phenological variation, AGB was also assessed by Landsat 8 data using the same vegetation indices, in which EVI performed better than others with a 0.72 R2 value. The study indicated that phenological variations have a significant impact on AGB estimation, and narrowband indices can be useful in such studies. [ABSTRACT FROM AUTHOR]

This paper aims to improve the methodology and results accuracy of MEDALUS model for assessing land degradation sensitivity through the application of different data detail levels and by introducing the application of Ellenberg indices in metrics related to vegetation drought sensitivity assessment. For that purpose, the MEDALUS model was applied at 2 levels of detail. Level I (municipality level) implied the use of available large-scale databases and level II (watershed) contains more detailed information about vegetation used in the calculation of the VQI and MQI factors (Fig. S6). The comparison was made using data based on CORINE Land Cover (2012) and forest inventory data, complemented with object-based classification. Results showed that data based on forest inventory data with the application of Ellenberg's indices and object-based classification have one class more, critical (C1 and C2) and that the percentage distribution of classes is different in both quantitative (area size of class sensitivity) and qualitative (aggregation and dispersion of sensitivity classes). The use of data from Forest Management Plans and the application of Ellenberg's indices affect the quality of the results and find its application in the model, especially if these results are used for monitoring and land area management on fine scales. Remote sensed data images (Sentinel-2B) were introduced into the methodology as a very important environmental monitoring tool and model results validation. [ABSTRACT FROM AUTHOR]

This paper presents a spectral reflectance characterization of the specie Quillaja saponaria (Mol.), endemic tree of Chile and valued by society due to its provision of several ecosystem services that gives to society and also for its high concentration of saponins in cortex widely used in the pharmacological industry. For spectral characterization a foliar spectral signatures protocol was designed which included standardized instrumental and environmental parameters. The spectral response of different individuals was measured to evaluate the spectral behaviour and degree of variability within species in the visible and near infrared ranges (VNIR; 400-990 nm) with two hyperspectral sensors (ASD HH and camera PDF-65-V10E). The resulting spectral signatures obtained with ASD HH showed a variation less than 5% of reflectance in VNIR and lesser than that in the transition zone from red to near infrared (red-edge; 680-730 nm). Additionally, two distinctive spectral features were detected for the specie, the first is related to a fast increase of reflectance in bands 450-480 nm and the second, to a marked decrease in the 920-970 nm range associated with water absorption features. At branch level, these distinctive features are maintained but with a smaller magnitude of reflectance, which could indicate that they are useful characteristic spectral patterns that can eventually be used for monitoring the physical health state of the specie using remote sensing. On the other hand, we used a PDF-65 camera for study the plant vigour from different health states (healthy, ill, died) with spectral vegetation index. The Plant Senescence Reflectance Index detected stress on leaves, and Triangular Vegetation Index allows for a gradually characterization of every state. This work provides the first spectral reference for one of the most important sclerophyll species of Chile.

The retrieval of crop biophysical parameters using spectral indices obtained from high temporal and spatial resolution satellite data, is a valuable tool to monitor crop growth and status. Tasseled Cap Features (TCFs) for RapidEye data were derived from spectral variances typically present in agricultural scenes. The TCF Greenness (GRE) was aligned to the spectral variance of vital vegetation, and therefore, it represents the typical reflectance characteristics of green vegetation, with relatively higher reflectance at the nearinfrared (NIR) range. The TCF Yellowness (YEL) was aligned to correspond to the reflectance characteristics of senescent crops, with relatively higher reflectance in the visible portion of the spectrum due to chlorophyll breakdown, and lower reflectance in the NIR range due to cell structure decomposition compared to vital green vegetation. The goal of this work was to assess the potential of RapidEye's TCFs for the prediction of green leaf area index (LAI), plant chlorophyll (Chl), and nitrogen (N) concentration, as well as the identification of senescence patterns. The linear relationships between the biophysical parameters and the TCFs were compared to the performance of the widely used indices NDVI and PSRI. Preliminary results indicate that GRE is strongly related to LAI in vital crops and suggests a higher predictive power than NDVI. YEL demonstrated a strong linear relation and a higher potential to estimate Chl and N concentration in senescent soft white winter wheat (Triticum aestivum L.) in comparison to PSRI. PSRI showed a stronger correlation to Chl in senescent soft white spring wheat (Triticum aestivum L.), compared to YEL. Results indicate that YEL may be used to characterize the variability in senescence status within fields. This information, in conjunction with soil fertility and yield maps, can potentially be used to designate precision management zones. [ABSTRACT FROM AUTHOR]

• We tested the role of vegetation structure, NDVI and PSRI for bird diversity. • Shrub cover and tree cover had strong positive effects on bird richness. • The PSRI, shrub cover and herbaceous cover had positive effects on bird rarity. • Heterogeneous vertical vegetation structure promotes bird richness and rarity. • Combining forests with spontaneous succession will balance richness and rarity. Management of restored areas requires ecologically meaningful spatial data providing objective measures of restoration success. Understanding relationships between species diversity on the one hand and habitat heterogeneity and productivity on the other can help establish such measures and prioritize restoration management. We used airborne LiDAR and hyperspectral data to derive characteristics of vegetation structure, primary productivity and senescent vegetation (i.e. old dead vegetation) for prediction of richness and rarity of bird communities colonizing newly available habitats restored after coal mining. In addition, we analysed, which type of restoration (i.e. agricultural, forest, or spontaneous succession) results in more favourable conditions. The boosted regression trees explained 52% and 12% of deviance of overall species richness and rarity, respectively. We found that the overall species richness was strongly affected by the variance in vegetation structure, while the rarity was also affected by the presence of senescent vegetation. The relative importance of variables differed between the richness and rarity. The shrub cover had a strong positive effect on both, while the tree cover had a strong positive effect on species richness. The herbaceous cover and presence of senescent vegetation had positive effects on species rarity. This study, therefore, supports the necessity to create a mosaic of habitats with heterogeneous vertical structure including all layers of vegetation and highlights the importance of senescent vegetation. Combination of forests restoration with sites left to spontaneous succession appears to be the best strategy to increase both bird species richness and rarity in newly restored sites after coal mining. [ABSTRACT FROM AUTHOR]