Natural disturbances induced by insect outbreaks have increased in forest ecosystems over the past decades. To minimize economic loss and prevent a mass outbreak, early detection of bark beetle green attack – a period when trees have yet to show visual signs of infestation stress – is therefore crucial to effective and timely forest management. In this study, we evaluated the ability of spectral vegetation indices extracted from Landsat‐8 and Sentinel‐2 imagery to map bark beetle green attack using principal component analysis (PCA) and partial least square discriminate analysis (PLS‐DA). A recent infestation map produced through visual interpretation of high‐resolution aerial photographs validated the final infestation output maps. Leaf spectral measurements alongside total chlorophyll and nitrogen concentration, leaf water content and leaf dry matter content were measured to assess the impact of bark beetle green attack on foliar properties. We observed that the majority of spectral vegetation indices (SVIs) calculated from Sentinel‐2, particularly red‐edge dependent indices (NDRE2 and 3) and water‐related indices (SR‐SWIR,NDWI,DSWIandLWCI), were able to discriminate healthy from infested plots. In contrast, only the water‐related indices (NDWI,DSWIandRDI) from Landsat‐8 were able to discriminate between healthy and infested plots efficiently. The total number of pixels identified as harboring a green attack that matched with ground truth data (aerial photography) was higher for Sentinel‐2 (67%) than for Landsat‐8 (36%)SVIs, indicating the elevated sensitivity of Sentinel‐2 imagery to changes induced by bark beetle green attack. We also determined that foliar chlorophyll and leaf water content were significantly higher (P < 0.05) in healthy trees than in green‐attacked trees. Our study highlights the potential of Sentinel‐2 data for the early detection of bark beetle infestations and the production of reliable infestation maps at the green‐attack stage.

In this study, the early stage of European spruce bark beetle (Ips typographus, L.) infestation (so-called green attack) is investigated using Landsat-8 optical and thermal data. We conducted an extensive field survey in June and the beginning of July 2016, to collect field data measurements from several infested and healthy trees in the Bavarian Forest National Park (BFNP), Germany. In total, 157 trees were selected, and leaf traits (i.e. stomatal conductance, chlorophyll fluorescence, and water content) were measured. Three Landsat-8 images from May, July, and August 2016 were studied, representing an early stage, advanced stage, and post-infestation, respectively. Spectral vegetation indices (SVIs) sensitive to the measured traits were calculated from the optical domain (VIS, NIR, and SWIR), and canopy surface temperature (CST) was calculated from the thermal infrared band using the mono-window algorithm. The leaf traits were used to examine the impact of bark beetle infestation on the infested trees and to explore the link between these traits and remote sensing data (CST and SVIs). The differences between healthy and infested samples regarding measured leaf traits were assessed using Student’s t test. The relative importance of the CST and SVIs for estimating measured leaf traits was evaluated based on the variable importance in projection (VIP) obtained from the partial least squares regression (PLSR) analysis. A temporal comparison was then made for SVIs with a VIP > 1, including CST, using statistical significance tests. The clustering method using a principal components analysis (PCA) was used to examine visually how well the two groups of sample plots (healthy and infested) are separated in 2-D space based on principal component scores. Finally, linear regression (LR) was used to generate the leaf traits maps using the SVI that have highest VIP score and then used to produce a stress map for the study area. The results revealed that all measured leaf traits were significantly different (p < 0.05) between healthy versus infested samples. Moreover, the study showed that CST was superior to the SVIs in detecting subtle canopy changes due to bark beetle infestation for the three months considered in this study. The results showed that CST is an essential variable for estimating measured leaf traits with VIP > 1, improving the results of clustering when used with other SVIs. Likewise, the stress map produced by CST and leaf traits well presented the infestation areas at the green attacked stage. The new insight offered by this study is that the stress induced by the early stage of bark beetle infestation is more pronounced by Landsat-8 thermal bands than the SVIs calculated from its optical bands. The potential of CST in detecting the green attack stage would have positive implications for forest practice.

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (K_c). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based K_c and NDWI, NDRE, NDVI, and LAI-based K_c were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET₀) and VI-based K_c. The weather station data of CER were used to calculate ET₀ based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A field campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. A normalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artificial neural network was used to predict the LAI using: (i) reflectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reflectance data from the OLI sensor and the land surface emissivity; and (iii) reflectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modified vegetation index had the highest accuracy for LAI prediction (R²_CV = 0.33, RMSE_CV = 1.21 m²m⁻²). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artificial neural network, the prediction accuracy of LAI increased (R²_CV = 0.58, RMSE_CV = 0.83 m²m⁻²). The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R²_CV = 0.81, RMSE_CV = 0.63 m²m⁻²). For the first time, our results demonstrate that the combination of Landsat-8 reflectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This finding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.

Water scarcity is a prominent consequence of global climate change, presenting a significant challenge to the livelihoods of wide parts of the world, particularly in arid and semi-arid regions. This study focuses on Erbil Province in Iraq, where the dual effects of climate change and human activity have significantly depleted water resources in the past two decades. To address this challenge, rainwater harvesting (RWH) is explored as a viable solution. The purpose of this study is to make a suitability zone map that divides the study area into several classes based on the features of each area and its ability to collect rainwater. The map will then be used to find the best place to build different RWH structures. Seven different layers are used to make the RWH suitability zone map: rainfall, runoff, land use/cover (LU/LC), soil texture, slope, drainage density, and the Topographic Wetness Index (TWI). Each layer was assigned specific weights through the Analytical Hierarchy Process (AHP), considering its relevance to RWH. Results revealed four suitability classes: very highly suitable 1583.25 km² (10.67%), highly suitable 4968.55 km² (33.49%), moderately suitable 5295.65 km² (35.69%), and lowly suitable 2989.66 km² (20.15%). Notably, the suitability map highlights the northern and central regions as particularly suitable for RWH. Furthermore, the study suggested three suitable locations for constructing medium dams, six for check dams, and twenty-seven for farm ponds, according to the requirements of each type. These findings provide valuable insights for the strategic planning and effective management of water resources in the study area, offering potential solutions to the pressing challenges of water scarcity.