Landsat8 vs. Sentinel-2: Land Use / Land Cover Change mapping for Karbala Governorate, Iraq, 2017 and 2021

Satellite images are the essential data source for analyzing and monitoring land cover on various time scales, particularly across large regions. Landsat satellite data with a medium resolution was used to estimate land cover change over a 40-year period.This data contains information on land use and land cover patterns, is now freely available in the international archives. The LULC Remote Sensing Study assists in the ongoing detection and mitigation of crucial habitat risks to protect the environment. Sentinel-2, a satellite mission launched by the European Space Agency between 2015 and 2017 that uses high-resolution 10-day time-lapse multispectral data, gives a new opportunity for ground-based mapping and monitoring in the tropics. We employed 2015 ERDAS, a supervised classification method employing the maximum likelihood technique, to achieve this goal in Karbala/Iraq. This study examines if There is a significant difference in quality of data supplied by Landsat 8 and Sentinel-2 photographs in terms of change-detection maps of land use and land coverfor 2017 and 2021, the results of two satellites were compared ,They showed that their overall accuracy increased by 2.07% for 2017 and 1.83% for 2021, which is more overall accuracy than Landsat-8.

The use of spectral techniques to monitor the vegetation status in a protected area in the Iasi county

Remote sensing technology offers the possibility to monitor biophysical attributes and changes in plant biomass and productivity during the growing season, which can enable sustainable management. Recent advances in satellite remote sensing technology have produced innovative sensors for monitoring the Earth's surface, with increasing spatial and temporal resolution of available satellite images, such as those provided by the Sentinel-2, creating new opportunities for environmental monitoring and the generation of accurate datasets. This study aimed to assess vegetation condition during the spring, summer and autumn seasons in a protected area near Iasi, ROSCI0058, using biophysical indices derived from Sentinel-2 satellite imagery. The study area was chosen due to the existence of signals indicating the possibility of changes in the type and health status of vegetation within the site of Community importance. The analysis was based on a series of vegetation-specific spectral indices such as: normalized differential vegetation index (NDVI), leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), fraction of photosynthetically active absorbed radiation (FAPAR) and fraction of canopy cover (FCOVER), derived from Sentinel-2 high-resolution images. The time series of satellite images used covers the phenophase periods specific to the spontaneous flora in the period 2020-2022. With SNAP software the Sentinel-2 images were pre-processed to convert the reflectance of the ToA images to BoA, vegetation indices were calculated, after which final distribution maps were created with ArcGIS. The results indicate that the highest values for NDVI, LAI, FAPAR, FCOVER, CAB and CW did not follow a pattern, they occurred at different times of the year, as follows: in the spring season, the highest value was on April 10, 2020; in the summer season, highlighting the values of July 9, 2021 while for the fall, the year 2022 recorded the highest values on September 7, the results being directly proportional to the variation of climatic parameters. The analysis also considered the type of land use, with non-irrigated arable land having the highest values for various indices. The results highlight the potential of Sentinel-2 images for these types of studies, as they can be used to observe and assess the health of the vegetation cover

Tree extraction and estimation of walnut structure parameters using airborne LiDAR data

[EN] The development of new tools based on remote sensing data in agriculture contributes to cost reduction, increased production, and greater profitability. Airborne LiDAR (Light Detection and Ranging) data show a significant potential for geometrically characterizing tree plantations. This study aims to develop a methodology to extract walnut (Juglans regia L.) crowns under leafless conditions using airborne LiDAR data. An original approach based on the alpha-shape algorithm, identification of local maxima, and k-means algorithms is developed to extract the crowns of walnut trees in a plot located in Viver (Eastern Spain) with 192 trees. In addition, stem diameter and volume, crown diameter, total height, and crown height were estimated from cloud metrics and other 2D parameters such as crown area, and diameter derived from LiDAR data. A correct identification was made of 178 trees (92.7%). For structure parameters, the most accurate results were obtained for crown diameter, stem diameter, and stem volume with coefficient of determination values (R-2) equal to 0.95, 0.87 and 0.83; and RMSE values of 0.43 m (5.70%), 0.02 m (9.35%) and 0.016 m(3) (21.55%), respectively. The models that gave the lowest R-2 values were 0.69 for total height and 0.70 for crown height, with RMSE values of 0.84 m (12.4%) and 0.83 m (14.5%), respectively. A suitable definition of the central and lower parts of tree canopies was observed. Results of this study generate valuable information, which can be applied for improving the management of walnut plantations.Estornell Cremades, J.; Hadas, E.; Marti-Gavila, J.; López- Cortés, I. (2021). Tree extraction and estimation of walnut structure parameters using airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation. 96:1-9. https://doi.org/10.1016/j.jag.2020.102273S199

Assessment of air pollutants removal by green infrastructure and urban and peri-urban forests management for a greening plan in the Municipality of Ferrara (Po river plain, Italy)

Air pollution is a serious concern for human health and is even more worrying in areas that are known to be "pollution hotspots", such as the Po Plain in northern Italy. The Urban Green Infrastructure (UGI), which includes urban and peri-urban forests, enhances human health and wellbeing delivering a wide range of ecosystem services, including air quality improvement. In this research, we analyzed, in biophysical and monetary terms, the role of the UGI in removing PM10 and O-3 from the atmosphere in the Municipality of Ferrara using established removal models. We used a multiscale approach that includes geospatial data, field sampling and laboratory analysis. Then, using a local green areas database, we located public areas that could potentially undergo forestation actions without requiring any land conversion and evaluated the benefit in terms of ESs provision that these actions may exert. We found that, in 2019, the UGI in the Municipality of Ferrara removed about 19.8 Mg of PM10 and 8.6 Mg of O-3, for a monetary benefit of (sic) 2.12 million (sic) and 147*103 respectively. We then identified about 121 ha within the urban core of the Municipality that could potentially be forested. Such an action would increase the PM10 and O-3 removal by about 49% and 18%, respectively. Our findings comply with the EU Biodiversity strategy for 2030, which calls for the development of an ambitious greening plan for cities with more than 20,000 inhabitants

Estimation of Walnut Structure Parameters Using Terrestrial Photogrammetry Based on Structure-from-Motion (SfM)

[EN] Remote sensing techniques are increasingly used for crop monitoring to improve the profitability of plantations. These studies are mainly based on spectral information recorded by satellites or unmanned aerial vehicles. However, the development of Earth Observation Systems capable of retrieving 3D point clouds at an affordable cost enables the possibility of exploring new approaches in agriculture. In this context, more research is required to analyze the capability of 3D data for inventory, management and prediction of inputs (water, fertilizers and pesticides) and outputs (production, biomass) of fruit plantations. To do this, the complete representation of each tree contribute to extract the main geometric parameters. The objective of this work is to obtain regression models to estimate total height (H-t), crown height (H-c), stem diameter (D-s), crown diameter (D-c), stem volume (V-s) and crown volume (V-c) from 45 walnut specimens. For this, 3D models were computed for these trees by applying ground-based Structure from Motion (SfM). A circular photogrammetric survey of each tree was carried out using a standard digital camera and three-dimensional point clouds were retrieved for each tree. From these data, the tree parameters were computed. Linear regression models were obtained to estimate H-t, H-c, D-s, D-c, V-s and V-c, with R-2 values between 0.89 and 0.99. The results showed accurate fits between field parameters and those derived from 3D point clouds retrieved from SfM technique, indicating the applicability of this cost-effective method to model walnut trees and to extract their accurate parameters without costly field campaigns.Fernández-Sarría, A.; López- Cortés, I.; Marti-Gavila, J.; Estornell Cremades, J. (2022). Estimation of Walnut Structure Parameters Using Terrestrial Photogrammetry Based on Structure-from-Motion (SfM). 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Hyper and multi-spectral comparison of Cynodon nlemfuensis pasture under tropical and grazing conditions with dairy cattle

La información espectral ha sido utilizada ampliamente en el estudio de las condiciones nutricionales y en el desarrollo de diversos cultivos empleados en el ámbito agrícola, pero existe un vacío de investigación en especies forrajeras para condiciones tropicales. El presente estudio contempló la comparación de información multi e hiperespectral correspondientes al cultivo de pasto Estrella Africana (Cynodon nlemfuensis) dedicado a la alimentación de ganadería lechera usando espectroscopia de campo e información satelital de Sentinel-2. Se determinó que existe una heterogeneidad de la firma espectral del cultivo, debido al patrón aleatorio de alimentación por parte del ganado y la variación de las condiciones ambientales. Se generaron zonas con distintas alturas del cultivo, inuyendo directamente en los valores de reectancia, índice de área foliar e índices de vegetación. Se compararon los índices espectrales calculados con información de campo y satelital, obteniéndose valores de R2 de 0,725 para el caso del NDVI y de 0,446 para el SAVI. La presente investigación es de gran relevancia ya que sienta la línea base del uso de información espectral para el estudio de pastos dedicados a la alimentación de ganado lechero a partir de sensores remotos y espectrorradiometría de campo.Spectral information has been widely applied to study the growth and nutritional conditions of different crops used in the agricultural field; however, there is a research gap regarding forage crops in tropical conditions. This study compared the multi and hyperspectral information of the African star pasture crop (Cynodon nlemfuensis) for dairy cattle feeding using field spectroscopy and satellite information of Sentinel-2. This study determined spectral signature heterogeneity of this crop due to the randomness of the feeding pattern of the cattle and the continuous change of the environmental conditions. Different crop heights in the sampling areas affected the reflectance values, leaf area index and vegetation indices directly. For the NDVI and SAVI, R2 values of 0,725 and 0,446 were achieved for spectral indices between field and satellite data. This research is relevant because it lays the baseline for the use of spectral information regarding the analysis of tropical pastures employed in dairy cattle feeding using remote sensing and a field spectroradiometer.Universidad de Costa Rica/[340-B5-507]/UCR/Costa RicaUCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería de Biosistema

Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

Leaf area index (LAI) and canopy chlorophyll density (CCD) are key indicators of crop growth status. In this study, we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages. The indices were calculated with Sentinel-2 MSI data and hyperspectral data. Their performances were validated against ground measurements using R2, RMSE, and bias. The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage, head emergence stage, and filling stage. Furthermore, red-edge modified indices outperformed the traditional indices for both data genres. Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early jointing and milk development stage, while indices with long red-edge wavelength estimate the sought variables better at the middle three stages. The results were consistent with the red-edge inflection point shift at different growth stages. The best indices for Sentinel-2 LAI retrieval, Sentinel-2 CCD retrieval, hyperspectral LAI retrieval, and hyperspectral CCD retrieval at five growth stages were determined in the research. These results are beneficial to crop trait monitoring by providing references for crop biophysical and biochemical parameters retrieval

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically-based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals (R2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications