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UID:pretalx-foss4g-it-2023-899M3E@talks.staging.osgeo.org
DTSTART;TZID=GMT:20230612T144500
DTEND;TZID=GMT:20230612T150000
DESCRIPTION:Authors: Claudio Ladisa\, Manuel A. Aguilar\, Alessandra Capolu
 po\, Eufemia Tarantino\, Fernando J. Aguilar.\nThe use of renewable energy
  sources in power generation is increasing due to environmental awareness 
 and technological advancements. Solar energy\, with its extensive availabi
 lity and minimal greenhouse gas emissions\, is a promising source. However
 \, large photovoltaic (PV) plants require constant monitoring to ensure ef
 ficiency and reliability. Remote sensing technology can be beneficial in p
 roviding accurate information on the plant's size\, shape\, and location\,
  reducing costs and increasing monitoring efficiency. The detection of lar
 ge PV plants can be carried out using various technologies\, including the
  use of satellite imagery\, drones imagery or observation from aircraft. H
 owever\, the use of satellite imagery is advantageous for the detection of
  large PV plants because it allows to acquire data on a large area without
  having to move around the site and to monitor the plant over time without
  interfering with its activity. Open-source imagery from satellites like S
 entinel-2 (S2) and Landsat 9 has led to a significant increase in remote s
 ensing research related to extracting (PV) systems. This is because the fr
 ee and public availability of high-quality images with extensive spatial c
 overage has eliminated the need to buy costly private satellite images. Ad
 ditionally\, the frequency of image acquisition\, which can occur every fe
 w days\, has allowed for quick and accurate monitoring of areas of interes
 t. Several research have recently merged remote sensing with machine learn
 ing (ML) methods to develop automatic classification algorithms for PV sys
 tems. Most of these algorithms employ different spectral indices\, such as
  the Normalized Difference Water Index (NDWI)\, the Normalized Difference 
 Vegetation Index (NDVI)\, and Normalized Difference Bare Index (NDBI)\, as
  input. These spectral indices provide useful information on the presence 
 of water\, vegetation and bare soil\, respectively\, which can be used to 
 identify PV systems more accurately\, thus improving classification accura
 cy. However\, there is no specific spectral index that has been tested exc
 lusively for the extraction of PV. This is partially because PV arrays may
  be constructed on many kinds of surfaces\, in various environmental and c
 limatic circumstances\, and with different solar panel sizes and types. In
  this regard\, the goal of this work was to suggest a Photovoltaic Systems
  Extraction Index (PVSEI) for the detection of PV installations from S2 im
 ages in two distinct study areas characterized by the persistent presence 
 of large PV installations: The province of Viterbo (Italy) and the provinc
 e of Seville (Spain). The development of the PVSEI was based on the combin
 ation of different bands provided by S2\, in order to maximise the spectra
 l difference between the solar panels and their surroundings. For each stu
 dy area\, two S2 images\, one taken in February and the other one in Augus
 t\, were used to analyse the seasonal variation of the solar panels' spect
 ral signature and test the PVSEI's accuracy in each of the four scenarios.
  The image analysis was carried out using an Object-Based Image Analysis (
 OBIA) method since it allowed for a more accurate identification of PV sys
 tems than the pixel-based method\, which analyzes individual elements with
 out taking their spatial arrangement and semantic significance into accoun
 t. Multi-resolution segmentation was used to create segments with differen
 t dimensions based on scale\, shape and compactness parameters. The Decisi
 on Tree (DT) classifier was used to evaluate the effectiveness of the PVSE
 I and its importance in comparison to the other indices used in the litera
 ture in both locations and for both periods after the objects had been lab
 elled as "PV" and "No-PV”. The effectiveness of the new index was demons
 trated through the results obtained from the DT analysis. In three out of 
 four scenarios\, the PVSEI was selected as the first cut in the DT analysi
 s. In the remaining scenario where it was not ranked first\, it still main
 tained a high level of significance\, being the second index in importance
 . The accuracy was assessed using an error matrix calculated on both the e
 ntire segmentation dataset (i.e. using all the objects) and with TTA mask 
 with 2 m pixel size. Four metrics were used to evaluate accuracy of the PV
 SEI\, including Overall Accuracy (OA)\, Kappa Index of Agreement (KIA)\, P
 roducer Accuracy (PA)\, and User Accuracy (UA) for both classes. OA exceed
 ed 98% in all scenarios\, both for the segmentation dataset and the TTA ma
 sk. KIA values for the TTA mask ranged from 0.81 to 0.86\, while values fo
 r the segmentation objects ranged from 0.74 to 0.82. In conclusion\, the n
 ew index has demonstrated favourable outcomes in both study areas\, with o
 nly a limited number of misclassifications involving bare soil objects tha
 t have a spectral signature resembling certain photovoltaic systems.
DTSTAMP:20260517T224232Z
LOCATION:Sala Videoconferenza @ PoliBa
SUMMARY:Development of a Photovoltaic System Extraction Index for the detec
 tion of large PV plants using Sentinel-2 images - Alessandra Capolupo\, Eu
 femia Tarantino\, Claudio Ladisa\, Fernando J. Aguilar
URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/899M3E/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-foss4g-it-2023-UGT3XN@talks.staging.osgeo.org
DTSTART;TZID=GMT:20230612T153000
DTEND;TZID=GMT:20230612T154500
DESCRIPTION:Autores: Abderrahim Nemmaoui\, Fernando J. Aguilar\, Manuel A. 
 Aguilar\nForests act as important carbon sinks\, therefore being key compo
 nents of the global carbon cycle. The carbon dioxide emissions account is 
 essential for climate regulation policies and the evaluation of the effect
 s of these policies\, as well as for understanding the services they provi
 de to societies.\nTraditionally\, forest inventories are completed by grou
 nd-based expert crews. These field surveys are uneconomical\, time consumi
 ng and not adequate for studies dealing with periodic data collection. Con
 sequently\, one of the key topic in forest applications is to find an effe
 ctive method to produce effective and accurate inventories.\nIn recent yea
 rs\, Remote Sensing (RS) has proven to be capable of providing independent
 \, timely and reliable forest information. RS data are used to estimate se
 veral forest variables of silvicultural interest such as crown diameter (C
 D)\, tree height (H)\, diameter at breast height (DBH) and aboveground bio
 mass (AGB). In this sense\, and due to its ability to estimate attributes 
 at tree level\, LiDAR derive point cloud data has become a valuable data s
 ource in the field of efficient and accurate detection and segmentation of
  individual trees (IT).\nState-of-the-art approaches use different algorit
 hms for individual tree segmentation (ITS). For each algorithm\, a specifi
 c methodology to create the input Canopy Height Model (CHM) and/or many pa
 rameters should be tuned to somehow adapt the segmentation algorithm to ea
 ch particular forest stand. This approach makes the results highly depende
 nt on the applied local fitting parameters\, which implies difficulties wh
 en applied for large-scale mapping. In addition\, the parameter setting pr
 ocess is quite time consuming and requires learning and understanding the 
 meaning and role of each parameter.\nThe main goal of this work aims at de
 veloping a pipeline that requires minimal user interaction when working on
  large areas of Mediterranean forests. The expected results should facilit
 ate the production of broad-extend IT maps and extract the corresponding d
 endrometric parameters from low-density airborne laser scanning (ALS) data
  without spending time tuning algorithm parameters. \nThe study area was l
 ocated in Sierra de María-Los Vélez Natural Park (Almeria\, Spain). Up t
 o 38 reference square plots of 25 m side containing reforested stands of A
 leppo pine (Pinus halepensis Mill.) with variable density\, tree height an
 d presence of shrubs and low vegetation mainly represented by little holm 
 oak trees (Quercus ilex L.). This forest composition and structure make up
  a forest typology that is very representative of the Mediterranean forest
 s.\nThree open source raster-based (i.e.\, CHM-based) were tested to extra
 ct tree location and some dendrometric parameters such as tree H and CD. T
 he first algorithm is the method proposed by Dalponte & Coomes(2016) adapt
 ed and introduced in the package lidR (Roussel et al.2020). The second one
  is the algorithm developed by Silva et al.(2016)\, which is focused on th
 e way to better approximating the intersecting canopy of multiple trees af
 ter locating treetops by local maxima. The last algorithm tested is includ
 ed in the library Digital Forestry Toolbox (DFT). In addition\, the point 
 cloud-based algorithm proposed by Li et al.(2012) was also tested. \nFor e
 very algorithm tested\, we tried different parameters to find the best pip
 eline\, finally obtaining up to 4024 combinations of all tested algorithms
  for each experimental plot. For each setting\, tree detection accuracy wa
 s assessed by computing the detection rate\, and the commission and omissi
 on errors. Some statistics\, such as median\, RMSE and relative RMSE\, wer
 e also used to quantitatively assess the accuracy of tree H and CD estimat
 es over each reference plot.\nThe IT detection accuracy rates\, in terms o
 f precision\, recall\, and F1-score\, showed the successful performance of
  the pipeline proposed in this study. The algorithm proposed by Li et al.(
 2012) showed detection F1-score average values of 82.65% (using the same p
 arameter combination for the 38 experimental plots). However\, it failed i
 n delimiting the crown diameter (relative RMSE 57.06% and Pearson r of 0.5
 5). The method developed by Silva et al.(2016)\, when applied on a CHM gen
 erated with the point-to-raster algorithm and using a LM based on a variab
 le Tree Window Size (TWS)\, presented a similar F1-score for ITS (i.e.\, 8
 2.53%)\, but being most successful delimiting the crown (relative RMSE 22.
 21% and Pearson r of 0.68). Finally\, Dalponte & Coomes(2016) and DFT meth
 ods showed slightly worse results\, with average F1-scores of 80.41% and 7
 5.66%\, respectively.\nThe results obtained confirms the usefulness of low
 -density ALS data to both detect IT and estimate H and CD\, also underlini
 ng some key aspects regarding the choice of the correct method and paramet
 ers to perform single tree detection for Aleppo pine in large areas of Med
 iterranean forests.
DTSTAMP:20260517T224232Z
LOCATION:Sala Biblioteca @ PoliBa
SUMMARY:AN AUTOMATIC AND EFFECTIVE PIPELINE FOR INDIVIDUAL TREE DETECTION A
 ND SEGMENTATION USING LOW-DENSITY AIRBORNE LASER SCANNING DATA IN LARGE AR
 EAS OF MEDITERRANEAN FOREST - Abderrahim\, Fernando J. Aguilar
URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/UGT3XN/
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