Assessing the use of discrete, full-waveform LiDAR and TLS to classify Mediterranean forest species composition

Revista oficial de la Asociación Española de Teledetección[EN] LiDAR technology –airborne and terrestrial- is becoming more relevant in the development of forest inventories, which are crucial to better understand and manage forest ecosystems. In this study, we assessed a classification of species composition in a Mediterranean forest following the C4.5 decision tree. Different data sets from airborne laser scanner full-waveform (ALSFW), discrete (ALSD) and terrestrial laser scanner (TLS) were combined as input data for the classification. Species composition were divided into five classes: pure Quercus ilex plots (QUI); pure Pinus halepensis dense regenerated (HALr); pure P. halepensis (HAL); pure P. pinaster (PIN); and mixed P. pinaster and Q. suber (mPIN). Furthermore, the class HAL was subdivided in low and dense understory vegetation cover. As a result, combination of ALSFW and TLS reached 85.2% of overall accuracy classifying classes HAL, PIN and mPIN. Combining ALSFW and ALSD, the overall accuracy was 77.0% to discriminate among the five classes. Finally, classification of understory vegetation cover using ALSFW reached an overall accuracy of 90.9%. In general, combination of ALSFW and TLS improved the overall accuracy of classifying among HAL, PIN and mPIN by 7.4% compared to the use of the data sets separately, and by 33.3% with respect to the use of ALSD only. ALSFW metrics, in particular those specifically designed for detection of understory vegetation, increased the overall accuracy 9.1% with respect to ALSD metrics. These analyses show that classification in forest ecosystems with presence of understory vegetation and intermediate canopy strata is improved when ALSFW and/or TLS are used instead of ALSD.[ES] La tecnología LiDAR, tanto en sus versiones aerotransportada como terrestre, ha adquirido relevancia en los últimos años en la realización de inventarios forestales que permiten entender y adecuar la gestión de los ecosistemas forestales. En este estudio, se evaluó la clasificación por composición de especies en un bosque mediterráneo mediante el árbol de decisión C4.5. Para ello, se emplearon diferentes conjuntos de datos derivados de LiDAR discreto (ALSD ), LiDAR de retorno de onda completa (full-waveform, ALSFW) y láser escáner terrestre (TLS) como datos de entrada de la clasificación. La composición de especies se dividió en cinco clases: parcelas puras de Quercus ilex (QUI); puras de Pinus halepensis regenerado (HALr); puras de P. halepensis (HAL); puras de P. pinaster (PIN); y mixta de P. pinaster y Q. suber (mPIN). Además, se realizó una subdivisión de la clase HAL en cobertura de sotobosque escasa y densa. Como resultado se obtuvo una fiabilidad del 85,2% en la clasificación de las clases HAL, PIN y mPIN combinando ALSFW y TLS. En la clasificación de las cinco composiciones de especies, la fiabilidad alcanzada empleando ALSFW y ALSD fue del 77,0%. Finalmente, en la clasificación de las subclases de cobertura de sotobosque se logró un 90,9% de fiabilidad con ALSFW. En general, la combinación de ALSFW y TLS mejoró los resultados en un 7,4% en la clasificación de las clases HAL, PIN y mPIN en comparación con el uso de los datos de los sensores por separado, y en un 33,3% con respecto al uso de ALSD. Las métricas ALSFW, en particular aquellas diseñadas especialmente para la detección del sotobosque, mejoraron la precisión en un 9,1% con respecto a las métricas derivadas de ALSD. Estos análisis muestran que el uso del ALSFW y TLS mejora la clasificación de los ecosistemas forestales con presencia de sotobosque y diferentes especies arbóreas en los estratos intermedios con respecto al ALSD.This research has been funded by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R.Torralba, J.; Crespo-Peremarch, P.; Ruiz, LA. (2018). Evaluación del uso de LiDAR discreto, full-waveform y TLS en la clasificación por composición de especies en bosques mediterráneos. Revista de Teledetección. (52):27-40. https://doi.org/10.4995/raet.2018.11106SWORD274052Åkerblom, M., Raumonen, P., Mäkipää, R., Kaasalainen, M. 2017. Automatic tree species recognition with quantitative structure models. 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Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data

[EN] The use of laser scanning acquired from the air, or ground, holds great potential for the assessment of forest structural attributes, beyond conventional forest inventory. The use of full-waveform airborne laser scanning (ALSFW) data allows for the extraction of detailed information in different vertical strata compared to discrete ALS (ALSD). Terrestrial laser scanning (TLS) can register lower vertical strata, such as understory vegetation, without issues of canopy occlusion, however is limited in its acquisition over large areas. In this study we examine the ability of ALSFW to characterize understory vegetation (i.e. maximum and mean height, cover, and volume), verified using TLS point clouds in a Mediterranean forest in Eastern Spain. We developed nine full-waveform metrics to characterize understory vegetation attributes at two different scales (3.75¿m square subplots and circular plots with a radius of 15¿m); with, and without, application of a height filter to the data. Four understory vegetation attributes were estimated at plot level with high R2 values (mean height: R2¿=¿0.957, maximum height: R2¿=¿0.771, cover: R2¿=¿0.871, and volume: R2¿=¿0.951). The proportion of explained variance was slightly lower at 3.75¿m side cells (mean height: R2¿=¿0.633, maximum height: R2¿=¿0.470, cover: R2¿=¿0.581, and volume R2¿=¿0.651). These results indicate that Mediterranean understory vegetation can be estimated and accurately mapped over large areas with ALSFW. The future use of these types of predictions includes the estimation of ladder fuels, which drive key fire behavior in these ecosystems.This research was developed mainly in the Integrated Remote Sensing Studio (IRSS) of University of British Columbia (UBC) (Canada) as a result of the Erasmus + KA-107 mobility grant. The authors thank the financial support provided by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R.Crespo-Peremarch, P.; Tompalski, P.; Coops, N.; Ruiz Fernández, LÁ. (2018). Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data. Remote Sensing of Environment. 217:400-413. https://doi.org/10.1016/j.rse.2018.08.033S40041321

Comparing the generation of DTM in a forest ecosystem using TLS, ALS and UAV-DAP, and different software tools

[EN] Remote sensing and photogrammetry techniques have demonstrated to be an important tool for the characterization of forest ecosystems. Nonetheless, the use of these techniques requires an accurate digital terrain model (DTM) for the height normalization procedure, which is a key step prior to any further analyses. In this manuscript, we assess the extraction of the DTM for different techniques (airborne laser scanning: ALS, terrestrial laser scanning: TLS, and digital aerial photogrammetry in unmanned aerial vehicle: UAV-DAP), processing tools with different algorithms (FUSION/LDV© and LAStools©), algorithm parameters, and plot characteristics (canopy and shrub cover, and terrain slope). To do this, we compare the resulting DTMs with one used as reference and extracted from classic surveying measurements. Our results demonstrate, firstly, that ALS and reference DTMs are similar in the different scenarios, except for steep slopes. Secondly, TLS DTMs are slightly less accurate than those extracted for ALS, since items such as trunks and shrubs cause a great occlusion due to the proximity of the instrument, and some of the points filtered as ground correspond to these items as well, therefore a finer setting of algorithm parameters is required. Lastly, DTMs extracted for UAV-DAP in dense canopy scenarios have a low accuracy, however, accuracy may be enhanced by modifying the processing tool and algorithm parameters. An accurate DTM is essential for further forestry applications, therefore, to know how to take advantage of the available data to obtain the most accurate DTM is also fundamental.The authors are thankful for the financial support provided by the Spanish Ministerio de Economía y Competitividad and FEDER, in the framework of the project CGL2016-80705-R.Crespo-Peremarch, P.; Torralba, J.; Carbonell-Rivera, JP.; Ruiz Fernández, LÁ. (2020). Comparing the generation of DTM in a forest ecosystem using TLS, ALS and UAV-DAP, and different software tools. ISPRS. 575-582. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-575-2020S57558

Estudio comparativo de métodos de regresión para la predicción de variables de estructura y combustibilidad a partir de datos LiDAR full-waveform

Revista oficial de la Asociación Española de Teledetección[EN] Regression methods are widely employed in forestry to predict and map structure and canopy fuel variables. We present a study where several regression models (linear, non-linear, regression trees and ensemble) were assessed. Independent variables were calculated using metrics extracted from full-waveform LiDAR data, while the reference data used to generate the dependent variables for the prediction models were obtained from fieldwork in 78 plots of 16 m radius. Transformations of dependent and independent variables with feature selection were carried out to assess their influence in the prediction of response variables. In order to evaluate significant differences and rank regression models we used the non-parametric tests Wilcoxon and Friedman, and post-hoc analysis or post-hoc pairwise multiple comparison tests, such as Nemenyi, for Friedman test. Regressions using transformation of the dependent variable, like square-root or logarithmic, or the independent variable, increased R2 up to 6% with respect to linear regression using unprocessed response variables. CART (Classification and Regression Tree) method provided poor results, but it may be interesting for categorisation purposes. Square-root transformation of the dependent variable is the method having the best overall results, except for stand volume. However, not always has a significant improvement with respect to other regression methods.[ES] Los métodos de regresión se utilizan ampliamente en el ámbito forestal para la predicción y el cartografiado de las variables de estructura y combustibilidad. En este artículo se evalúan diferentes modelos de regresión (lineal, no lineal, árboles de regresión y ensemble). Como variables independientes se utilizaron métricas extraídas de datos LiDAR full-waveform, mientras que los valores de las variables dependientes se generaron a partir de modelos basados en datos de campo obtenidos para 78 parcelas de 16 m de radio. Se llevaron a cabo transformaciones de las variables dependientes e independientes con selección de atributos para evaluar su influencia en la predicción de la variable respuesta. Con el fin de verificar diferencias significativas y ordenar los modelos de regresión se emplearon los tests no paramétricos de Wilcoxon y Friedman, y el análisis post-hoc o los tests de comparación post-hoc por pares, como el de Nemenyi, para el test de Friedman. Las regresiones basadas en la transformación de la variable dependiente, como raíz cuadrada o logaritmo, o en la transformación de las variables independientes, obtuvieron un incremento de la R2 de hasta un 6% con respecto a la regresión lineal. Mediante el método CART (Classification and Regression Tree) se obtuvieron resultados discretos, si bien su uso puede estar indicado para la categorización o estratificación. Con el método basado en la transformación de la variable dependiente mediante raíz cuadrada se consiguieron los mejores resultados comparativos en la predicción de variables forestales, excepto para el volumen. Sin embargo, su uso no siempre implica una mejora significativa con respecto a los otros métodos de regresión usados en este trabajo.This research has been funded by the Spanish Ministerio de Economía y Competitividad and FEDER, in the framework of the project CGL2013-46387-C2-1-R.Crespo-Peremarch, P.; Ruiz, L.; Balaguer-Beser, A. (2016). A comparative study of regression methods to predict forest structure and canopy fuel variables from LiDAR full-waveform data. Revista de Teledetección. (Special Issue):27-40. https://doi.org/10.4995/raet.2016.4066SWORD2740Special Issu

Classification of UAV-based photogrammetric point clouds of riverine species using machine learning algorithms: a case study in the Palancia river, Spain

[EN] The management of riverine areas is fundamental due to their great environmental importance. The fast changes that occur in these areas due to river mechanics and human pressure makes it necessary to obtain data with high temporal and spatial resolution. This study proposes a workflow to map riverine species using Unmanned Aerial Vehicle (UAV) imagery. Based on RGB point clouds, our work derived simple geometric and spectral metrics to classify an area of the public hydraulic domain of the river Palancia (Spain) in five different classes: Tamarix gallica L. (French tamarisk), Pinus halepensis Miller (Aleppo pine), Arundo donax L. (giant reed), other riverine species and ground. A total of six Machine Learning (ML) methods were evaluated: Decision Trees, Extra Trees, Multilayer Perceptron, K-Nearest Neighbors, Random Forest and Ridge. The method chosen to carry out the classification was Random Forest, which obtained a mean score cross-validation close to 0.8. Subsequently, an object-based reclassification was done to improve this result, obtaining an overall accuracy of 83.6%, and individually a producer¿s accuracy of 73.8% for giant reed, 87.7% for Aleppo pine, 82.8% for French tamarisk, 93.5% for ground and 80.1% for other riverine species. Results were promising, proving the feasibility of using this cost-effective method for periodic monitoring of riverine species. In addition, the proposed workflow is easily transferable to other tasks beyond riverine species classification (e.g., green areas detection, land cover classification) opening new opportunities in the use of UAVs equipped with consumer cameras for environmental applications.Carbonell-Rivera, JP.; Estornell Cremades, J.; Ruiz Fernández, LÁ.; Torralba, J.; Crespo-Peremarch, P. (2020). Classification of UAV-based photogrammetric point clouds of riverine species using machine learning algorithms: a case study in the Palancia river, Spain. ISPRS. 659-666. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-659-2020S65966

Procesado y análisis de datos láser escáner full-waveform aéreo para la caracterización de la estructura y combustibilidad forestal

[EN] This PhD thesis addresses the development of full-waveform airborne laser scanning (ALSFW) processing and analysis methods to characterize the vertical forest structure, in particular the understory vegetation. In this sense, the influence of several factors such as pulse density, voxel parameters (voxel size and assignation value), scan angle at acquisition, radiometric correction and regression methods is analyzed on the extraction of ALSFW metric values and on the estimate of forest attributes. Additionally, a new software tool to process ALSFW data is presented, which includes new metrics related to understory vegetation. On the other hand, occlusion caused by vegetation in the ALSFW, discrete airborne laser scanning (ALSD) and terrestrial laser scanning (TLS) signal is characterized along the vertical structure. Finally, understory vegetation density is detected and determined by ALSFW data, as well as characterized by using the new proposed metrics.[ES] Esta tesis doctoral aborda el desarrollo de métodos de procesado y análisis de datos láser escáner full-waveform aéreo (ALSFW) para la caracterización de la estructura vertical del bosque y, en particular, del sotobosque. En este sentido, se analiza la influencia de diferentes factores como la densidad de pulso, parámetros de voxelización (tamaño de vóxel y tipo de asignación), ángulo de escaneo en la adquisición, corrección radiométrica y métodos de regresión en la extracción de los valores de métricas ALSFW y en la estimación de atributos forestales. Asimismo, se presenta una nueva herramienta de procesado de datos ALSFW, la cual incluye nuevas métricas relacionadas con el sotobosque. Por otro lado, se caracteriza la oclusión provocada por la vegetación en la señal ALSFW, láser escáner discreto aéreo (ALSD) y láser escáner terrestre (TLS) en toda la estructura vertical. Por último, se detecta y determina la densidad de sotobosque mediante datos ALSFW, así como su caracterización empleando las nuevas métricas propuestas.The author of this PhD thesis is thankful for the financial support provided by the Spanish Ministerio de Economía y Competitividad and FEDER, in the framework of the projects ForeStructure CGL2013-46387-C2-1-R (2013-2016) and FIRMACARTO CGL2016-80705-R (2016-2019). In addition, this PhD thesis was partly developed in the Integrated Remote Sensing Studio (IRSS) of University of British Columbia (UBC) (Canada) and in the Centre d’Applications et de Recherche en Télédétection of Université de Sherbrooke (Canada) thanks to the Erasmus+ KA-107 mobility grant and to the Canadian research project Assessment of Wood Attributes using Remote Sensing (AWARE) (NSERC CRDPJ-462973-14, grantee N.C. Coops, UBC), respectivelyCrespo-Peremarch, P.; Ruiz, LA. (2020). Processing and analysis of airborne full-waveform laser scanning data for the characterization of forest structure and fuel properties. Revista de Teledetección. 0(57):95-99. https://doi.org/10.4995/raet.2020.14551OJS959905

ANALYSIS OF THE SIDE-LAP EFFECT ON FULL-WAVEFORM LIDAR DATA ACQUISITION FOR THE ESTIMATION OF FOREST STRUCTURE VARIABLES

LiDAR full-waveform provides a better description of the physical and forest vertical structure properties than discrete LiDAR since it registers the full wave that interacts with the canopy. In this paper, the effect of flight line side-lap is analysed on forest structure and canopy fuel variables estimations. Differences are related to pulse density changes between flight stripe side-lap areas, varying the point density between 2.65 m−2 and 33.77 m−2 in our study area. These differences modify metrics extracted from data and therefore variable values estimated from these metrics such as forest stand variables. In order to assess this effect, 64 pairwise samples were selected in adjacent areas with similar canopy structure, but having different point densities. Two parameters were tested and evaluated to minimise this effect: voxel size and voxel value assignation testing maximum, mean, median, mode, percentiles 90 and 95. Student’s t-test or Wilcoxon test were used for the comparison of paired samples. Moreover, the absolute value of standardised paired samples was calculated to quantify dissimilarities. It was concluded that optimizing voxel size and voxel value assignation minimised the effect of point density variations and homogenised full-waveform metrics. Height/median ratio (HTMR) and Vertical distribution ratio (VDR) had the lowest variability between different densities, and Return waveform energy (RWE) reached the best improvement with respect to initial data, being the difference between standardised paired samples 1.28 before and 0.69 after modification

Automated classification of crop types and condition in a mediterranean area using a fine-tuned convolutional neural network

[EN] Crop classification based on satellite and aerial imagery is a recurrent application in remote sensing. It has been used as input for creating and updating agricultural inventories, yield prediction and land management. In the context of the Common Agricultural Policy (CAP), farmers get subsidies based on the crop area cultivated. The correspondence between the declared and the actual crop needs to be monitored every year, and the parcels must be properly maintained, without signs of abandonment. In this work, Sentinel2 time series images and 4-band Very High Resolution (VHR) aerial orthoimages from the Spanish National Programme of Aerial Orthophotography (PNOA) were combined in a pre-trained Convolutional Neural Network (CNN) (VGG-19) adapted with a double goal: (i) the classification of agricultural parcels in different crop types; and (ii) the identification of crop condition (i.e., abandoned vs. non-abandoned) of permanent crops in a Mediterranean area of Spain. A total of 1237 crop parcels from the CAP declarations of 2019 were used as ground truth to classify into cereals, fruit trees, olive trees, vineyards, grasslands and arable land, from which 80% were used for training and 20% for testing. The overall accuracy obtained was greater than 93% both, at parcel and area levels. Olive trees were the least accurate crop, mostly misclassified with fruit trees, and young vineyards were slightly confused with cereal and arable land. In the assessment of crop condition, only 9.65% of the abandoned plots were missed (omission errors), and 7.21% of plots were over-detected (commission errors), having a 99% of overall accuracy from a total of 1931 image subset samples. The proposed methodology based on CNN is promising for its operational application in crop monitoring and in the detection of abandonments in the context of CAP subsidies, but a more exhaustive number of training samples is needed for extension to other crop types and geographical areas.This research has been funded by the Conselleria d'Agricultura, Medi Ambient, Canvi Climàtic i Desenvolupament Rural, Generalitat Valenciana, throught the nominative line S847000. The authors also thank the Institut Cartogràfic Valencià for providing very high resolution aerial imagery data of the study area.Ruiz Fernández, LÁ.; Almonacid-Caballer, J.; Crespo-Peremarch, P.; Recio Recio, JA.; Pardo Pascual, JE.; Sánchez-García, E. (2020). Automated classification of crop types and condition in a mediterranean area using a fine-tuned convolutional neural network. ISPRS. 1061-1068. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1061-2020S1061106