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Beta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest

ALEJANDRA DEL PILAR OCHOA FRANCO JOSE RENE VALDEZ LAZALDE GREGORIO ANGELES PEREZ HECTOR MANUEL DE LOS SANTOS POSADAS JOSE LUIS HERNANDEZ STEFANONI JUAN IGNACIO VALDEZ HERNANDEZ PAULINO PEREZ RODRIGUEZ (2019)

Tree beta-diversity denotes the variation in species composition at stand level, it is a key indicator of forest degradation, and is conjointly required with alpha-diversity for management decision making but has seldom been considered. Our aim was to map it in a continuous way with remote sensing technologies over a tropical landscape with different disturbance histories. We extracted a floristic gradient of dissimilarity through a non-metric multidimensional scaling ordination based on the ecological importance value of each species, which showed sensitivity to different land use history through significant differences in the gradient scores between the disturbances. After finding strong correlations between the floristic gradient and the rapidEye multispectral textures and LiDAR-derived variables, it was linearly regressed against them; variable selection was performed by fitting mixed-effect models. The redEdge band mean, the Canopy Height Model, and the infrared band variance explained 68% of its spatial variability, each coefficient with a relative importance of 49%, 32.5%, and 18.5% respectively. Our results confirmed the synergic use of LiDAR and multispectral sensors to map tree beta-diversity at stand level. This approach can be used, combined with ground data, to detect effects (either negative or positive) of management practices or natural disturbances on tree species composition. 

Article

FLORISTIC GRADIENT SPECIES COMPOSITION DISSIMILARITY NMDS RAPIDEYE REMOTE SENSING LIDAR LINEAR MODEL MIXED MODEL BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) DESARROLLO VEGETAL DESARROLLO VEGETAL

Improving species diversity and biomass estimates of tropical dry forests using airborne LiDAR

JOSE LUIS HERNANDEZ STEFANONI JUAN MANUEL DUPUY RADA Richard Birdsey FERNANDO JESUS TUN DZUL Alicia Peduzzi JUAN PABLO CAAMAL SOSA (2014)

The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.

Article

ABOVE-GROUND BIOMASS BIODIVERSITY HABITAT HETEROGENEITY LIDAR VEGETATION STRUCTURE BIOLOGÍA Y QUÍMICA

Estimating species richness and biomass of tropical dry forests using LIDAR during leaf-on and leaf-off canopy conditions

JOSE LUIS HERNANDEZ STEFANONI KRISTOFER D. JOHNSON BRUCE D. COOK JUAN MANUEL DUPUY RADA Richard Birdsey Alicia Peduzzi FERNANDO JESUS TUN DZUL (2015)

Questions: Is the accuracy of predictions of above-ground biomass (AGB) and plant species richness of tropical dry forests from LIDAR data compromised during leaf-off canopy period, when most of the vegetation is leafless, compared to the leaf-on period? How does topographic position affect prediction accuracy of AGB for leaf-off and leaf-on canopy conditions? Location: Tropical dry forest, Yucatan Peninsula, Mexico. Methods: We evaluated the accuracy of predictions using both leaf-on and leaf-off LIDAR estimates of biomass and species richness, and assessed the adequacy of both LIDAR data sets for characterizing these vegetation attributes in tropical dry forests using multiple regression analysis and ANOVA. The performance of the models was assessed by leave-one-out cross-validation. We also investigated differences in vegetation structure between two topographic conditions using PCA and ANOSIM. Finally, we evaluated the influence of topography on the accuracy of biomass estimates from LIDAR using multiple regression analysis and ANOVA. Results: A higher overall accuracy was obtained with leaf-on vs leaf-off conditions for AGB (root mean square error (RMSE) = 21.6 vs 25.7 ton·ha-1), as well as for species richness (RMSE = 5.5 vs 5.8 species, respectively). However, no significant differences in mean dissimilarities between biomass estimates from LIDAR and in situ biomass estimates comparing the two canopy conditions were found (F1,39 = 0.03, P = 0.87). In addition, no significant differences in dissimilarities of AGB estimation were found between flat and hilly areas (F1,39 = 1.36, P = 0.25). Conclusions: Our results suggest that estimates of species richness and AGB from LIDAR are not significantly influenced by canopy conditions or slope, indicating that both leaf-on and leaf-off models are appropriate for these variables regardless of topographic position in these tropical dry forests. We evaluated the accuracy of predictions using both leaf-on and leaf-off LiDAR estimates of biomass and species richness in tropical dry forest. Estimations of biomass and species richness from LiDAR data were not influenced by canopy conditions, indicating that LiDAR estimates of these variables can be obtained during the dry season. Moreover, biomassestimates were unaffected by topography.

Article

LIDAR ABOVE-GROUND BIOMASS CANOPY CONDITIONS FOREST STRUCTURE, SPECIES RICHNESS TOPOGRAPHY TROPICAL DRY FOREST BIOLOGÍA Y QUÍMICA BIOLOGÍA Y QUÍMICA

Análisis del deslizamiento en la carretera Tijuana-Ensenada (km 93 +50) a partir modelos de relieve de alta resolución espacial

Analysis of landslide of Tijuana-Ensenada Highway (km 93 +50) from relief models of high spatial resolution

PATRICIA ANDRADE GARCIA (2016)

El aumento de actividad antropogénica en la zona costera de Baja California ha incrementado la susceptibilidad a los deslizamientos de laderas. La combinación particular de historia geológica, tipo de rocas, condiciones tectónicas y del relieve, así como la persistente acción del oleaje en la costa de la Bahía Salsipuedes, la expone como una zona especialmente propensa a eventos de remoción de masa. Este trabajo se enfoca en el análisis del deslizamiento rotacional en el km 93 +50 ocurrido el 28 de diciembre de 2013 en la Carretera escénica Tijuana-Ensenada, se discuten los posibles factores que desencadenan el deslizamiento y la dinámica de los bloque deslizantes. Se analiza una serie de tiempo de mediciones de la topografía con técnicas emergentes como levantamientos LiDAR terrestre, aéreo y fotogrametría por drones. Se calculó una serie de modelos digitales de alta resolución (MDE) para estimar los cambios en elevación y volumen desde una situación pre-deslizamiento hasta la completa reconstrucción en la carretera, se analiza la evolución en la transformación del relieve. La primera reconstrucción 3D postdeslizamiento se hizo a partir de fotos aéreas tomadas por dron unas horas después del evento. Se estimó un volumen desplazado cercano a los 390 mil m3 durante el deslizamiento. En la transformación del relieve se analizó un levantamiento laser terrestre (TLS), un modelo de elevación derivado de estéreo par satelital GeoEye, un levantamiento aéreo LiDAR y un último de fotografía con drone. Los vectores de movimiento relativo de los bloques deslizantes se determinaron a partir de actitud de lineaciones con respecto al rumbo y echado de los planos de deslizamiento. Se extrajeron los vectores de las estrías en la pared de piso registrados fielmente en la nube de puntos del escaneo con láser terrestre. La reparación de la carretera afectada por el deslizamiento tardó más de un año en restablecer el tráfico. Existen tramos cercanos al deslizamiento de 2013 propensos a correr la misma suerte. Los resultados de este trabajo serán de utilidad para comprender mejor las características de esta zona y de los factores que desencadenan los deslizamientos. La metodología usada puede servir como modelo en eventos futuros.

The increase of anthropogenic activity in the Baja California coastal zone has augmented the susceptibility to landslides. The combination of geological and tectonic history, rock types, steep slopes and the persistent wave erosion in Salsipuedes Bay, makes this zone prone to landslides along the coastal bluffs. This work focuses on the rotational landslide that occurred on December 28, 2013 at km 93 +50 of TijuanaEnsenada Scenic Road. We analyze the possible factors that triggered the failure of the land mass. A series of high resolution digital terrain models (DTM) were used to estimate the elevation and volume change between pre- and post-landslide up to the complete reconstruction of highway, the terrain transformation is presented. The first post-event 3D reconstruction was from a series of aerial photos taken by a drone few hours after the landslide and applying the emergent Structure from Motion (SfM) technology. A displaced volume close to 390,000 m3 was calculated for the landslide. To continue with the landscape transformation, we used a satellite-derived DTM from a GeoEye stereo pair, a Terrestrial Laser Scan (TLS), a LiDAR aerial survey and a final DTM derived from drone photogrammetry after the highway was completely repaired. Relative motion of the sliding blocks was determined from lineation’s attitude. Motion vectors were extracted from striations carved on the footwall by the sliding hanging wall and accurately recorded in the TLS point cloud. It took almost a year to restore traffic after the landslide. There are highway stretches close to the 2013 landslide that are prone to the same fate. The results of this work will be useful to better understand the characteristics of this area and the factors that trigger landslides. The methodology used can serve as a model for future events.

Master thesis

Deslizamiento LiDAR SfM CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO

Estimating species richness and biomass of tropical dry forests using LIDAR during leaf-on and leaf-off canopy conditions

JOSE LUIS HERNANDEZ STEFANONI KRISTOFER D. JOHNSON BRUCE D. COOK JUAN MANUEL DUPUY RADA Richard Birdsey Alicia Peduzzi FERNANDO JESUS TUN DZUL (2015)

Questions: Is the accuracy of predictions of above-ground biomass (AGB) and plant species richness of tropical dry forests from LIDAR data compromised during leaf-off canopy period, when most of the vegetation is leafless, compared to the leaf-on period? How does topographic position affect prediction accuracy of AGB for leaf-off and leaf-on canopy conditions? Location: Tropical dry forest, Yucatan Peninsula, Mexico. Methods: We evaluated the accuracy of predictions using both leaf-on and leaf-off LIDAR estimates of biomass and species richness, and assessed the adequacy of both LIDAR data sets for characterizing these vegetation attributes in tropical dry forests using multiple regression analysis and ANOVA. The performance of the models was assessed by leave-one-out cross-validation. We also investigated differences in vegetation structure between two topographic conditions using PCA and ANOSIM. Finally, we evaluated the influence of topography on the accuracy of biomass estimates from LIDAR using multiple regression analysis and ANOVA. Results: A higher overall accuracy was obtained with leaf-on vs leaf-off conditions for AGB (root mean square error (RMSE) = 21.6 vs 25.7 ton·ha-1), as well as for species richness (RMSE = 5.5 vs 5.8 species, respectively). However, no significant differences in mean dissimilarities between biomass estimates from LIDAR and in situ biomass estimates comparing the two canopy conditions were found (F1,39 = 0.03, P = 0.87). In addition, no significant differences in dissimilarities of AGB estimation were found between flat and hilly areas (F1,39 = 1.36, P = 0.25). Conclusions: Our results suggest that estimates of species richness and AGB from LIDAR are not significantly influenced by canopy conditions or slope, indicating that both leaf-on and leaf-off models are appropriate for these variables regardless of topographic position in these tropical dry forests. We evaluated the accuracy of predictions using both leaf-on and leaf-off LiDAR estimates of biomass and species richness in tropical dry forest. Estimations of biomass and species richness from LiDAR data were not influenced by canopy conditions, indicating that LiDAR estimates of these variables can be obtained during the dry season. Moreover, biomassestimates were unaffected by topography.

Article

LIDAR ABOVE-GROUND BIOMASS CANOPY CONDITIONS FOREST STRUCTURE, SPECIES RICHNESS TOPOGRAPHY TROPICAL DRY FOREST BIOLOGÍA Y QUÍMICA

Estimación regional de biomasa aérea en la península de Yucatán mediante tecnología geoespacial.

ALMA DELIA ORTIZ REYES (2019)

Tesis (Doctorado en Ciencias Forestales).- Colegio de Postgraduados, 2019.

Caracterizar la distribución espacial e incertidumbre de la biomasa aérea en bosques tropicales sobre áreas extensas es factible mediante el uso combinado de datos de campo, LiDAR (Light Detection And Ranging) y datos espectrales. Se colectaron datos en tres niveles y se empleó una estrategia de modelado que permitió la estimación de biomasa aérea y su incertidumbre asociada a nivel regional, en dos tipos de selva mediana subperennifolia (SMSP) y subcaducifolia (SMSC) en la Península de Yucatán, México. En una primera fase, se relacionaron los datos de campo y LiDAR en franjas mediante regresión lineal múltiple y Random Forest (RF). El modelo de regresión transformado explicó en mayor proporción la varianza para ambos tipos de vegetación, mientras que RF arrojó los valores menores de RMSE. En la segunda fase, una cierta combinación de variables espectrales y climáticas explicaron la varianza en 50% mediante RF (RMSE = 34.2 Mg ha-1 SMSP; 26.2 Mg ha-1 SMSC). Finalmente se evaluó la incertidumbre, a nivel de pixel, mediante el algoritmo Quantile Regression Forests (QRF). Se reportan intervalos de incertidumbre de 0 a 250 Mg ha-1 para SMSP y de 0 a 140 Mg ha-1 para SMSC. Los resultados obtenidos apoyan la idea de utilizar datos LiDAR como una herramienta de muestreo, así como, variables auxiliares de diversas fuentes para estimar parámetros forestales de interés en áreas extensas. Contar con mapas de distribución espacial e incertidumbre de biomasa aérea a este nivel, ayudará a evaluar y proponer estrategias frente al desafío que supone el cambio climático global, sobre todo en bosques tropicales distribuidos en áreas geográficas extensas y dada su naturaleza compleja y dinámica. _______________ ESTIMATION OF ABOVE-GROUND BIOMASS AT REGIONAL LEVEL IN THE YUCATAN PENINSULA USING GEOSPATIAL TECHNOLOGY. ABSTRACT: Characterizing spatial distribution and uncertainty of above-ground biomass in tropical forests over large areas is feasible through combined use of field data, LiDAR (Light Detection And Ranging) and spectral data. Data were collected at three levels and a modeling strategy was used, which allowed the estimation of above-ground biomass and its associated uncertainty at regional level, in two types of tropical forest: semi-evergreen (SETF) and semi-deciduous tropical forest (SDTF) in the Yucatan Peninsula, Mexico. First, field data and LiDAR data in strips were related by multiple linear regression and Random Forest (RF). Second, a particular combination of spectral and climatic variables explained the variance in 50% by RF (RMSE = 34.2 Mg ha-1 SETF; 26.2 Mg ha-1 SDTF). Finally, uncertainty was evaluated, at pixel level, using Quantile Regression Forests (QRF) algorithm. Uncertainty intervals of 0 to 250 Mg ha-1 for SETF and 0 to 140 Mg ha-1 for SMSC were reported. The results support the idea of using LiDAR data as a sampling tool, as well as, auxiliary variables from various sources to estimate forest parameters of interest in large areas. Having maps of spatial distribution and uncertainty of above-ground biomass at this level will help to evaluate and propose strategies to face the challenge by global climate change, especially in tropical forests distributed in large geographical areas and given their complex and dynamic nature.

Doctoral thesis

Bosque tropical LiDAR Incertidumbre Random Forest Landsat Tropical forest Uncertainty Ciencias Forestales Doctorado CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CIENCIAS AGRARIAS CIENCIA FORESTAL OTRAS

Respuesta de la vegetación nativa al trazo de la Falla Agua Blanca, Ensenada, Baja California.

Response of native vegetation to Agua Blanca Fault trace, Ensenada, Baja California.

MARIANA ELIZABETH ESPINOSA BLAS (2017)

La distribución de la vegetación es modulada por diversos factores ambientales como la disponibilidad de agua, clima, orientación de laderas, tipo de suelo, elevación, relieve entre otros. En el caso de la disponibilidad de agua, discontinuidades como rocas fracturadas y fallas pueden actuar como barreras, conductos o una combinación de ambas en el desplazamiento del agua, donde la vegetación la aprovecha para su desarrollo. Este trabajo evaluó si la Falla Agua Blanca (FAB) tiene algún efecto en la distribución espacial de la vegetación nativa; para ello se prospectó un corredor a lo largo de la falla por vegetación nativa anómala (más verde y/o más alta que la de sus alrededores), asociándola con el agua atrapada a lo largo de la zona de falla. Para la anomalía de altura se utilizó una densa nube de puntos de la cubierta terrestre a lo largo de la falla generada en un levantamiento aéreo LiDAR y por reconstrucción fotogramétrica por dron. De la nube de puntos se calculó la estructura vertical de la vegetación normalizando su altura con respecto al nivel del suelo, construyendo así modelos de altura de dosel. Para el verdor de la vegetación, se utilizó el índice de vegetación de diferencia normalizada (NDVI) a partir de una imagen Landsat 8 contemporánea al levantamiento LiDAR. Se identificaron zonas de acumulación de flujo superficial de agua, donde la vegetación se favorece por la disponibilidad de agua a lo largo de cañadas y corredores riparios. Además, se analizó la anomalía en la vegetación con respecto a la orientación de laderas Norte y Sur. Los resultados revelan la existencia de parches de vegetación anómala a lo largo de la FAB e indican que la vegetación anómala ocurre en cañadas y laderas Norte. Se hizo un comparativo entre las nubes de puntos generadas por levantamiento aéreo LiDAR y fotogrametría por dron, donde la nube LiDAR tiene ventajas por los múltiples rebotes a pesar de la menor densidad de puntos, sin embargo, la nube por fotogrametría no es despreciable como una alternativa de bajo costo.

The spatial distribution vegetation is modilated by various environmental factors such as water availability, climate, slope orientation, soil type, elevation, relief, among others. In the case of water availability, discontinuities such as fractured rocks and faults can act as barriers, conduits or a combination of both for a water passage, where vegetation takes advantage of the availability for its development. The objective of this research is to evaluate if the Agua Blanca Fault (ABF) has any effect on the spatial distribution of native vegetation. A corridor along the fault was prospected for anomalous native vegetation, anomalous due to its height and/or greenness, associating the vegetation anomaly to the water trapped in fractures along the fault zone. Anomalous vegetation is the one that is greener and/or higher than that of its surroundings. For the height anomaly we used a dense point cloud of the ground cover in a corridor along the fault generated from an aerial LiDAR survey and by photogrammetric reconstruction by drone. From the point cloud, we calculated the vertical structure of the vegetation, normalizing its height with respect to the ground level, thus constructing Canopy Height Models. For the vegetation greenness, we used the Normalized Difference Vegetation Index (NDVI) from a Landsat 8 image representative to the end of the dry season and contemporary to the LiDAR survey. Areas of flow accumulation were identified, where the vegetation is favored by the greater availability of water along canyons and riparian corridors. The results reveal patches of anomalous vegetation along the ABF, also indicate that anomalous vegetation occurs along the thalweg of canyons and preferably on north facing slopes. We did a comparison between LiDAR and drone photogrammetry derived point clouds, where the LiDAR cloud has advantages due to the multiple rebounds of a laser pulse despite the lower point density; however the cloud by photogrammetry is not negligible as a low cost alternative.

Master thesis

Percepción remota, vegetación, LiDAR. CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO OTRAS ESPECIALIDADES DE LA TIERRA, ESPACIO O ENTORNO