To What Extent Can UAV Photogrammetry Replicate UAV LiDAR to Determine Forest Structure? A Test in Two Contrasting Tropical Forests

Tropical forests are complex multi-layered systems, with the height and three-dimensional (3D) structure of trees influencing the carbon and biodiversity they contain. Fine-resolution 3D data on forest structure can be collected reliably with Light Detection and Ranging (LiDAR) sensors mounted on aircraft or Unoccupied Aerial Vehicles (UAVs), however, they remain expensive to collect and process. Structure-from-Motion (SfM) Digital Aerial Photogrammetry (SfM-DAP), which relies on photographs taken of the same area from multiple angles, is a lower-cost alternative to LiDAR for generating 3D data on forest structure. Here, we evaluate how SfM-DAP compares to LiDAR data acquired concurrently using a fixed-wing UAV, over two contrasting tropical forests in Gabon and Peru. We show that SfM-DAP data cannot be used in isolation to measure key aspects of forest structure, including canopy height (%Bias: 40%–50%), fractional cover, and gap fraction, due to difficulties measuring ground elevation, even under low tree cover. However, we find even in complex forests, SfM-DAP is an effective means of measuring top-of-canopy structure, including surface heterogeneity, and is capable of producing similar measurements of vertical structure as LiDAR. Thus, in areas where ground height is known, SfM-DAP is an effective method for measuring important aspects of forest structure, including canopy height, and gaps, however, without ground data, SfM-DAP is of more limited utility. Our results support the growing evidence base pointing to photogrammetry as a viable complement, or alternative, to LiDAR, capable of providing much needed information to support the mapping and monitoring of biomass and biodiversity

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations

Accurate measurement of tropical forest canopy heights and aboveground carbon using structure from motion

© 2019 by the authors. Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies ( > 10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location (R 2 = 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced R 2 . The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR.The study was financed by faculty of Technology and Environment, Prince of Songkla Universit

Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion

Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies (&gt;10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location ( R 2 = 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced R 2 . The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR.</jats:p

Accurate measurement of tropical forest canopy heights and aboveground carbon using structure from motion

© 2019 by the authors. Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies ( > 10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location (R 2 = 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced R 2 . The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR.The study was financed by faculty of Technology and Environment, Prince of Songkla Universit

Fenotipaje convencional y comportamiento fenológico de 25 accesiones de quinua (Chenopodium quinoa willd) del programa de investigación en quinua del Centro de Investigación en Cultivos Andinos, en el Centro Agronómico K’ayra

El presente trabajo de investigación intitulado “Fenotipaje convencional y comportamiento fenológico de 25 accesiones de quinua (Chenopodium quinoa willd) del Programa de Investigación en Quinua del Centro de Investigación en Cultivos Andinos, en el Centro Agronómico K’ayra”, fue realizado entre los meses de setiembre del 2018 a marzo del 2019. El objetivo general planteado fue evaluar el fenotipaje convencional y el comportamiento fenológico de 25 accesiones de Quinua (Chenopodium quinoa willd), del Programa de Investigación en quinua, del CICA – FCA – UNSAAC. El material evaluado pertenece al Banco de Germoplasma del Programa de Investigación en quinua del Centro de Investigación en Cultivos Andinos (CICA), de la Facultad de Ciencias Agrarias. La accesión CQC-296 presenta la altura de planta mayor. Todas las accesiones muestran diámetro de tallo, longitud de peciolo y longitud de hoja similares. La accesión CQC–062, presenta ancho de hoja mayor. Todas las accesiones presentan longitud y diámetro de panoja similares. La accesión CQC-051 presenta el diámetro de grano mayor. Todas las accesiones presentan igual espesor de grano y peso de grano por hectárea. Las accesiones CQC-199 y CQC-062 presentan peso de grano por planta superior. La accesión CQC-199 presenta el nivel de espuma mayor. El tipo de crecimiento de las accesiones fueron herbáceos, forma del tallo anguloso, amarillo y axilas sin pigmentación, estrías de color variable, ramificación variable, hoja romboidal, margen dentada, peciolo y lámina foliar verde, gránulos blancos. Panoja a la floración verde y verde/púrpura, a la madurez anaranjado, forma de panoja variable, densidad de panoja intermedia. Perigonio amarillo grisáceo, pericarpio blanco, episperma blanca, perisperma opaco y fruto lenticular. Las accesiones CQC-260 y CQC-167 presentaron el periodo vegetativo más corto con 150 y 168 días respectivamente, mientras que, las accesiones CQC-045 y CQC-003 presentaron el periodo vegetativo más largo con 228 y 215 días respectivamente

Using Point Clouds for Single Tree Forest Inventory at the Beginning and End of the Rotation

The portability and reduced price of unmanned aerial systems (UAS) in recent years has led to a broad range of new UAS-enabled scientific inquiries, including for forestry. Small, consumer-grade UAS are advantageous for forest measurements due to their portability, ease and safety of deployment, and notably, they are currently the only remote sensing technology capable of measuring both individual seedlings and individual mature trees from above. Light detection and ranging (lidar) sensors have been increasingly used in forestry research over the past few decades as well, and now models exist which can be integrated with UAS for tree mensuration. Computer vision software known as structure from motion (SfM) can be used to produce analogous data to those produced by lidar, known as point clouds, from still images taken from UAS. The goal of this dissertation is to examine how to use point cloud data to augment tree level estimates for forest inventory. To cover a broad range of dimensional values defining a forest, two different stages in the life cycle of the stand were investigated, the seedling stage, and the mature stage which precedes harvest. For analyzing seedlings, the first manuscript (second chapter) in this dissertation used UAS and multispectral sensors to produce point clouds of southwestern white pine seedlings in common garden boxes. Here, a methodology is presented for estimating seedling sizes from SfM reconstructions and using them to improve the predictive power of seedling size models along with ground measurements from the previous year. Also, I make recommendations for how common garden designs can be designed so as to lengthen the duration of useful UAS surveys. Finally, I present a seedling size variable that performs well both as a predictor and as a response, the product of seedling height and diameter at root collar, or longitudinal area. To address the mature stage of the trees, the second manuscript (third chapter) compared the performance of three platforms that vary greatly in cost, ease of operation, and data processing requirements. One of the platforms was identical to the UAS used in the first manuscript, one was a lidar carried by a larger UAS (UALS), and one was a ground based mobile lidar scanner (MLS). The UAS produced SfM height estimates that were comparable to those by the UALS, though they tended to be underestimates due to smoothing of the SfM reconstruction. Both the UALS and MLS platforms produced sufficient stem returns to locate a majority of the tree stems in the scene, while none could be located from the UAS. Using data from the MLS and the UALS, I showed that using the stem near the base of the crown or the treetop to estimate lean will produce different lean estimates and contend that the MLS is the best platform for estimating the lean of the stems. In the third and final manuscript (fourth chapter), I compared two methods for estimating stem lean from the MLS data. The more conservative lean estimate, which involves using the horizontal distance between the top and bottom of the merchantable portion of the stem, was included as a predictor to improve the fit of existing nonlinear stem taper and volume equations. The results suggest that trees that lean as little as 2° should be modeled differently than those which are vertical. Also, substituting other diameters higher on the stems for DBH impacts the fit of the models for leaning trees differently than for vertical ones, such that leaning trees seem to have a narrower range of optimal diameter heights. As a whole, my dissertation supports the usage of UAS and MLS to improve the quality and efficiency of remotely measuring single seedlings or mature trees forest inventory, while also identifying major limitations of the technology and recommending strategies to contend with them

Fenotipaje de 25 accesiones de quinua (Chenopodium quinoa Willdenow) del banco de germoplasma de la UNSAAC, utilizando índices de vegetación adquiridos desde sensores remotos

El cultivo de quinua es uno de los cultivos más importantes en la región andina, siendo fuente de alimentación y nutrición por sus características de calidad de grano. Por otro lado, sensores ópticos proximales instalados en plataformas aéreas no tripuladas permiten adquirir imágenes aéreas multiespectrales de los cultivos, permitiendo recuperar características de los cultivos con un alta escala espacial y temporal. Por ello, en este proyecto se ha realizado la evaluación de 25 accesiones de quinua del Banco de Germoplasma del Programa de Investigación en Quinua del CICA-FCA-UNSAAC; con la finalidad de evaluar 24 índices de vegetación, calculados a partir de las cinco bandas adquiridas con una cámara multiespectral RedEdge Mx (MicaSense, Seatle, USA) instalada en un Dron. Las parcelas en campo fueron evaluadas en 5 fechas distintas (62, 86, 93, 121, 128 días después de la siembra), a lo largo del periodo vegetativo del cultivo, en el Centro Agronómico K’ayra de la UNSAAC. Como resultado de los análisis de las imágenes multiespectrales y la determinación de los índices de vegetación, se tiene que, 10 de ellos presentan diferencias significativas entre las accesiones evaluadas, asimismo, fue posible encontrar siete grupos después de realizada la prueba de comparación de medias de Tukey. También fue posible determinar la cobertura vegetal en las parcelas de quinua, para las accesiones, evaluadas, sin embargo, no se encontró diferencias significativas entre dichas accesiones, para este carácter. Finalmente, la mejor fecha para la estimación del rendimiento de grano en el cultivo de quinua, se encuentra cercana a los 86 díaUNSAAC - CONCYTE