The use of dual-wavelength airborne laser scanning for estimating tree species composition and species-specific stem volumes in a boreal forest

The estimation of species composition and species-specific stem volumes are critical components of many forest inventories. The use of airborne laser scanning with multiple spectral channels may prove instrumental for the cost-efficient retrieval of these forest variables. In this study, we scanned a boreal forest using two channels: 532 nm (green) and 1064 nm (near infrared). The data was used in a two-step methodology to (1) classify species, and (2) estimate species-specific stem volume at the level of individual tree crowns. The classification of pines, spruces and broadleaves involved linear discriminant analysis (LDA) and resulted in an overall accuracy of 91.1 % at the level of individual trees. For the estimation of stem volume, we employed species-specific k-nearest neighbors models and evaluated the performance at the plot level for 256 field plots located in central Sweden. This resulted in root-mean-square errors (RMSE) of 36 m3/ha (16 %) for total volume, 40 m3/ha (27 %) for pine volume, 32 m3/ha (48 %) for spruce volume, and 13 m3/ha (87 %) for broadleaf volume. We also simulated the use of a monospectral near infrared (NIR) scanner by excluding features based on the green channel. This resulted in lower overall accuracy for the species classification (86.8 %) and an RMSE of 41 m3/ha (18 %) for the estimation of total stem volume. The largest difference when only the NIR channel was used was the difficulty to accurately identify broadleaves and estimate broadleaf stem volume. When excluding the green channel, RMSE for broadleaved volume increased from 13 to 26 m3/ha. The study thus demonstrates the added benefit of the green channel for the estimation of both species composition and species-specific stem volumes. In addition, we investigated how tree height influences the results where shorter trees were found to be more difficult to classify correctly

GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep

Traditional knowledge about the behavior of grazing livestock is about to disappear. Shepherds well know that sheep behavior follows non-random patterns. As a novel alternative to seeking behavioral patterns, this study quantified the grazing activities of two sheep flocks of Churra breed (both in the same area but separated by 10 years) based on Global Position System (GPS) monitoring and remote monitoring sensing techniques. In the first monitoring period (2009-10), geolocations were recorded every 5 min (4, 240 records), while in the second one (2018-20), records were taken every 30 min (7, 636 records). The data were clustered based on the day/night and the activity (resting, moving, or grazing). An airborne LiDAR dataset was used to study the slope, aspect, and vegetation height. Four visible-infrared orthophotographs were mosaicked and classified to obtain the land use/land cover (LU/LC) map. Then, GPS locations were overlain on the terrain features, and a Chi-square test evaluated the relationships between locations and terrain features. Three spatial statistics (directional distribution, Kernel density, and Hot Spot analysis) were also calculated. Results in both monitoring periods suggested that the spatial distribution of free-grazing ewes was non-random. The flocks showed strong preferences for grazing areas with gentle north-facing slopes, where the herbaceous layer formed by pasture predominates. The geostatistical analyses of the sheep locations corroborated those preferences. Geotechnologies have emerged as a potent tool to demonstrate the influence of environmental and terrain attributes on the non-random spatial behavior of grazing sheep. © 2022 Malque Publishing. All rights reserved

Mapping Riparian Habitats of Natura 2000 Network (91E0*, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data

Riparian habitats provide a series of ecological services vital for the balance of the environment, and are niches and resources for a wide variety of species. Monitoring riparian environments at the intra-habitat level is crucial for assessing and preserving their conservation status, although it is challenging due to their landscape complexity. Unmanned aerial vehicles (UAV) and multi-spectral optical sensors can be used for very high resolution (VHR) monitoring in terms of spectral, spatial, and temporal resolutions. In this contribution, the vegetation species of the riparian habitat (91E0*, 3240 of Natura 2000 network) of North-West Italy were mapped at individual tree (ITD) level using machine learning and a multi-temporal phenology-based approach. Three UAV flights were conducted at the phenological-relevant time of the year (epochs). The data were analyzed using a structure from motion (SfM) approach. The resulting orthomosaics were segmented and classified using a random forest (RF) algorithm. The training dataset was composed of field-collected data, and was oversampled to reduce the effects of unbalancing and size. Three-hundred features were computed considering spectral, textural, and geometric information. Finally, the RF model was cross-validated (leave-one-out). This model was applied to eight scenarios that differed in temporal resolution to assess the role of multi-temporality over the UAV’s VHR optical data. Results showed better performances in multi-epoch phenology-based classification than single-epochs ones, with 0.71 overall accuracy compared to 0.61. Some classes, such as Pinus sylvestris and Betula pendula, are remarkably influenced by the phenology-based multi-temporality: the F1-score increased by 0.3 points by considering three epochs instead of two

Remote sensing evaluation of Cape parrot habitat in the Eastern Cape: implications for conservation

The Cape parrot is the only endemic parrot of South Africa and is currently nationally threatened. One of the biggest threats to the Cape parrot is the past and present degradation of indigenous forest. The Amathole Mistbelt Forest in the Eastern Cape is the primary habitat for Cape parrot and has historically been heavily degraded. In order to conserve the Cape parrot effectively, there is a need to understand the spatial distribution of indigenous forest patches and their quality. There is currently not a sufficiently accurate landcover map available to fulfil this need. Thus, this study uses remotely sensed imagery at a 10 m resolution and random forest classification to (1) produce a land cover map of the indigenous forest in the Amathole region; (2) determine habitat quality of the indigenous forest, and (3) determine whether forest loss, as reported by Global Forest Watch (GFW), reflects the loss of indigenous forest or the clearing of plantations and woody alien invasives. The overall accuracy of the classification was very high at 82%. Cross validated accuracies were all high ranging from 95 – 100%, with water having the highest accuracy and indigenous forest, eucalyptus spp., pine spp., and infrastructure having the lowest accuracies. F1 scores ranged from 0.78 – 1.0, with indigenous forest ranking the second lowest at 0.80 and grassland ranking the second highest at 0.91. Indigenous forest covered 26% of the study area. Black wattle, pine spp. and eucalyptus spp. covered a combined 35% of the study area. The detailed map of indigenous forest shows the extent of its fragmentation and outlines some of the management implications associated with small forest patches. Secondly, habitat quality for Cape parrot is questioned as there is a lack of emergent canopy tree species and 30% of the matrix between forest patches is invaded by invasive alien species. Thus, it is suggested that a strong focus is put into clearing and managing invasive alien species. Lastly, GFW ‘forest cover loss' is shown to be comprised primarily of plantation felling and invasive clearing. It is suggested that there has been little loss of indigenous forest in the last 30 years. Further research will include creating an open and accessible product in the form of a Google Earth Engine App to share with conservation managers in the area

Improving LiDAR-based tree species mapping in Central European mixed forests using multi-temporal digital aerial colour-infrared photographs

Digital colour-infrared (CIR) aerial photographs, which have been collected routinely in many parts of the world, are an invaluable data source for the monitoring and assessment of forest resources. Yet, the potential of these data for automated individual tree species mapping remains largely unexplored. One way to maximize the usefulness of digital CIR aerial photographs for individual tree species mapping is to integrate them with modern and complementary remote sensing technologies such as the light detection and ranging (LiDAR) system and 3D segmentation algorithms. In this study, we examined whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airborne LiDAR-based individual tree species mapping for a temperate mixed forest in eastern Germany. Our results showed that the texture features captured by multi-temporal digital CIR orthophotos under different view-illumination conditions were species-specific. As a consequence, combining these texture features with LiDAR metrics significantly improved tree species mapping accuracy (overall accuracy: 77.4%, kappa: 0.68) compared to using LiDAR data alone (overall accuracy: 69.3%, kappa: 0.58). Among various texture features, the average gray level in the near-infrared band was found to contribute most to the classification. Our results suggest that the synergic use of multi-temporal digital aerial photographs and airborne LiDAR data has the potential to accurately classify individual tree species in Central European mixed forests

Nuevas estrategias basadas en geotecnologías de aplicación a la agricultura y ganadería de precisión

[ES]Las geotecnologías han emergido como la piedra angular del nuevo paradigma digital en el que están actualmente inmersas la agricultura y la ganadería contemporáneas, es decir, la nueva revolución agrícola, conocida como Agricultura 4.0, en la que se enmarcan las denominadas agricultura y ganadería de precisión. La obligada modernización a la que se ven sometidas las prácticas agroganaderas tradicionales viene desencadenada por el incipiente crecimiento demográfico y la consecuente demanda de productos agroalimentarios. Esta drástica transformación del mundo rural se torna imprescindible no solo para conseguir abastecer las necesidades de una población creciente, sino para rescatar a un sector primario cada vez más castigado por los elevados precios de los insumos y los escasos beneficios que se perciben. Como avales también de esta necesaria reconversión de los sistemas de manejo agropecuarios, entran también en juego pilares fundamentales de la productividad agrícola y ganadera como son la sostenibilidad medioambiental y el bienestar animal, ambos muy demandados en los productos de primera necesidad por una sociedad cada vez más concienciada con la producción respetuosa con el medio y con los animales. En este contexto, las geotecnologías no deben ser tomadas como herramientas que amenacen con sustituir los conocimientos agroganaderos tradicionales o que promuevan su desaparición. El enfoque es categóricamente opuesto, ya que tratan de perfeccionar la toma de decisiones de los agricultores y ganaderos, fundada en dicha sabiduría tradicional. Esta complementariedad resultará en nuevos modelos de gestión de los sistemas agropecuarios, que serán ostensiblemente más respetuosos con el medio que los sustenta, a la par que se maximizará el respeto hacia los principios básicos de sostenibilidad y bienestar animal. Por lo tanto, en este trabajo se plantea la siguiente hipótesis: la implementación de nuevas estrategias metodológicas basadas en geotecnologías en el sector agroganadero contribuirán a reducir los costes de producción, el tiempo empleado por agricultores y ganaderos en sus labores y el impacto medioambiental que dichas labores pudieran ocasionar, generando beneficios de corte económico, social y medioambiental. Considerando la hipótesis anteriormente expuesta, el objetivo de la presente tesis doctoral se centró en demostrar el potencial de las geotecnologías como herramientas alternativas y complementarias destinadas a la mejora de la gestión de los sistemas de manejo agroganaderos en el ámbito económico, medioambiental y desde el punto de vista del bienestar animal. Así mismo, se planteó que dichas estrategias geotecnológicas sirvan también para ahondar en el aprendizaje de nuevos conocimientos agrícolas y ganaderos. Para lograr este objetivo, se plantearon una serie de aportaciones que permitieran dilucidar la idoneidad de dichas geotecnologías en la gestión agroganadera