Tree species classification using Sentinel-2 imagery and Bayesian inference

The increased temporal frequency of optical satellite data acquisitions provides a data stream that has the potential to improve land cover mapping, including mapping of tree species. However, for large area operational mapping, partial cloud cover and different image extents can pose challenges. Therefore, methods are needed to assimilate new images in a straightforward way without requiring a total spatial coverage for each new image. This study shows that Bayesian inference applied sequentially has the potential to solve this problem. To test Bayesian inference for tree species classification in the boreo-nemoral zone of southern Sweden, field data from the study area of Remningstorp (58?27?18.35?N, 13?39?8.03?E) were used. By updating class likelihood with an increasing number of combined Sentinel-2 images, a higher and more stable cross-validated overall accuracy was achieved. Based on a Mahalanobis distance, 23 images were automatically chosen from the period of 2016 to 2018 (from 142 images total). An overall accuracy of 87% (a Cohen?s kappa of 78.5%) was obtained for four tree species classes: Betula spp., Picea abies, Pinus sylvestris, and Quercus robur. This application of Bayesian inference in a boreo-nemoral forest suggests that it is a practical way to provide a high and stable classification accuracy. The method could be applied where data are not always complete for all areas. Furthermore, the method requires less reference data than if all images were used for classification simultaneously

Center for Research on Sustainable Forests 2021 Annual Report

The Center for Research on Sustainable Forests (CRSF) and Cooperative Forestry Research Unit (CFRU) continued to move forward on multiple fronts with a particularly productive and rewarding FY18-19. This included leadership on several key new initiatives such as the Forest Climate Change Initiative (FCCI), Intelligent GeoSolutions (IGS), and a funded National Science Foundation (NSF) Track 2 EPSCoR grant (INSPIRES). This is in addition to ongoing leadership and support for important CRSF programs such as NSF’s Center for Advanced Forestry Systems (CAFS), the Northeastern Research Cooperative (NSRC), and FOR/Maine. In short, CRSF is on a bold upward trajectory that highlights its relevance and solid leadership with a rather bright future

Center for Research on Sustainable Forests 2019 Annual Report

The Center for Research on Sustainable Forests (CRSF) and Cooperative Forestry Research Unit (CFRU) continued to move forward on multiple fronts with a particularly productive and rewarding FY18-19. This included leadership on several key new initiatives such as the Forest Climate Change Initiative (FCCI), Intelligent GeoSolutions (IGS), and a funded National Science Foundation (NSF) Track 2 EPSCoR grant (INSPIRES). This is in addition to ongoing leadership and support for important CRSF programs such as NSF’s Center for Advanced Forestry Systems (CAFS), the Northeastern Research Cooperative (NSRC), and FOR/Maine. In short, CRSF is on a bold upward trajectory that highlights its relevance and solid leadership with a rather bright future

Using Landsat-Based Phenology Metrics, Terrain Variables, and Machine Learning for Mapping and Probabilistic Prediction of Forest Community Types in West Virginia

This study investigates the mapping of forest community types for the entire state of West Virginia, USA using Global Land Analysis and Discovery (GLAD) Phenology Metrics analysis ready data (ARD) derived from the Landsat time series and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study is to explore the use of globally consistent ARD data for operational forest type mapping over a large spatial extent. Mean overall accuracy calculated from 50 model replicates for differentiating seven forest community types using only variables selected from the 348 GLAD Phenology Metrics used in the study resulted in an overall accuracy (OA) of 53.36% (map-level image classification efficacy (MICE) = 0.42). Accuracy increased to a mean OA of 73.0% (MICE = 0.62) when the Oak/Hickory and Oak/Pine classes were combined to an Oak Dominant class. Once selected terrain variables were added to the model, the mean OA for differentiating the seven forest types increased to 61.58% (MICE = 0.52). Our results highlight the benefits of combining spectral data and terrain variables and also the enhancement of the product’s usefulness when probabilistic prediction are provided alongside a hard classification. The GLAD Phenology Metrics did not provide an accuracy comparable to those obtained using harmonic regression coefficients; however, they generally outperformed models trained using only summer or fall seasonal medians and performed comparably to spring medians. We suggest further exploration of the GLAD Phenology Metrics as input for other spatial predictive mapping and modeling tasks

Generation of a Land Cover Atlas of environmental critic zones using unconventional tools

L'abstract è presente nell'allegato / the abstract is in the attachmen

COBERTURA BOSCOSA AL 2021 EN LA PROVINCIA LEONCIO PRADO, PERÚ

The loss of forests is one of the main environmental problems in Peru and in various parts of the world. The present work aims to calculate the forest cover by 2021 within the Leoncio Prado province, located in the Huánuco region, Peru. Sentinel-2 images were used, which were classified on the Google Earth Engine platform using the Random Forest artificial intelligence algorithm. Likewise, the thematic accuracy of the resulting classification was evaluated using high spatial resolution Planet images. As results, it was found that the study area includes 349,811.47 ha, which represents more than 70% of the total area, while the degraded and intervened areas add up to a total of 131,392.12 ha, which come mainly from the change in use of forest to agricultural areas. Regarding the metrics that evaluate the thematic accuracy of the classification, a value of 0.77 was found in the Kappa Index and 89.14% global accuracy. Therefore, it is concluded that the forest cover is the most predominant in the Leoncio Prado province, which was classified with high thematic accuracy.La pérdida de bosques es uno de los principales problemas ambientales en el Perú y en diversas partes del mundo, en ese sentido el presente trabajo tiene por objetivo calcular la cobertura boscosa al 2021 dentro de la provincia Leoncio Prado, ubicada en la región Huánuco, Perú. Para ello, se utilizó las imágenes Sentinel-2 que fueron clasificadas en la plataforma Google Earth Engine utilizando el algoritmo de inteligencia artificial Random Forest. Asimismo, se evaluó la exactitud temática de la clasificación resultante utilizando imágenes de alta resolución espacial Planet. Como resultados se encontró que la zona de estudio presenta 349 811,47 ha lo que representa más del 70% del área total, mientras que las áreas degradas e intervenidas suman un total de 131 392,12 ha que proceden principalmente del cambio de uso de bosque a zonas agrícolas. Respecto a las métricas que evalúan la exactitud temática de la clasificación, se encontró un valor de 0,77 en el Índice de Kappa y 89,14% de exactitud global. Por lo que se concluye que la cobertura boscosa es la de mayor predominancia en la provincia Leoncio Prado, la cual fue clasificada con alta exactitud temática

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

Deep convolutional regression modelling for forest parameter retrieval

Accurate forest monitoring is crucial as forests are major global carbon sinks. Additionally, accurate prediction of forest parameters, such as forest biomass and stem volume (SV), has economic importance. Therefore, the development of regression models for forest parameter retrieval is essential. Existing forest parameter estimation methods use regression models that establish pixel-wise relationships between ground reference data and corresponding pixels in remote sensing (RS) images. However, these models often overlook spatial contextual relationships among neighbouring pixels, limiting the potential for improved forest monitoring. The emergence of deep convolutional neural networks (CNNs) provides opportunities for enhanced forest parameter retrieval through their convolutional filters that allow for contextual modelling. However, utilising deep CNNs for regression presents its challenges. One significant challenge is that the training of CNNs typically requires continuous data layers for both predictor and response variables. While RS data is continuous, the ground reference data is sparse and scattered across large areas due to the challenges and costs associated with in situ data collection. This thesis tackles challenges related to using CNNs for regression by introducing novel deep learning-based solutions across diverse forest types and parameters. To address the sparsity of available reference data, RS-derived prediction maps can be used as auxiliary data to train the CNN-based regression models. This is addressed through two different approaches. Although these prediction maps offer greater spatial coverage than the original ground reference data, they do not ensure spatially continuous prediction target data. This work proposes a novel methodology that enables CNN-based regression models to handle this diversity. Efficient CNN architectures for the regression task are developed by investigating relevant learning objectives, including a new frequency-aware one. To enable large-scale and cost-effective regression modelling of forests, this thesis suggests utilising C-band synthetic aperture radar SAR data as regressor input. Results demonstrate the substantial potential of C-band SAR-based convolutional regression models for forest parameter retrieval