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

Leveraging machine learning to extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): a case study in forest-type mapping

Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy

Mapeamento e caracterização da vegetação e geoambientes de Hope Bay, Península Antártica, usando imangens de VANT

As mudanças ambientais em áreas sem cobertura permanente de gelo na Antártica têm levado ao rápido desenvolvimento de comunidades vegetais da área, destacando a necessidade de monitoramento eficaz e contínuo para estimar a dinâmica em constante mudança deste subsistema. Devido à complexidade da cobertura superficial do ambiente antártico e a heterogeneidade espacial e homogeneidade espectral da vegetação criptogâmica, a detecção e o mapeamento da vegetação por técnicas de sensoriamento remoto ainda permanecem limitados. Nos últimos anos, o advento de plataformas como os veículos aéreos não tripulados (VANTs) tem possibilitado um mapeamento detalhado da superfície e das características geoecológicas, principalmente na região da Antártica Marítima. Como as imagens de VANT permitem análises abrangentes da vegetação, este estudo tem como objetivo mapear e identificar as características das comunidades vegetais de Hope Bay, no extremo norte da Península Antártica, explorando técnicas de sensoriamento remoto e apontando possibilidades para o uso de sensores de altíssima resolução espacial na identificação desses alvos. Para tanto, foram realizadas: (i) a avaliação da adequação do NDVI de imagens de VANT, Sentinel-2 e Landsat 8 para o mapeamento de áreas vegetadas na Antártica Marítima, com o estabelecimento de intervalos de classe por meio de parâmetros estatísticos (i.e. média e desvio padrão); (ii) a classificação de tipos (i.e. algas, musgos e liquens) e subtipos de vegetação antártica por meio da análise de imagem baseada em objetos geográficos (GEOBIA) e do algoritmo de aprendizagem de máquina Random Forest; e (iii) a avaliação da relação entre as comunidades vegetais da área e um conjunto de variáveis ambientais (e.g. altitude, declividade, aspecto, geoforma, solo, etc.) usando técnicas de sensoriamento remoto e geoprocessamento, com a divisão da área em geoambiente. Os resultados do NDVI mostram que diferentes sensores podem retornar valores diferentes para a mesma classe de vegetação. A classificação do índice permite identificar da cobertura vegetal da área de estudo, sendo que uma correspondência espacial na distribuição das classes é observada entre as classificações. Além disso, é identificada uma associação entre classes de NDVI e tipos de vegetação, onde os liquens são geralmente classificados em classes de probabilidade mais baixas e algas e musgo em classes de probabilidade mais altas. O estudo mostra o potencial do NDVI aplicado à vegetação antártica e a significância dos parâmetros estatísticos na definição dos intervalos de classe, reduzindo a necessidade de dados de campo em áreas remotas. Os resultados do GEOBIA mostram que as subclasses têm baixa separabilidade quando considerado apenas o número digital das bandas G-R-NIR do sensor S110. E que uso combinado de dados de múltiplas camadas de informação (i.e. espectrais, índices, IHS, topográficos e texturas) potencializa a separabilidade entre as subclasses e fornece o melhor resultado para a detecção e o delineamento de diferentes tipos de vegetação, com uma acurácia geral de 0.966 e um coeficiente Kappa de 0.946. O estudo demonstra a relevância dos dados de VANT em fornecer as propriedades necessárias para a classificação eficaz da vegetação da Antártica Marítima, mesmo em imagens obtidas por sensores com baixa resolução espectral. Os resultados da relação entre as comunidades vegetais e os fatores ambientais mostram que a vegetação da área de estudo segue um gradiente altimétrico, com algas mais presentes em altitudes abaixo de 60 m e musgos e liquens mais presentes a altitudes acima de 60 m. As comunidades vegetais estão principalmente distribuídas em encostas setentrionais e em terrenos planos ou de leve inclinação, com os solos ornitogênicos mais associados a algas e musgos e o solo Lithic Haploturbels mais associado a liquens e musgos. O estudo mostra que as comunidades mapeadas são influenciadas pelos fatores ambientais investigados, e que o conjunto de informações reunidas para Hope Bay permite a setorização da área de estudo em sete geoambientes, contribuindo para o monitoramento da cobertura vegetal da Antártica frente às mudanças ambientais e auxiliando no entendimento da evolução e da estabilidade da área livre de gelo.Environmental changes in areas without permanent ice cover in Antarctica have led to rapid development of plant communities in the area, highlighting the need for effective and continuous monitoring to estimate the ever-changing dynamics of the subsystem. Due to the complexity of surface coverage of the Antarctic environment and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation, detection and mapping of vegetation by remote sensing techniques remains limited. In recent years, the advent of platforms such as unmanned aerial vehicles (UAVs) has enabled a detailed mapping of surface and geoecological characteristics, particularly in the maritime Antarctic region. As UAV images allow for a comprehensive vegetation analysis, this study aims to map and identify the characteristics of vegetal communities in Hope Bay, northern tip of the Antarctic Peninsula, by exploring remote sensing techniques and identifying the potential of ultra-high spatial resolution sensors in identifying these targets. For this reason, the following carried out: (i) assessment of the adequacy of NDVI derived from UAV, Sentinel-2 and Landsat 8 to map vegetated areas in the maritime Antarctic, setting range intervals using statistical parameters (i.e. mean and standard deviation); (ii) classification of vegetation types (i.e. algae, mosses and lichens) and subtypes by geographic object analysis and Random Forest machine learning algorithm; and (iii) assessment of the relationship between vegetation communities in the area and a set of environmental variables (e.g. altitude, slope, aspect, landform, soil, etc.) using remote sensing and geoprocessing techniques, with the division of the area into geoenvironments. NDVI results show that different sensors provide different values for the same vegetation class. NDVI classification enabled the identification of areas showing vegetation cover, and correspondence in vegetation distribution and classes can be observed across all classifications. A close association between NDVI classes and Antarctic vegetation type is identified, where lichens are generally classified in lower probability classes, and algae and moss in higher probability classes. This work shows the potential of NDVI applied to Antarctic vegetation and the significance of data statistical parameters in the selection of thresholds, reducing the need for ground-truth information in remote areas. Results from GEOBIA show that subclasses have low separability when considering only the digital number of G-R-NIR bands of S110 sensor. A combination of data from multiple layers (i.e. spectral, indices, HSI, topographic and texture) enhances the separability between subclasses and provides the best results for the detection and delineation of different vegetation types – with a general accuracy of 0.966 and a Kappa coefficient of 0.946. This study demonstrates the relevance of UAV data in providing the properties necessary for the effective classification of maritime Antarctic vegetation, even in images obtained by sensors with low spectral resolution. The results of the relationship between vegetal communities and environmental factors show that the vegetation follows an altitudinal gradient in the study area, with algae more present at altitudes below 60 m and mosses and lichens more present at altitudes above 60 m. Communities are mainly distributed on northern slopes and on flat or gently sloping; ornithogenic soils are more associated with algae and mosses and Lithic Haploturbels soil more associated with lichens and mosses. The study shows that the mapped communities are affected by the environmental factors studied, and that data gathered for Hope Bay enables the study area to be zoned in seven geo-environments, contributing for the monitoring of the Antarctic vegetation cover in the face of environmental changes and improving understanding of the evolution and stability of ice-free areas

Forest Types Classification Based on Multi-Source Data Fusion

Forest plays an important role in global carbon, hydrological and atmospheric cycles and provides a wide range of valuable ecosystem services. Timely and accurate forest-type mapping is an essential topic for forest resource inventory supporting forest management, conservation biology and ecological restoration. Despite efforts and progress having been made in forest cover mapping using multi-source remotely sensed data, fine spatial, temporal and spectral resolution modeling for forest type distinction is still limited. In this paper, we proposed a novel spatial-temporal-spectral fusion framework through spatial-spectral fusion and spatial-temporal fusion. Addressing the shortcomings of the commonly-used spatial-spectral fusion model, we proposed a novel spatial-spectral fusion model called the Segmented Difference Value method (SEGDV) to generate fine spatial-spectra-resolution images by blending the China environment 1A series satellite (HJ-1A) multispectral image (Charge Coupled Device (CCD)) and Hyperspectral Imager (HSI). A Hierarchical Spatiotemporal Adaptive Fusion Model (HSTAFM) was used to conduct spatial-temporal fusion to generate the fine spatial-temporal-resolution image by blending the HJ-1A CCD and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The spatial-spectral-temporal information was utilized simultaneously to distinguish various forest types. Experimental results of the classification comparison conducted in the Gan River source nature reserves showed that the proposed method could enhance spatial, temporal and spectral information effectively, and the fused dataset yielded the highest classification accuracy of 83.6% compared with the classification results derived from single Landsat-8 (69.95%), single spatial-spectral fusion (70.95%) and single spatial-temporal fusion (78.94%) images, thereby indicating that the proposed method could be valid and applicable in forest type classification