Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification

Vegetation maps are essential tools for the conservation and management of landscapes as they contain essential information for informing conservation decisions. Traditionally, maps have been created using field-based approaches which, due to limitations in costs and time, restrict the size of the area for which they can be created and frequency at which they can be updated. With the increasing availability of satellite sensors providing multi-spectral imagery with high temporal frequency, new methods for efficient and accurate vegetation mapping have been developed. The objective of this study was to investigate to what extent multi-seasonal Sentinel-2 imagery can assist in mapping complex compositional classifications at fine spatial scales. We deliberately chose a challenging case study, namely a visually and structurally homogenous scrub vegetation (known as kwongan) of Western Australia. The classification scheme consists of 24 target classes and a random 60/40 split was used for model building and validation. We compared several multi-temporal (seasonal) feature sets, consisting of numerous combinations of spectral bands, vegetation indices as well as principal component and tasselled cap transformations, as input to four machine learning classifiers (Support Vector Machines; SVM, Nearest Neighbour; NN, Random Forests; RF, and Classification Trees; CT) to separate target classes. The results show that a multi-temporal feature set combining autumn and spring images sufficiently captured the phenological differences between the classes and produced the best results, with SVM (74%) and NN (72%) classifiers returning statistically superior results compared to RF (65%) and CT (50%). The SWIR spectral bands captured during spring, the greenness indices captured during spring and the tasselled cap transformations derived from the autumn image emerged as most informative, which suggests that ecological factors (e.g. shared species, patch dynamics) occurring at a sub-pixel level likely had the biggest impact on class confusion. However, despite these challenges, the results are auspicious and suggest that seasonal Sentinel-2 imagery has the potential to predict compositional vegetation classes with high accuracy. Further work is needed to determine whether these results are replicable in other vegetation types and regions

Monitoring oil spill in Norilsk, Russia using satellite data.

This paper studies the oil spill, which occurred in the Norilsk and Taimyr region of Russia due to the collapse of the fuel tank at the power station on May 29, 2020. We monitored the snow, ice, water, vegetation and wetland of the region using data from the Multi-Spectral Instruments (MSI) of Sentinel-2 satellite. We analyzed the spectral band absorptions of Sentinel-2 data acquired before, during and after the incident, developed true and false-color composites (FCC), decorrelated spectral bands and used the indices, i.e. Snow Water Index (SWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The results of decorrelated spectral bands 3, 8, and 11 of Sentinel-2 well confirmed the results of SWI, NDWI, NDVI, and FCC images showing the intensive snow and ice melt between May 21 and 31, 2020. We used Sentinel-2 results, field photographs, analysis of the 1980-2020 daily air temperature and precipitation data, permafrost observations and modeling to explore the hypothesis that either the long-term dynamics of the frozen ground, changing climate and environmental factors, or abnormal weather conditions may have caused or contributed to the collapse of the oil tank.Open access funding provided by the Qatar National Library

Random forest effectiveness for Bragança region mapping: comparing indices, number of the decision trees, and generalization

Mestrado de dupla diplomação com o Institute Agronomic and Veterinary Hassan IIRemote sensing is a domain that tends to use satellite images for classification and Land Use/Cover (LULC) mapping. For this purpose, classification algorithms are used, which are numerous and diverse, and it is necessary to establish decision criteria when choosing the algorithm. Ultimately, the main decision criterion will be the accuracy obtained in classification because the accuracy of classification may differ from one algorithm to another, even within the same algorithm, according to its variables. But there are other equally important criteria: it depends on the nature of the task, the quantity and types of data available, the type of response expected, the time and computational resources available, the depth of our knowledge about the algorithms. The methodology of each part of the work was described and the criteria for comparison were established. In this research, with the same training data, the same validation data, the same application context (7 classes), and the same image data (Sentinel-2), we tested 15 iterations with the Random Forest classification algorithm, with different tree number decision values, and 3 iterations with vegetation and soil indexes, for the production of the LULC map of the Bragança region (northeast Portugal). Finally, we evaluate the accuracy of the classification, before and after the post-classification tasks (generalization, fragmentation and removal of isolated pixels). The results obtained show that a classification with an nb-trees = 1000, including vegetation and soil indices, and after post-classification tasks, provided excellent precision results (Coefficient Kappa = 0.93, Overall accuracy = 96%, and marginal errors of omission & commission below 4%).A teledetecção é um domínio que tende a utilizar imagens de satélite para classificação e mapeamento de Uso/Cobertura da Terra (LULC). Para este fim, são utilizados algoritmos de classificação, que são numerosos e diversos, sendo necessário estabelecer critérios de decisão ao escolher o algoritmo. Em última análise, o principal critério de decisão será a precisão obtida na classificação, porque a precisão da classificação pode diferir de um algoritmo para outro, mesmo dentro do mesmo algoritmo, de acordo com as suas variáveis. Mas existem outros critérios igualmente importantes: depende da natureza da tarefa, da quantidade e tipos de dados disponíveis, do tipo de resposta esperada, do tempo e dos recursos computacionais disponíveis, da profundidade dos nossos conhecimentos sobre os algoritmos. A metodologia de cada parte do trabalho foi descrita e os critérios de comparação foram estabelecidos. Nesta investigação, com os mesmos dados de formação, os mesmos dados de validação, o mesmo contexto de aplicação (7 classes), e os mesmos dados de imagem (Sentinel-2), testámos 15 iterações com o algoritmo de classificação Random Forest, com diferentes valores de decisão de número de árvores, e 3 iterações com índices de vegetação e solo, para a produção do mapa LULC da região de Bragança (nordeste de Portugal). Finalmente, avaliámos a exactidão da classificação, antes e depois das tarefas de pós-classificação (generalização, fragmentação e remoção de pixels isolados). Os resultados obtidos mostram que uma classificação com um nb-trees = 1000, incluindo índices de vegetação e solo, e após tarefas de pós-classificação, forneceu excelentes resultados de precisão (Coeficiente Kappa = 0.93, Precisão geral =96%, e erros marginais de omissão & comissão abaixo de 4%)

Remote Sensing of Georgia Tidal Marsh Habitats Using Aerial Photography and Planetscope Satellite Imagery

Globally, tidal marshes cover about 90,800 km. Within the state of Georgia tidal marshes are primarily located behind the barrier islands and total 1,619 km2. The combination of high salinity environments and daily inundation, and being dependent on river output, make these dynamic systems. Tidal marshes provide numerous ecosystem services such as carbon and nitrogen sequestration, flood control, coastal protection, and numerous biogeochemical processes. Due to their unique position, tidal marshes are under threat from sea level rise, drought, coastal development, and large-scale disturbance events. Tidal freshwater marshes are especially susceptible to these threats due to their geographic location and small extent which have been historically understudied. By mapping tidal marshes, species composition is better understood and can be used to scale up ecosystem services, biogeochemical processes, and above ground biomass using remote sensing imagery. This study uses aerial orthoimagery along with a digital elevation model, National Wetland Inventory, and vegetation indices to map salt, brackish, and tidal freshwater marshes along the entire coast of Georgia. Higher spectral and spatial resolution PlanetScope 4- and 8-band satellite imagery was also used to map salt, brackish, and tidal freshwater marshes of the three main watersheds in coastal Georgia which include the Ogeechee, Altamaha, and Satilla Rivers. The aerial orthoimagery classification had an accuracy of 86.3% with salt marshes making up 67.8%, brackish 28.7%, and tidal freshwater 3.5% of the classified image and showed the importance of using a DEM and NWI for tidal marsh mapping. The PlanetScope classifications were comparable to the aerial classification with an accuracy of 86.5% (Ogeechee), 88.1% (Altamaha), and 75.9% (Satilla). Differences between the 4-band and 8-band PlanetScope imagery proved to be minimal. Due to the vulnerability of salt marshes to climate change, this study aims to contribute and expand upon current remote sensing studies on tidal marsh mapping

UNMANNED AERIAL SYSTEMS (UAS) AS A TOOL FOR INVESTIGATING EDGE INFLUENCES IN NEW HAMPSHIRE FORESTS

The continued decline in forest cover across New England becomes more concerning when faced with the fact that these same forests may be playing an important role in the fight against climate change. New Hampshire, in particular, is experiencing a 0.27% annual net loss in forest cover as of 2018. Increased population growth and accompanied development has resulted in the removal of forest cover and the fragmentation of once continuous forest blocks. Fragmentation can lead to further degradation of the remaining forest stands via alterations of the biotic and abiotic process at their edges. The use of unmanned aerial systems (UAS) is becoming an important tool to ensure the sustainable management of current forests stands and may help to better understand the effects of fragmentation at forest edges. Because of the relatively recent arrival of this technology, effective and appropriate testing for accurate and efficient data collection is necessary. Furthermore, UAS have not been employed yet to detect edge effects.This research investigated the impacts of UAS flight parameters on the accuracy of canopy height estimates made from UAS data by comparing UAS estimates across twelve combinations of flying height and image overlap to ground measured canopy height. A multi-temporal approach to species level mapping with UAS imagery was tested by collecting multiple dates of UAS imagery from early spring to late summer and assessing whether the inclusion of one or more dates improved classification accuracy. Additional comparisons between RGB and multi-spectral cameras were carried out. Finally, UAS imagery was used to measure and assess the changes in canopy cover with increased distance from the edge. This trend was compared to trends in canopy cover measured on the ground. The results show that flying height had no impact of the accuracy of the height estimates made from UAS data and increasing forward image overlap resulted in a significant but minor increase in accuracy. Classification accuracy was improved with the use of multi-temporal data collection but no more than three dates of optimally timed imagery was necessary. Additionally, the RGB imagery produced maps with consistently higher accuracy than the multi-spectral sensor employed in this study. Finally, we were able to detect and measure a significant trend in canopy cover that mimicked the trends found on the ground. The results of the first two parts of this dissertation will go on to provide guidance to forestry practitioners on how to collect UAS that balances accuracy and efficiency, thus reducing project costs. The final result serves as an initial demonstration of utilizing UAS for understanding edge effects and opens the door to better understanding the impacts of fragmentation over larger areas

UNMANNED AERIAL SYSTEMS (UAS) AS A TOOL FOR INVESTIGATING EDGE INFLUENCES IN NEW HAMPSHIRE FORESTS

The continued decline in forest cover across New England becomes more concerning when faced with the fact that these same forests may be playing an important role in the fight against climate change. New Hampshire, in particular, is experiencing a 0.27% annual net loss in forest cover as of 2018. Increased population growth and accompanied development has resulted in the removal of forest cover and the fragmentation of once continuous forest blocks. Fragmentation can lead to further degradation of the remaining forest stands via alterations of the biotic and abiotic process at their edges. The use of unmanned aerial systems (UAS) is becoming an important tool to ensure the sustainable management of current forests stands and may help to better understand the effects of fragmentation at forest edges. Because of the relatively recent arrival of this technology, effective and appropriate testing for accurate and efficient data collection is necessary. Furthermore, UAS have not been employed yet to detect edge effects.This research investigated the impacts of UAS flight parameters on the accuracy of canopy height estimates made from UAS data by comparing UAS estimates across twelve combinations of flying height and image overlap to ground measured canopy height. A multi-temporal approach to species level mapping with UAS imagery was tested by collecting multiple dates of UAS imagery from early spring to late summer and assessing whether the inclusion of one or more dates improved classification accuracy. Additional comparisons between RGB and multi-spectral cameras were carried out. Finally, UAS imagery was used to measure and assess the changes in canopy cover with increased distance from the edge. This trend was compared to trends in canopy cover measured on the ground. The results show that flying height had no impact of the accuracy of the height estimates made from UAS data and increasing forward image overlap resulted in a significant but minor increase in accuracy. Classification accuracy was improved with the use of multi-temporal data collection but no more than three dates of optimally timed imagery was necessary. Additionally, the RGB imagery produced maps with consistently higher accuracy than the multi-spectral sensor employed in this study. Finally, we were able to detect and measure a significant trend in canopy cover that mimicked the trends found on the ground. The results of the first two parts of this dissertation will go on to provide guidance to forestry practitioners on how to collect UAS that balances accuracy and efficiency, thus reducing project costs. The final result serves as an initial demonstration of utilizing UAS for understanding edge effects and opens the door to better understanding the impacts of fragmentation over larger areas

Multispectral remote sensing of vegetation responses to groundwater variability in the greater floristic region of the Western Cape, South Africa

>Magister Scientiae - MScGroundwater dependent vegetation (GDV) communities are increasingly threatened by the transformation of the natural environment to different land use/land cover, over-exploitation of groundwater resources and the proliferation of invasive species within the Cape Floristic Region (CFR). These changes affect the groundwater regime, level, and quality, which supports GDV. Natural resource managers often lack an understanding at appropriate scales of the nature of dependency of GDV to make informed sustainable decisions. This work thus assesses the spatial distribution of GDV and their responses to groundwater variability within the Cape floristic region from June 2017 to July 2018. To achieve this aim, firstly a literature review on the background of GDV, threats and the impact of climate change was assessed

Forest Landscape Restoration and Ecosystem Services in A Luoi District, Thua Thien Hue Province, Vietnam

Abstract The Government of Vietnam has invested efforts to increase the forest cover, and to conserve biodiversity through different forest development projects and programs. Losing natural forests and landscapes in the context of the “exhaust” of ecosystem services has been seen as burden in many mountainous areas. The Decision No.16 on ecosystem restoration, which was adopted by the Conference of the Parties to the Convention on Biological Diversity (CBD) at the 11th meeting (December 5th, 2012) stated that ecosystem restoration requires the application of suitable technologies and the fully-effective participation of local entities. This serves to identify obstacles while attempting to restore, regenerate ecosystem services and biodiversity, which have been degraded and lost in the recent decades. Furthermore, Vietnam’s National Forest Development Strategy targeted to achieve a forest area of 16.2 million hectares by the year 2020. Local people living adjacent to forests depend on the forest ecosystem services supplied from various natural forest landscapes in the area. This holds true especially for the people of Central Vietnam where the terrestrial area is narrow due to the country shape. In this area, agriculture practices play an essential role although the agricultural land is very limited due to the topographic conditions. The distinct land-uses reflect the natural distribution of plant and animal species as well as human interventions. In Vietnam, the forest ecosystems have been classified into three categories according to their main functions: special-use forest for nature conservation; protection forest for the watershed and protective measures; and production forest for commercial operations. This study was conducted in the A Luoi District, Thua Thien Hue Province. Ground truth samples were inventoried in three forest types from 150 m to 1162 m above sea level (a.s.l.) and steep slopes from 5 to 48 degrees. The elevation range was divided into the lower elevation level H1 ranging from 150 m – 699 m and into the higher elevation level H2 from 700 m-1162 m a.s.l.. The slopes were stratified into level S1 from 5-20 degrees, and into S2 from 21-48 degrees. The forest cover was classified into the types: undisturbed forest (UF), low disturbed forest (LF), and heavily disturbed forest (DF). To strengthen the classification of forest types, a t-test of extracted vegetation indices between ground truth plots and training sample plots was done. Up to date, no remote sensing-based work on ecological stratification of the natural forest landscapes has been conducted. Finding the tree species distribution, species diversity, and species composition over the sub-stratification of the elevations, slopes, and the forest types - by applying remote sensing - are necessary to classify the land-use types and to map out the availability of natural resources, especially the ecosystem services supply and demand of local people. Land-use and forest type classification may contribute remarkably to long-term planning, which has been assigned to local authorities, and which should include local communities. The entire study consists of four main parts. The first part aimed at evaluating the influence of topography on tree species diversity, distribution, and composition of the forests in Central Vietnam. A significant difference of species richness and species diversity was found in shallower and steeper slopes (p < 0.05) and a relatively high correlation of the species distribution, the number of stems, and the number of tree families with the elevation factor was found. The lower elevation and shallower slope showed higher species richness (p < 0.05) but not a significant difference between the number of families and the evenness. The dominance and the abundance of tree species among the topographic attributes were significantly different (p < 0.05). Lower elevation and shallower slope showed higher species richness and species diversity than the higher elevation and steeper slope. The most dominant and abundant tree families from different elevations and slopes included the Myrtaceae, Dipterocarpaceae, Burseraceae, Fagaceae, Moraceae, Cornaceae, Apocynaceae, Sapindaceae, Cannabaceae, Juglandaceae, Lauraceae, Myristicaeae, Annonaceae, Ebenaceae, Meliaceae, Rubiaceae, and the Rosaceae. The second part aimed at assessing the soil qualities, which belong to the most essential elements for land-use planning and agricultural production. 155 soil samples from different land-use types and topographic aspects were collected in order to compare information on soil organic carbon (SOC), soil total nitrogen (STN), and soil acidity (pH) at two soil depths. The SOC of arable land and forest plantation land was found to be higher than those of grassland and of natural forests (p < 0.05). The total nitrogen in the natural forests was significantly less, compared to the other land-use types. No significant differences in the total nitrogen content (p < 0.05) were found among arable land, plantation forest, and grassland. The soil organic carbon and the total nitrogen were high in the upper soil and less downwards, within all land-use types. The soil pH in the plantation forest and the arable land-use types showed no significant change among soil depth categories. Significant differences were not found in topographic aspects and the soil organic carbon content; however, differing trends of soil organic carbon and land-use types and aspects were found. The impact of the slope, elevation, farming system and soil texture accounted for the main differences of soil indicators under varying land-use types in the A Luoi District. The third part of this study was designed to apply remote sensing data from Landsat-8 and Sentinel-2 sources in order to classify land-cover and land-use classes (including three forest types UF, LF, and DF) in the study area by using machine learning algorithms. Further, vegetation indices were applied to find possible correlations and regressions of both, vertical and horizontal structures of the dominant forest tree species within different forest types. It was found that the vegetation indices between the ground-truth plots and the training sample plots were significantly different (p<0.05). The most dominant and abundant tree families in the context of the vertical structure were the Dipterocaparceae, Combretaceae, Moraceae, Leguminosae, Burseraceae, and the Polygalaceae. These, in the context of the horizontal structure were the Fagaceae, Lauraceae, Leguminosae, Dipterocaparceae, Myrtaceae, Myristicaceae, Euphorbiaceae, and the Clusiaceae. The results of the land cover and the land-use classification of Sentinel-2 were found to be more precise than those of Landsat-8 with the Random Forest algorithm: (Sentinel-2 with out-of-bag error of 14.3%, overall accuracy of 85.7%, kappa of 83% and Landsat-8 with out-of-bag error 31.6%, overall accuracy of 68%, kappa of 67.5%). The study found relationships (from 43% up to 66%) between four (out of ten) vegetation indices within horizontal and vertical structures of the forest stands: the Enhanced Vegetation Index (EVI), the Difference Vegetation Index (DVI), the Perpendicular Vegetation Index (PVI), and the Transformed Normalized Difference Vegetation Index (TNDVI). The fourth part evaluated potential provisioning services of the current natural forests - apart from wood and timber supply. It (i) assessed and compared the amount of non-timber forest tree species (NTFP species) in the different investigated forest types and elevations as potential resources; explored (ii) the respective demands of local people and (iii) their personal views concerning the importance of natural forests and the satisfaction with their provisioning services; and finally (iv) gathered their awareness of limited consequences of former forest development and requirements for forest landscape restoration. Thirty-nine NTFP tree species were found for various uses such as food, medicine, and resin or oil. Random on-site interviews of 120 out of 627 local households were conducted in a commune with high dependency on local natural forest products. Their importance and satisfaction ranking of natural forests - considering different target groups with respect to gender, income, age-class, and education - was commenced. Multiple methods were used to assess an array of gathering information, which are related to (a) the forest resources importance and (b) the local people satisfaction. These were set into context with the involvement of non-timber forest goods extraction, landslides, goods declination, and the perception for natural forest landscapes restoration, in order to clarify perspectives on forest provisioning services. The results revealed remarkable differences among target groups, adjustment, perceptions. The insufficient supply of NTFPs, particularly profitable natural medicine provision, urges for adapted silvicultural measures. The results imply that NTFPs from natural forests are not only very important to the local communities, but also contribute to the enrichment of biodiversity. The participation of local people in practical forest management and forest improvement should be considered in the decision-making process for natural forest landscape restoration of remote mountainous areas. The findings of this study can support sustainable forest management; natural forest landscape restoration with the involvement of local communities; conservation practices of biodiversity, based on topographic conditions; land-use planning; identification of dominant tree species using vegetation indices’ values, and land cover and land-use classification using open source satellite images. This final component will be aided by application of machine learning algorithms in the current study area and in the central mountainous area of Vietnam.2021-07-2