The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Enhancing Landsat time series through multi-sensor fusion and integration of meteorological data

Over 50 years ago, the United States Interior Secretary, Stewart Udall, directed space agencies to gather "facts about the natural resources of the earth." Today global climate change and human modification make earth observations from all variety of sensors essential to understand and adapt to environmental change. The Landsat program has been an invaluable source for understanding the history of the land surface, with consistent observations from the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors since 1982. This dissertation develops and explores methods for enhancing the TM/ETM+ record by fusing other data sources, specifically, Landsat 8 for future continuity, radar data for tropical forest monitoring, and meteorological data for semi-arid vegetation dynamics. Landsat 8 data may be incorporated into existing time series of Landsat 4-7 data for applications like change detection, but vegetation trend analysis requires calibration, especially when using the near-infrared band. The improvements in radiometric quality and cloud masking provided by Landsat 8 data reduce noise compared to previous sensors. Tropical forests are notoriously difficult to monitor with Landsat alone because of clouds. This dissertation developed and compared two approaches for fusing Synthetic Aperture Radar (SAR) data from the Advanced Land Observation Satellite (ALOS-1) with Landsat in Peru, and found that radar data increased accuracy of deforestation. Simulations indicate that the benefit of using radar data increased with higher cloud cover. Time series analysis of vegetation indices from Landsat in semi-arid environments is complicated by the response of vegetation to high variability in timing and amount of precipitation. We found that quantifying dynamics in precipitation and drought index data improved land cover change detection performance compared to more traditional harmonic modeling for grasslands and shrublands in California. This dissertation enhances the value of Landsat data by combining it with other data sources, including other optical sensors, SAR data, and meteorological data. The methods developed here show the potential for data fusion and are especially important in light of recent and upcoming missions, like Sentinel-1, Sentinel-2, and NASA-ISRO Synthetic Aperture Radar (NISAR)

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