Monitoring the vegetation start of season (SOS) across the island of Ireland using the MERIS global vegetation index

The aim of this study was to develop a methodology, based on satellite remote sensing, to estimate the vegetation Start of Season (SOS) across the whole island of Ireland on an annual basis. This growing body of research is known as Land Surface Phenology (LSP) monitoring. The SOS was estimated for each year from a 7-year time series of 10-day composited, 1.2 km reduced resolution MERIS Global Vegetation Index (MGVI) data from 2003 to 2009, using the time series analysis software, TIMESAT. The selection of a 10-day composite period was guided by in-situ observations of leaf unfolding and cloud cover at representative point locations on the island. The MGVI time series was smoothed and the SOS metric extracted at a point corresponding to 20% of the seasonal MGVI amplitude. The SOS metric was extracted on a per pixel basis and gridded for national scale coverage. There were consistent spatial patterns in the SOS grids which were replicated on an annual basis and were qualitatively linked to variation in landcover. Analysis revealed that three statistically separable groups of CORINE Land Cover (CLC) classes could be derived from differences in the SOS, namely agricultural and forest land cover types, peat bogs, and natural and semi-natural vegetation types. These groups demonstrated that managed vegetation, e.g. pastures has a significantly earlier SOS than in unmanaged vegetation e.g. natural grasslands. There was also interannual spatio-temporal variability in the SOS. Such variability was highlighted in a series of anomaly grids showing variation from the 7-year mean SOS. An initial climate analysis indicated that an anomalously cold winter and spring in 2005/2006, linked to a negative North Atlantic Oscillation index value, delayed the 2006 SOS countrywide, while in other years the SOS anomalies showed more complex variation. A correlation study using air temperature as a climate variable revealed the spatial complexity of the air temperature-SOS relationship across the Republic of Ireland as the timing of maximum correlation varied from November to April depending on location. The SOS was found to occur earlier due to warmer winters in the Southeast while it was later with warmer winters in the Northwest. The inverse pattern emerged in the spatial patterns of the spring correlates. This contrasting pattern would appear to be linked to vegetation management as arable cropping is typically practiced in the southeast while there is mixed agriculture and mostly pastures to the west. Therefore, land use as well as air temperature appears to be an important determinant of national scale patterns in the SOS. The TIMESAT tool formed a crucial component of the estimation of SOS across the country in all seven years as it minimised the negative impact of noise and data dropouts in the MGVI time series by applying a smoothing algorithm. The extracted SOS metric was sensitive to temporal and spatial variation in land surface vegetation seasonality while the spatial patterns in the gridded SOS estimates aligned with those in landcover type. The methodology can be extended for a longer time series of FAPAR as MERIS will be replaced by the ESA Sentinel mission in 2013, while the availability of full resolution (300m) MERIS FAPAR and equivalent sensor products holds the possibility of monitoring finer scale seasonality variation. This study has shown the utility of the SOS metric as an indicator of spatiotemporal variability in vegetation phenology, as well as a correlate of other environmental variables such as air temperature. However, the satellite-based method is not seen as a replacement of ground-based observations, but rather as a complementary approach to studying vegetation phenology at the national scale. In future, the method can be extended to extract other metrics of the seasonal cycle in order to gain a more comprehensive view of seasonal vegetation development

IMPACT OF CLIMATE CHANGE ON WILDLAND FIRE THREAT TO THE AMUR TIGER AND ITS HABITAT

Global biodiversity is increasingly threatened by combined pressures from human- and climate-related environmental change. Projected climate change indicates that these trends are likely to continue and may accelerate by the end of this century leading to large scale modification of species habitats. Such modification will be amplified by an increase in catastrophic natural events such as wildland fire - one of the dominant disturbance agents in boreal and temperate forests of the Russian Far East (RFE). In the RFE, large fire events lead to abrupt, extensive, and long-term conversion of forests to open landscapes, thus considerably impacting the habitat of the critically endangered Amur tiger (Panthera tigris altaica). A remotely sensed data-driven regional fire threat model (FTM) is developed to assess current and projected fire threat to the Amur tiger under scenarios of climate change. The FTM is parameterized to account for regional specifics of fire occurrence in the RFE and fire impacts on the Amur tigers, their main prey, and their habitat. Fire regimes are shown to be strongly influenced by anthropogenic use of fire and the monsoonal climate of the RFE, with large fire seasons observed during uncharacteristically dry years. Even with a large proportion of human ignition sources and periodic extreme events, fire currently poses a limited threat to the Amur tiger meta-population. The observed peaks in high fire threat conditions are localized in space and time and are likely to impact a small number of individual tigers. Under the wide range of the IPCC climate change scenarios, no considerable change in fire danger is expected by the mid-21st century. However, by the end of the 21st century under the A2 (regional self-reliance) scenario of the IPCC Special Report on Emissions, fire danger over the southern part of the RFE is predicted to increase by nearly 15%. An overlap of areas of likely increase in fire danger with areas of highest tiger habitat quality results in a 20% mean yearly increase in fire threat with a mean monthly increase of ~40% in August. The results have implications for conservation strategies aimed at securing long-term habitat availability

Spectral Characteristics and Mapping of Rice Fields using Multi-Temporal Landsat and MODIS Data: A Case of District Narowal

Availability of remote sensed data provides powerful access to the spatial and temporal information of the earth surface Real-time earth observation data acquired during a cropping season can assist in assessing crop growth and development performance As remote sensed data is generally available at large scale rather than at field-plot level use of this information would help to improve crop management at broad-scale Utilizing the Landsat TM ETM ISODATA clustering algorithm and MODIS Terra the normalized difference vegetation index NDVI and enhanced vegetation index EVI datasets allowed the capturing of relevant rice cropping differences In this study we tried to analyze the MODIS Terra EVI NDVI February 2000 to February 2013 datasets for rice fractional yield estimation in Narowal Punjab province of Pakistan For large scale applications time integrated series of EVI NDVI 250-m spatial resolution offer a practical approach to measure crop production as they relate to the overall plant vigor and photosynthetic activity during the growing season The required data preparation for the integration of MODIS data into GIS is described with a focus on the projection from the MODIS Sinusoidal to the national coordinate systems However its low spatial resolution has been an impediment to researchers pursuing more accurate classification results and will support environmental planning to develop sustainable land-use practices These results have important implications for parameterization of land surface process models using biophysical variables estimated from remotely sensed data and assist for forthcoming rice fractional yield assessmen

SEASONAL AND INTERANNUAL VARIABILITY OF EMISSIONS FROM CROP RESIDUE BURNING IN THE CONTIGUOUS UNITED STATES

Crop residue burning is a global agricultural practice used to remove excess residues before or after harvest. Crop residue burning in the contiguous United States (CONUS) has been documented at the regional and state-level by governmental organizations and in the scientific literature. Emissions from crop residue burning in the CONUS have been found to impair local and regional air quality, leading to serious health impacts and legal disputes. Currently, there is no baseline estimate for the area and emissions of crop residue burning in the CONUS. A bottom-up model for emissions calculations is employed to calculate CO2, CO, CH4, NO2, SO2, PM2.5, PM10, and Pb emissions from crop residue burning in the CONUS for the years 2003 through 2007. These atmospheric species have negative impacts on air quality and human health and are important to the carbon cycle. Spatially and temporally explicit cropland burned area and crop type products for the CONUS, necessary for emissions calculations, are developed using remote sensing approaches. The majority of crop residue burning and emissions in the CONUS are shown to occur during the spring (April - June) and fall harvests (October - December). On average, 1,239,000 ha of croplands burn annually in the CONUS with an average interannual variability of ± 91,200 ha. In general, CONUS crop residue burning emissions vary less than ±10% interannually. The states of Arkansas, California, Florida, Idaho, Texas, and Washington emit 50% of PM10, 51% of CO2, 52% of CO, and 63% of PM2.5 from all crop residue burning in the CONUS. Florida alone emits 17% of all annual CO2, CO, and PM2.5 emissions and 12% of annual PM10 emissions from crop residue burning. Crop residue burning emissions in the CONUS account for as little as 1% of global agricultural emissions and as much as 15% of all agricultural burning emissions estimates in North America, including Mexico and Canada. The results have implications for international, federal, and state-level reporting and monitoring of air quality and greenhouse gas and carbon emissions aimed at protecting human health, mitigating climate change, and understanding the carbon cycle

Standardized time-series and interannual phenological deviation : new techniques for burned-area detection using long-term MODIS-NBR datase

Typically, digital image processing for burned-areas detection combines the use of a spectral index and the seasonal differencing method. However, the seasonal differencing has many errors when applied to a long-term time series. This article aims to develop and test two methods as an alternative to the traditional seasonal difference. The study area is the Chapada dos Veadeiros National Park (Central Brazil) that comprises different vegetation of the Cerrado biome. We used the MODIS/Terra Surface Reflectance 8-Day composite data, considering a 12-year period. The normalized burn ratio was calculated from the band 2 (250-meter resolution) and the band 7 (500-meter resolution reasampled to 250-meter). In this context, the normalization methods aim to eliminate all possible sources of spectral variation and highlight the burned-area features. The proposed normalization methods were the standardized time-series and the interannual phenological deviation. The standardized time-series calculate for each pixel the z-scores of its temporal curve, obtaining a mean of 0 and a standard deviation of 1. The second method establishes a reference curve for each pixel from the average interannual phenology that is subtracted for every year of its respective time series. Optimal threshold value between burned and unburned area for each method was determined from accuracy assessment curves, which compare different threshold values and its accuracy indices with a reference classification using Landsat TM. The different methods have similar accuracy for the burning event, where the standardized method has slightly better results. However, the seasonal difference method has a very false positive error, especially in the period between the rainy and dry seasons. The interannual phenological deviation method minimizes false positive errors, but some remain. In contrast, the standardized time series shows excellent results not containing this type of error. This precision is due to the design method that does not perform a subtraction with a baseline (prior year or average phenological curve). Thus, this method allows a high stability and can be implemented for the automatic detection of burned areas using long-term time series

Humans on Earth: Global extents of anthropogenic land cover from remote sensing

This review provides a perspective of the current state of the art in remote sensing of anthropogenic land cover and human-modified landscapes at global scales. The fact that humans have adapted to almost all of Earth’s environments, yet remain strongly clustered within each of these environments influences both the nature of anthropogenic impact on Earth’s landscapes and the challenges of mapping it. Remote sensing provides a consistent synoptic view of these environments by mapping the land cover associated with the anthropogenic land uses of settlement and food production, as well as their complement in forest cover. We give brief descriptions and illustrative comparisons of several current land cover products representing the global extents of settlements, agriculture and forests derived from remote sensing. To accommodate the challenges inherent to mapping any land cover at widely varying scales, we compare size distributions of spatially contiguous land cover (rather than total area) for several global land cover products. Despite the use of different sensors and different mapping criteria, there is remarkable consistency in the size distributions of these products – both within and across land cover class. Rank-size distributions of settlements, agricultural areas and forests are all well-described by power laws spanning more than four orders of magnitude in both area and number. This consistency in the form of the distributions suggests fundamental similarities among different types of land cover. The observed similarities can be explained by depicting land cover mosaics as co-evolving spatial networks sharing common processes of nucleation, growth and connection

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

Modelling patterns and drivers of post-fire forest effects through a remote sensing approach

Forests play a significant role in the global carbon budget as they are a major carbon sink. In addition to the deforestation caused by human activities, some forest ecosystems are experiencing detrimental changes in both quantity and quality due to wildfires and climate change that lead to the heterogeneity of forest landscapes. However, forest fires also play an ecological role in the process of forming and functioning of forest ecosystems by determining the rates and direction of forest stand recovery. This process is strongly associated with various biotic and abiotic factors such as: the disturbance regimes, the soil and vegetation properties, the topography, and the regional climatic conditions. However, the factors that influence forest-recovery patterns after a wildfire are poorly understood, especially at broad scales of the boreal forest ecosystems. The study purpose of this research is to use remote sensing approaches to model and evaluate forest patterns affected by fire regimes under various environmental and climatic conditions after wildfires. We hypothesized that the forest regeneration patterns and their driving factors after a fire can be measured using remote sensing approaches. The research focused on the post-fire environment and responses of a Siberian boreal larch (Larix sibirica) forest ecosystem. The integration of different remotely sensed data with field-based investigations permitted the analysis of the fire regime (e.g. burn area and burn severity), the forest recovery trajectory as well as the factors that control this process with multi temporal and spatial dimensions. Results show that the monitoring of post-fire effects of the burn area and burn severity can be conducted accurately by using the multi temporal MODIS and Landsat imagery. The mapping algorithms of burn area and burn severity not only overcome data limitations in remote and vast regions of the boreal forests but also account for the ecological aspects of fire regimes and vegetation responses to the fire disturbances. The remote sensing models of vegetation recovery trajectory and its driving factors reveal the key control of burn severity on the spatiotemporal patterns in a post-fire larch forest. The highest rate of larch forest recruitment can be found in the sites of moderate burn severity. However, a more severe burn is the preferable condition for the area occupied quickly by vegetation in an early successional stage including the shrubs, grasses, conifer and broadleaf trees (e.g. Betula platyphylla, Populus tremula, Salix spp., Picea obovata, Larix sibirica). In addition, the local landscape variables, water availability, solar insolation and pre-fire condition are also important factors controlling the process of post-fire larch forest recovery. The sites close to the water bodies, received higher amounts of solar energy during the growing season and a higher pre-fire normalized difference vegetation index (NDVI) showed higher regrowth rates of the larch forest. This suggests the importance of seed source and water-energy availability for the seed germination and growth in the post-fire larch forest. An understanding of the fire regimes, forest-recovery patterns and post-wildfire forest-regeneration driving factors will improve the management of sustainable forests by accelerating the process of forest resilience

Gap-filling using machine learning : implementations and applications in remote sensing

Gap-filling is an important preprocessing step in remote sensing applications because it enables successful sensor-based studies by greatly recovering the Earth’s surface records lost due to sensor failures and cloud cover. To date, a great number of methods have been proposed to reconstruct missing data in remote sensing images, but methods that deliver satisfactory performance in handling large-area gaps over heterogeneous landscapes are scant. To address this problem, this thesis proposes two methods—Missing Observations Prediction based on Spectral-Temporal Metrics (MOPSTM) and Spectral and Temporal Information for Missing Data Reconstruction (STIMDR)—that are capable of recovering small and large-area gaps in Landsat time series. Machine learning algorithms are used to implement MOPSTM and STIMDR. MOPSTM applies the k-Nearest Neighbors (k-NN) regression to the target image (i.e. image that is to be reconstructed) and spectral-temporal metrics (STMs, e.g. statistical quantiles) derived from a 1-year Landsat time series. Improved from MOPSTM, STIMDR achieves more powerful performance by employing an effective mechanic that excludes dissimilar data in a longer time series (e.g., changes in land cover). The proposed methods are compared site-to-site with six state-of-the-art gap-filling methods including three temporal interpolation methods and three hybrid methods. With higher accuracy in four study sites located in Kenya, Finland, Germany, and China, MOPSTM and STIMDR have indicated more robust performance than other methods, with STIMDR yielding higher accuracy than MOPSTM. Although gap-filling methods are proposed with increasing frequency, their necessity and effects are rarely evaluated, so this has become an unsolved research gap. This thesis addresses this research gap using land use and land cover (LULC) classification and tree canopy cover (TCC) modelling with the assistance of machine learning algorithms. Random forest algorithm is used to examine whether gap-filled images outperform non-gap-filled (or actual) images in LULC and TCC applications. The results indicate that (i) gap-filled images achieve no worse performance in LULC classification than the actual image, and (ii) gap-filled predictors derived from the Landsat time series deliver better performance on average than non-gap-filled predictors in TCC modelling. Therefore, we conclude that gap-filling has positive effects on LULC classification and TCC modelling, which justifies its inclusion in image preprocessing workflows.-

Estimating Above Ground Biomass using Remote Sensing in the Sub-Tropical Climate Zones of Australia

The study focused on assessing the total above ground biomass using remote sensing in the Kimberley rangelands of Western Australia. Remote sensing has the advantage that it can rapidly provide estimates non-destructively on a large scale with a high temporal frequency. In this thesis a field sampling protocol was developed and mono- and multi-temporal above ground biomass estimation models could be calibrated and validated with field based measurements for the most significant vegetation types