Seasonality of vegetation types of South America depicted by moderate resolution imaging spectroradiometer (MODIS) time series

The development, implementation and enforcement of policies involving the rational use of the land and the conservation of natural resources depend on an adequate characterization and understanding of the land cover, including its dynamics. This paper presents an approach for monitoring vegetation dynamics using high-quality time series of MODIS surface reflectance data by generating fraction images using Linear Spectral Mixing Model (LSMM) over South America continent. The approach uses physically-based fraction images, which highlight target information and reduce data dimensionality. Further dimensionality was also reduced by using the vegetation fraction images as input to a Principal Component Analysis (PCA). The RGB composite of the first three PCA components, accounting for 92.9% of the dataset variability, showed good agreement with the main ecological regions of South America continent. The analysis of 21 temporal profiles of vegetation fraction values and precipitation data over South America showed the ability of vegetation fractions to represent phenological cycles over a variety of environments. Comparisons between vegetation fractions and precipitation data indicated the close relationship between water availability and leaf mass/chlorophyll content for several vegetation types. In addition, phenological changes and disturbance resulting from anthropogenic pressure were identified, particularly those associated with agricultural practices and forest removal. Therefore the proposed method supports the management of natural and non-natural ecosystems, and can contribute to the understanding of key conservation issues in South America, including deforestation, disturbance and fire occurrence and management

Evaluation of MODIS LAI/FPAR product Collection 6. Part 2: Validation and intercomparison

The aim of this paper is to assess the latest version of the MODIS LAI/FPAR product (MOD15A2H), namely Collection 6 (C6). We comprehensively evaluate this product through three approaches: validation with field measurements, intercomparison with other LAI/FPAR products and comparison with climate variables. Comparisons between ground measurements and C6, as well as C5 LAI/FPAR indicate: (1) MODIS LAI is closer to true LAI than effective LAI; (2) the C6 product is considerably better than C5 with RMSE decreasing from 0.80 down to 0.66; (3) both C5 and C6 products overestimate FPAR over sparsely-vegetated areas. Intercomparisons with three existing global LAI/FPAR products (GLASS, CYCLOPES and GEOV1) are carried out at site, continental and global scales. MODIS and GLASS (CYCLOPES and GEOV1) agree better with each other. This is expected because the surface reflectances, from which these products were derived, were obtained from the same instrument. Considering all biome types, the RMSE of LAI (FPAR) derived from any two products ranges between 0.36 (0.05) and 0.56 (0.09). Temporal comparisons over seven sites for the 2001–2004 period indicate that all products properly capture the seasonality in different biomes, except evergreen broadleaf forests, where infrequent observations due to cloud contamination induce unrealistic variations. Thirteen years of C6 LAI, temperature and precipitation time series data are used to assess the degree of correspondence between their variations. The statistically-significant associations between C6 LAI and climate variables indicate that C6 LAI has the potential to provide reliable biophysical information about the land surface when diagnosing climate-driven vegetation responses.Help from MODIS and VIIRS Science team members is gratefully acknowledged. This work is supported by the MODIS program of NASA and partially funded by the National Basic Research Program of China (Grant No. 2013CB733402) and the key program of NSFC (Grant No. 41331171). Kai Yan gives thanks for the scholarship from the China Scholarship Council. (MODIS program of NASA; 2013CB733402 - National Basic Research Program of China; 41331171 - NSFC; China Scholarship Council

Evaluation of MODIS LAI/FPAR product Collection 6. Part 1: consistency and improvements

As the latest version of Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products, Collection 6 (C6) has been distributed since August 2015. This collection is evaluated in this two-part series with the goal of assessing product accuracy, uncertainty and consistency with the previous version. In this first paper, we compare C6 (MOD15A2H) with Collection 5 (C5) to check for consistency and discuss the scale effects associated with changing spatial resolution between the two collections and benefits from improvements to algorithm inputs. Compared with C5, C6 benefits from two improved inputs: (1) L2G–lite surface reflectance at 500 m resolution in place of reflectance at 1 km resolution; and (2) new multi-year land-cover product at 500 m resolution in place of the 1 km static land-cover product. Global and seasonal comparison between C5 and C6 indicates good continuity and consistency for all biome types. Moreover, inter-annual LAI anomalies at the regional scale from C5 and C6 agree well. The proportion of main radiative transfer algorithm retrievals in C6 increased slightly in most biome types, notably including a 17% improvement in evergreen broadleaf forests. With same biome input, the mean RMSE of LAI and FPAR between C5 and C6 at global scale are 0.29 and 0.091, respectively, but biome type disagreement worsens the consistency (LAI: 0.39, FPAR: 0.102). By quantifying the impact of input changes, we find that the improvements of both land-cover and reflectance products improve LAI/FPAR products. Moreover, we find that spatial scale effects due to a resolution change from 1 km to 500 m do not cause any significant differences.Help from MODIS & VIIRS Science team members is gratefully acknowledged. This work is supported by the MODIS program of NASA and partially funded by the National Basic Research Program of China (Grant No. 2013CB733402), the key program of NSFC (Grant No. 41331171) and Chinese Scholarship Council. (MODIS program of NASA; 2013CB733402 - National Basic Research Program of China; 41331171 - NSFC; Chinese Scholarship Council

Modelling global pyrogeography using data derived from satellite imagery

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia - ULVegetation burning has an important impact on the global atmosphere and vegetated land surface. Deforestation fires, peatland fires, and ecosystems with shortening fire return interval contribute substantially to the build-up of atmospheric greenhouse gases affecting environmental quality and the climate system at local and regional scales. Recognition of the role of fire in the Earth system led to its designation as an Essential Climate Variable (ECV), a physical, chemical, or biological variable that has a crucial contribution towards characterization of Earth’s climate. The central task of this thesis was the development of a new global classification and map of fire regimes, using multiple correspondence analysis and hierarchical clustering, and relying on active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD14ML product. That work was preceded by study dedicated to a thorough screening and exploratory spatial analysis of the dataset, and led to the development of an improved algorithm for identifying individual active fire clusters, and to global analysis of size inequality in their statistical distributions. In addition to this core research, other continental-global pyrogeography studies were developed, and are presented, dealing with: the time lag between the timing of optimal fire weather conditions and peak fire season dates as a diagnostic of anthropogenic vegetation burning; the spatial non-stationarity in the parameters of the relationship between population density and area burned; and the modulation of weekly cycles of vegetation burning in African croplands by regionally dominant religious affiliation. We hope that this set of studies may constitute a useful contribution to the burgeoning topic of global pyrogeograph

Mapping Weekly Rangeland Vegetation Productivity Using MODIS Algorithms

The great spatial extent of rangelands combined with recent emphasis on rangeland health has prompted a need for more efficient and cost effective management tools. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor of the Earth Observing System (EOS) will offer improved and more timely monitoring of rangeland vegetation, and, unlike any previous satellite sensor, the publicly available MODIS data stream will include estimates of rangeland productivity. These estimations of rangeland productivity can be used regionally for measuring biomass production and will be available every eight-days, with global coverage at 1- km^ resolution. MODIS derived estimates of rangeland productivity combine remote sensing information with daily meteorological data as inputs to a mathematical model of photosynthetic conversion of solar radiation into plant carbohydrates. Vegetation productivity is .a measure of rangeland vegetation vigor and growth capacity, which are important components of rangeland management and health assessment. Using MODIS data, it will be possible to characterize rangeland vegetation seasonality, estimate herbage quantity and, monitor the rates and trends of change in primary production. Consistent, objective and frequent productivity estimates will be available for even the most inaccessible rangelands. Potential applications of weekly and annual productivity estimates are demonstrated on the Shoshone BLM Administrative District and a larger portion of the Interior Northwestern United States. Productivity estimates were derived using Advanced Very High-Resolution Radiometer data as a surrogate for the MODIS data stream. Shrub and grassland vegetation seasonality for 1991 was characterized. Herbage quantity was estimated from the 1993 shrub and grassland regional net primary production. A 5-year average productivity from 1990 - 1994 and departures from that average were calculated for the years 1991 and 1993. The measures of departure indicated that 1991 was regionally less productive and 1993 more productive than the five year average. Collaboration between rangeland scientists and managers is necessary to realize the potential for EOS-derived vegetation productivity as a management tool. Future research will include field calibration of the productivity algorithms and exploration of new techniques for using EOS-derived productivity measures for rangeland management. Measures of rangeland productivity could become part of an integrated rangeland system analysis. This may permit differentiation between anthropogenic, biotic, and abiotic factors as the primary cause of declining productivity. Other research may include customization of biome properties for selected regions

Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data

The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area

Anthropogenic and climatic control upon vegetation fires: new insights from satelite observations to assess current and future impacts

Doutoramento em Engenharia Florestal - Instituto Superior de AgronomiaVegetation fires actively participate in ecosystem dynamics and atmospheric composition. Their contemporaneous occurrence and impacts – described under the concept of “fire regimes” – is driven by climate, vegetation, and human activities – the components of the “fire triangle”. The gaps in our understanding of those drivers hamper the proper consideration of fires in various domains, including ecosystems management, vegetation modeling, and climate change investigation. This thesis capitalizes on satellite observations to depict the anthropogenic and climatic influence on fire regimes. Fire inter-annual variability is shown to be dominated by large scale climatic patterns, of which the El Niño-Southern Oscillation has the most widespread and long term footprint. Fire frequency and seasonality are more complex, being determined by the interaction of all three factors of the fire triangle. The evaluation of a vegetation-fire model thus reveals significant discrepancies. It suggests a great margin of progress on representing of the anthropogenic factor, supported by the wide range of fire practices identified from fire season dynamics. A model specific to tropical deforestation fires is developed, as a regional application of this thesis contributions. Climate is a forceful safeguard against forest conversion progress, but ongoing environmental changes could revert the situation

Assessing the effect of band selection on accuracy of pansharpened imagery: application to young woody vegetation mapping

Expansion of woody vegetation has adverse effects on ecosystem services, and thus it is desirable to contain the problem at the early developmental stages. This can be aided by using high spatial resolution remotely-sensed data. The study investigated the effect of band selection during pansharpening on the ability to discriminate young woody vegetation from coexisting land cover types. Red-green-blue (RGB) spectral bands (30 m) of Landsat 8 imagery was pansharpened using the panchromatic band (15 m) of the same image to improve spatial resolution. Near-infrared (NIR), shortwave-infrared 1 (SWIR1) and shortwave-infrared 2 (SWIR2), bands were used respectively as the fourth spectral band during pansharpening, resulting in three pansharpened images. Unsupervised classification was performed on each pansharpened image as well as non-pansharpened multispectral image. The overall accuracies of classification derived from the pansharpened image was higher (87% − 89%) than that derived from the non-pansharpened multispectral image (83%). The study shows that band selection did not affect the classification accuracy of woody vegetation significantly. In addition, the study shows the potential of pansharpened Landsat data in detecting woody vegetation encroachment at the early growth stage.Keywords: Young woody vegetation, Landsat, pansharpening, unsupervised classificatio

Global and regional trends of Aerosol Optical Thickness derived using satellite- and ground-based observations

Atmospheric aerosol plays a critical role for human health, air quality, long range transport of pollution, and the Earth s radiative balance, thereby influencing global climate change. To test our scientific understanding and provide an evidence base for policymakers, long-term temporal changes of local, regional, and global aerosols are needed. Remote sensing from satellite borne and ground based observations offers unique opportunities to provide such data. However, only a few studies have discussed the limitations, associated with unrepresentative sampling originating from large/persistent cloud disturbance and limited/different sampling (limited orbital periods and different sampling times) in the trend analysis. Using a linear weighted model, the long-term trends of global AOTs from various polar orbiting satellites and ground observations: MODIS (aboard Terra), MISR (Terra), SeaWiFS (OrbView-2), MODIS (Aqua), and AERONET have been analyzed. In this manner, the present study attempts to minimize the influence of unrepresentative sampling in the trend analysis. Throughout terrestrial and marine regions, temporal increase of cloud-free AOTs were dominat over the globe (GL), northern (NH), and southern hemisphere (SH) (up to 0.00348±0.00185 for GL, 0.00514±0.00272 for NH, and 0.00232±0.00124 per year for SH). Generally, consistently in all observations, the weighted trends over Eastern US and OECD Europe showed a strong decreasing AOT (up to -0.00376±0.00174 for Eastern US and -0.00530±0.00304 per year for OECD Europe) attributed to the recent environmental legislation and resulting regulation of emissions. A significant increase was observed over Saharan/Arabian deserts, South, and East Asia (up to 0.00618±0.00326, 0.01452±0.00615, and 0.01939±0.00986 per year, respectively). These in part dramatic increases are caused by the enhanced amount of aerosol transported/emitted from industrialization, urbanization, deforestation, desertification, and climate change. Overall large/persistent cloud disturbance all year round and the limited/different sampling of polar orbiting satellites represent a challenge, which has been addressed successfully in this study for the accurate determination of aerosol amount and its trends

Vegetation dynamics in northern south America on different time scales

The overarching goal of this doctoral thesis was to understand the dynamics of vegetation activity occurring across time scales globally and in a regional context. To achieve this, I took advantage of open data sets, novel mathematical approaches for time series analyses, and state-of-the-art technology to effectively manipulate and analyze time series data. Specifically, I disentangled the longest records of vegetation greenness (>30 years) in tandem with climate variables at 0.05° for a global scale analysis (Chapter 3). Later, I focused my analysis on a particular region, northern South America (NSA), to evaluate vegetation activity at seasonal (Chapter 4) and interannual scales (Chapter 5) using moderate spatial resolution (0.0083°). Two main approaches were used in this research; time series decomposition through the Fast Fourier Transformation (FFT), and dimensionality reduction analysis through Principal Component Analysis (PCA). Overall, assessing vegetation-climate dynamics at different temporal scales facilitates the observation and understanding of processes that are often obscured by one or few dominant processes. On the one hand, the global analysis showed the dominant seasonality of vegetation and temperature in northern latitudes in comparison with the heterogeneous patterns of the tropics, and the remarkable longer-term oscillations in the southern hemisphere. On the other hand, the regional analysis showed the complex and diverse land-atmosphere interactions in NSA when assessing seasonality and interannual variability of vegetation activity associated with ENSO. In conclusion, disentangling these processes and assessing them separately allows one to formulate new hypotheses of mechanisms in ecosystem functioning, reveal hidden patterns of climate-vegetation interactions, and inform about vegetation dynamics relevant for ecosystem conservation and management