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

Linking Phenology and Biomass Productivity in South Dakota Mixed-Grass Prairie

Assessing the health of rangeland ecosystems based solely on annual biomass production does not fully describe the condition of the plant community; the phenology of production can provide inferences about species composition, successional stage, and grazing impacts. We evaluated the productivity and phenology of western South Dakota mixed-grass prairie in the period from 2000 to 2008 using the normalized difference vegetation index (NDVI). The NDVI is based on 250-m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. Growing-season NDVI images were integrated weekly to produce time-integrated NDVI (TIN), a proxy of total annual biomass production, and integrated seasonally to represent annual production by cool- and warm-season species (C3 and C4, respectively). Additionally, a variety of phenological indicators including cool-season percentage of TIN were derived from the seasonal profiles of NDVI. Cool-season percentage and TIN were combined to generate vegetation classes, which served as proxies of the conditions of plant communities. TIN decreased with precipitation from east to west across the study area. However, the cool-season percentage increased from east to west, following patterns related to the reliability (interannual coefficient of variation [CV]) and quantity of midsummer precipitation. Cool-season TIN averaged 76.8% of the total TIN. Seasonal accumulation of TIN corresponded closely (R2 . 0.90) to that of gross photosynthesis data from a carbon flux tower. Field-collected biomass and community composition data were strongly related to TIN and cool-season percentage. The patterns of vegetation classes were responsive to topographic, edaphic, and land management influences on plant communities. Accurate maps of biomass production, cool- and warm-season composition, and vegetation classes can improve the efficiency of land management by facilitating the adjustment of stocking rates and season of use to maximize rangeland productivity and achieve conservation objectives. Further, our results clarify the spatial and temporal dynamics of phenology and TIN in mixed-grass prairie

A Data-driven Approach for Mapping Grasslands at a Regional Scale

Ph.D.University of Kansas, Geology 2019The goal of this research was to use a data-driven approach to develop a regional scale grassland mapping protocol with the following objectives. First, identify and characterize the spatial distribution of grassland types and land use across Kansas as well as the static or dynamic nature of grasslands over time using multi-year U.S. Department of Agriculture (USDA) Farm Service Agency (FSA) 578 data. Second, evaluate the spectral separability of four hierarchies of grassland types and land use using FSA 578 data, multi-seasonal Landsat 8 spectral bands, Landsat 8 Normalized Difference Vegetation Index (NDVI) data, and Moderate Resolution Imaging Spectrometer (MODIS) NDVI time series. Third, determine the optimal data combination, and the appropriate thematic resolution, for mapping grassland type by evaluating the modeling performance of the Random Forest (RF) classifier. A county-level analysis of the multi-year FSA 578 data found that the data were not all-inclusive of total grasslands across Kansas, but were sufficient to illustrate regional trends in grassland type, land use, and field size. Eastern Kansas was found to be more diverse in grassland type, more variable in land use, and contained a high number of smaller fields. Conversely, western Kansas consisted of larger fields that were primarily grazed native grasslands and land enrolled in the Conservation Reserve Program (CRP). These results indicate a more complex grassland landscape to map in eastern Kansas, while also providing guidance for training sample distributions for image classification. Jeffries-Matusita (JM) distance statistics were calculated for three-date multispectral Landsat 8, three-date Landsat 8 NDVI, and 23-period, 16-day composite Terra MODIS NDVI time series. The results indicate that combining the three datasets maximized the spectral separability of grassland types across all four grassland-type hierarchies. A comparison of the three datasets showed that multispectral Landsat 8 data had the highest JM distance statistics (which indicates the most separability). JM distance statistics calculated by-band and by-period consistently showed that information from spring and fall was more important than summer for separating grassland types. The results showed lower separability for land-use classes within a grassland type versus between grassland types. The spectral separability of pairwise comparisons incorporating land use between grassland types varied, indicating that land use does affect spectral separability in some instances. On the other hand, JM distance statistics did not substantially drop when more refined grassland types were aggregated to coarser grassland type classes (e.g. Level-1: cool- and warm-season), indicating that land use does not negatively affect the spectral separability of functional grassland types. The results indicate low spectral separability between brome and fescue but moderate to high separability between native and CRP, suggesting the use of a Level-1 or Level-2 thematic classification scheme for the study area. Finally, random forest models were constructed and evaluated using 2015 FSA 578 data and four datasets of remotely sensed data in two adjacent Landsat scenes (path/rows). Models were created for each of the four grassland hierarchies. The results showed that out-of-bag (OOB) error increased with grassland hierarchy complexity (the number of thematic classes) and OOB error was lowest for the combined remotely sensed dataset. Mapping CRP as a separate grassland type resulted in low producer’s accuracy levels, with CRP largely mapped as warm-season grasslands, suggesting the Level-1 classification scheme was appropriate for regional mapping of grassland types. Path/rows 27/33 and 28/33 had OOB overall accuracy levels of 87% and 92%, respectively. User’s and producer’s accuracy levels indicate that cool-season grasslands were mapped more accurately in path/row 27/33 where that class is more dominant than in 28/33. Using test data (withheld verification data) unexpectedly increased overall accuracy levels by 4% and 6% over OOB accuracies, which may have resulted from varying data proportions between OOB and test data, suggesting the need for further evaluation

Discriminating and mapping the c3 and c4 composition of grasslands in the northern Great Plains, USA

There is uncertainty about the extent and distribution of grasslands following the C3 and C4 photosynthetic pathways. Since these grasses have an asynchronous seasonal profile it should be possible to estimate and map the C3–C4 composition of grasslands from multi-temporal remote sensing imagery. This potential was evaluated using 30 weekly composite MERIS MTCI images for South Dakota, USA. Derived relationships between the remotely sensed response and composition of grasslands were significant, with R2 0.6. It also appears possible to map broad classes of grassland composition, with a three class (high, medium and low C3 cover) classification having an accuracy of 77.8%

The Spatial Distribution of Terrestrial Stable Carbon Isotopes in North America, and the Impacts of Spatial and Temporal Resolution on Static Ecological Models

Due to the unique spatial and temporal characteristics of ecological phenomena, the extent and grain size of spatial data sets essentially filter the observations. This thesis examines the impacts of temporal and spatial resolution on the modeling of terrestrial stable carbon isotopic landscapes (isoscapes). I model the distribution of leaf stable carbon isotope composition (delta13C) for the continent of North America at multiple temporal and spatial resolutions. I generate each delta13C isoscape variation by first predicting the relative abundance of C3/C4 vegetation cover using monthly climate grids, crop distribution/type grids, and remote sensing data of plant life form, and then applying the respective leaf delta13C endmembers to each pixel. One application of isoscapes is predicting the geographic origin of migratory animals by relating the isotopic signature of animal tissue to environmental isotope values. I conduct multiple exercises in geographic origin assignment using known-origin feather isotope data of mountain plover (Charadrius montanus) chicks as an indirect means of testing the impact of resolution on delta13C isoscapes. Results indicate that temporal resolution does have a significant impact on predicted isoscape layers, and in turn, geographic origin assignment efficacy. Temporal periods that did not correspond to tissue growth exhibited a mismatch in the range of predicted vegetation delta13C values relative to the range of measured feather delta13C values and therefore were not useful in generating geographic origin assignments. The spatial resolution of modeled delta13C minimally impacted assignment accuracy and precision compared to temporal resolution; however, the current analysis was limited by the spatial resolution of the input data set. These results should be further explored to better characterize spatiotemporal ecological characteristics of migratory animals and to improve modeling of the isotopic landscape itself

INVESTIGATING THE IMPACTS OF ANTHROPOGENIC AND CLIMATIC CHANGES ON THE STEPPE ECOSYSTEM IN CHINA’S LOESS PLATEAU AND THE MIXED-GRASS PRAIRIE REGION IN SOUTHWEST OKLAHOMA, USA

Grassland ecosystems occupy approximately 40% of the earth’s terrestrial area and represent one of most important ecosystems on Earth in terms of its impacts on global food supply, carbon sequestration and maintaining biodiversity. Grassland ecosystems are very sensitive to disturbances caused by either climatic or anthropogenic changes such as changes in precipitation regimes or management practices. The objective of this dissertation is to investigate the impacts imposed by grassland restoration activities and changes in precipitation anomalies on the steppe in China’s Loess Plateau and the mixed-grass prairie in southwest Oklahoma. In chapter two, I analyzed how large-scale vegetation conservation programs affected the grassland dynamics in China’s Loess Plateau by combining remotely sensed data with socio-economic statistics. The results of this study showed that the impact of vegetation conservation programs on vegetation change in the Loess Plateau is twofold. On the one hand, vegetation conservation programs target marginal lands. Thus, significant vegetation increases due to cropland conversion and afforestation can be found in these regions. On the other hand, intensified agricultural production can be found in croplands with suitable topography and well-established irrigation systems which were not enrolled in conservation programs to offset the agricultural production loss caused by vegetation conservation programs elsewhere. In chapter three, I demonstrated a new methodology on mapping the historical distribution of grassland species in southwest Oklahoma based on the Random Forest classification algorithm. In this study, elevation, soil pH and soil clay content were found to be significant variables for predicting the distribution of C3 and C4 grassland species. With the mapped distribution of grassland species between 1981 and 2010, in chapter four, I examined the relationship between changes in precipitation anomalies and the dynamics of relative abundance of C3 and C4 grassland species in southwest Oklahoma. In this study, significant decreases of C3/C4 ratio were identified in pasture/hay fields due to the increases in C4 abundance resulting from the decreases of sparsely vegetated area between 2005 and 2010. I suspect that the increase in C4 abundance was a drought adaptation strategy adopted by ranchers. Because C4 species are more tolerant of drought conditions and thus can help to maintain stable forage/hay production when negative precipitation anomalies prevailed during the growing season of C3 species

Validation and application of the MERIS Terrestrial Chlorophyll Index.

Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity

Remote sensing of the distribution and quality of subtropical C3 and C4 grasses.

Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2013.Global climate change is expected to be accompanied by changes in the composition of plant functional types. Such changes are predicted to follow shifts in the percentage cover and abundance of grass species, following the C3 and C4 photosynthetic pathways. These two groups differ in a number of physiological, structural and biochemical aspects. It is important to measure these characteristic properties because they affect ecosystem processes, such as nutrient cycling. High spectral and spatial resolution remote sensing systems have been proven to offer data, which can be used to accurately detect, classify and map plant species. The major challenge, however, is that the spectral reflectance data obtained over many narrow contiguous channels (i.e. hyperspectral data) represent multiple classes that are often mixed for a limited training-sample size. This is commonly referred to as the Hughes phenomenon or “the curse of dimensionality”. In the context of hyperspectral data analysis, the Hughes phenomenon often introduces a high degree of multicollinearity, which is caused by the use of highly-correlated spectral predictors. Multicollinearity is a prominent problem in processing hyperspectral data for vegetation applications, due to similarities in the spectral reflectance properties of biophysical and biochemical attributes. This study explored an innovative method to solve the problems associated with spectral dimensionality and the related multicollinearity, by developing a user-defined inter-band correlation filter function to resample hyperspectral data. The proposed resampling technique convolves the spectral dependence information between a chosen band-centre and its shorter and longer wavelength neighbours. The utility of the new resampling technique was assessed for discriminating C3 (Festuca costata) and C4 (Themeda triandra and Rendlia altera) grasses and for predicting their nutrient content (nitrogen, protein, moisture, and fibre), using partial least squares and random forest regressions. In general, results obtained showed that the user-defined inter-band correlation filter technique can mitigate the problem of multicollinearity in both classification and regression analyses. Wavebands in the shortwave infrared region were found to be very important in regression and classification analyses, using field spectra-only datasets. Next, the analyses were up-scaled from field spectra to the new generation multispectral satellite, WorldView-2 imagery, which was acquired for the Cathedral Peak region of the Drakensberg Mountains. The results obtained, showed that the WV2 image data contain useful information for classifying the C3 and C4 grasses and for predicting variability in their nitrogen and fibre concentrations. This study makes a contribution by developing a user-defined inter-band correlation filter to resample hyperspectral data, and thereby mitigating the high dimensionality and multicollinearity problems, in remote sensing applications involving C3 and C4 grass species or communities

Validation and application of the MERIS Terrestrial Chlorophyll Index

Climate is one of the key variables driving ecosystems at local to global scales. How and to what extent vegetation responds to climate variability is a challenging topic for global change analysis. Earth observation provides an opportunity to study temporal ecosystem dynamics, providing much needed information about the response of vegetation to environmental and climatic change at local to global scales. The European Space Agency (ESA) uses data recorded by the Medium Resolution Imaging Spectrometer (MERlS) in red I near infrared spectral bands to produce an operational product called the MERlS Terrestrial Chlorophyll Index (MTCI). The MTCI is related to the position of the red edge in vegetation spectra and can be used to estimate the chlorophyll content of vegetation. The MTCI therefore provides a powerful product to monitor phenology, stress and productivity. The MTCI needs full validation if it is to be embraced by the user community who require precise and consistent, spatial and temporal comparisons of vegetation condition. This research details experimental investigations into variables that may influence the relationship between the MTCI and vegetation chlorophyll content, namely soil background and sensor view angle, vegetation type and spatial scale. Validation campaigns in the New Forest and at Brooms Barn agricultural study site reinforced the strong correlation between chlorophyll content and MTCI that was evident from laboratory spectroscopy investigations, demonstrating the suitability of the MTCI as a surrogate for field chlorophyll content measurements independent of cover type. However, this relationship was significantly weakened where the leaf area index (LAI) was low, indicating that the MTCI is sensitive to the effects of soil background. In the light of such conclusions, this project then assessed the MTCI as a tool to monitor changes in ecosystem phenology as a function of climatic variability, and the suitability of the MTCI as a surrogate measure of photosynthetic light use efficiency, to model ecosystem gross primary productivity (GPP) at various sites in North America with contrasting vegetation types. Changes in MTCI throughout the growing season demonstrated the potential of the MTCI to estimate vegetation dynamics, characterising the temporal characteristics in both phenology and gross primary productivity.EThOS - Electronic Theses Online ServiceGBUnited Kingdo