Spatiotemporal Assessment of Vegetation Indices and Land Cover for Erbil City and Its Surrounding Using Modis Imageries

The rate of global urbanization is exponentially increasing and reducing areas of natural vegetation. Remote sensing can determine spatiotemporal changes in vegetation and urban land cover. The aim of this work is to assess spatiotemporal variations of two vegetation indices (VI), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), in addition land cover in and around Erbil city area between the years 2000 and 2015. MODIS satellite imagery and GIS techniques were used to determine the impact of urbanization on the surrounding quasi-natural vegetation cover. Annual mean vegetation indices were used to determine the presence of a spatiotemporal trend, including a visual interpretation of time-series MODIS VI imagery. Dynamics of vegetation gain or loss were also evaluated through the study of land cover type changes, to determine the impact of increasing urbanization on the surrounding areas of the city. Monthly rainfall, humidity and temperature changes over the 15-year-period were also considered to enhance the understanding of vegetation change dynamics. There was no evidence of correlation between any climate variable compared to the vegetation indices. Based on NDVI and EVI MODIS imagery the spatial distribution of urban areas in Erbil and the bare around it has expanded. Consequently, the vegetation area has been cleared and replaced over the past 15 years by urban growth

Augmenting Land Cover/Land Use Classification by Incorporating Information from Land Surface Phenology: An Application to Quantify Recent Cropland Expansion in South Dakota

Understanding rapid land change in the U.S. NGP region is not only critical for management and conservation of prairie habitats and ecosystem services, but also for projecting production of crops and biofuels and the impacts of land conversion on water quality and rural transportation infrastructure. Hence, it raises the need for an LCLU dataset with good spatiotemporal coverage as well as consistent accuracy through time to enable change analysis. This dissertation aims (1) to develop a novel classification method, which utilizes time series images from comparable sensors, from the perspective of land surface phenology, and (2) to apply the land cover/land use dataset generated from the phenometrically-based classification approach to quantify crop expansion in South Dakota. A novel classification approach from the perspective of land surface phenology (LSP) uses rich time series datasets. First, surface reflectance products at 30 m spatial resolution from Landsat Collection-1, its newer structure—Landsat Analysis Ready Data, and the Harmonized Landsat Sentinel-2 (HLS) data are used to construct vegetation index time series, including the Enhanced Vegetation Index (EVI), and the 2-band EVI (EVI2), and various spectral variables (spectral band and normalized ratio composites). MODIS Level-3 Land Surface Temperature & Emissivity 8-day composite products at 1 km spatial resolution from both the Aqua and Terra satellites are used to compute accumulated growing degree-days (AGDD) time series. The EVI/EVI2 and AGDD time series are then fitted by two different land surface phenology models: the Convex Quadratic model and the Hybrid Piecewise Logistic Model. Suites of phenometrics are derived from the two LSP models and spectral variables and input to Random Forest Classifiers (RFC) to map land cover of sample areas in South Dakota. The results indicate that classifications using only phenometrics can accurately map major crops in the study area but show limited accuracy for non-vegetated land covers. RFC models using the combined spectralphenological variables can achieve higher accuracies than those using either spectral variables or phenometrics alone, especially for the barren/developed class. Among all sampling designs, the “same distribution” models—proportional distribution of the sample is like proportional distribution of the population—tends to yield best land cover prediction. A “same distribution” random sample dataset covering approximately 0.25% or more of the study area appears to achieve an accurate land cover map. To characterize crop expansion in South Dakota, a trajectory-based analysis, which considers the entire land cover dataset generated from the LSP-based classifications, is proposed to improve change detection. An estimated cropland expansion of 5,447 km2 (equivalent to 14% of the existing cropland area) occurred between 2007 and 2015, which matches more closely the reports from the National Agriculture Statistics Service—NASS (5,921 km2) and the National Resources Inventory—NRI (5,034 km2) than an estimation from a bi-temporal change approach (8,018 km2). Cropland gains were mostly concentrated in 10 counties in northern and central South Dakota. An evaluation of land suitability for crops using the Soil Survey Geographic Database—SSURGO indicates a scarcity in high-quality arable land available for cropland expansion

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology:A case study in Iraq

Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R2 = 0.70 compared to the date of MODIS EVI (Avg R2 = 0.68) and a NPP (Avg R2 = 0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from − 20 to 20%, − 45 to 28% and − 48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach

Analyzing Vegetation Trends with Sensor Data from Earth Observation Satellites

Abstract This thesis aims to advance the analysis of nonlinear trends in time series of vegetation data from Earth observation satellite sensors. This is accomplished by developing fast, efficient methods suitable for large volumes of data. A set of methods, tools, and a framework are developed and verified using data from regions containing vegetation change hotspots. First, a polynomial-fitting scheme is tested and applied to annual Global Inventory Modeling and Mapping Studies (GIMMS)–Normalized Difference Vegetation Index (NDVI) observations for North Africa for the period 1982–2006. Changes in annual observations are divided between linear and nonlinear (cubic, quadratic, and concealed) trend behaviors. A concealed trend is a nonlinear change which does not result in a net change in the amount of vegetation over the period. Second, a systematic comparison between parametric and non-parametric techniques for analyzing trends in annual GIMMS-NDVI data is performed at fifteen sites (located in Africa, Spain, Italy, and Iraq) to compare how trend type and departure from normality assumptions affect each method’s accuracy in detecting long-term change. Third, a user-friendly program (Detecting Breakpoints and Estimating Segments in Trend, DBEST) has been developed which generalizes vegetation trends to main features, and characterizes vegetation trend changes. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST is tested and evaluated using both simulated NDVI data, and actual NDVI time series for Iraq for the period 1982-2006. Finally, a decision-making framework is presented to help analysts perform comprehensive analyses of trend/change in time series of satellite sensor data. The framework is based on a conceptual model of the main aspects of trend analyses, including identification of the research question, the required data, the appropriate variables, and selection of efficient analysis methods. To verify the framework, it is applied to four case studies (located in Burkina Faso, Spain, Sweden, and Senegal) using Moderate-resolution Imaging Spectroradiometer (MODIS)–NDVI data for the period 2000–2013. Each of the case studies successfully achieved its research aim(s), showing that the framework can achieve the main goal of the study which is to advance the analysis of nonlinear changes in vegetation. The methods developed in this thesis can help to contribute more accurate information about vegetation dynamics to the field of land cover change research

Classification of Satellite Time Series-derived Land Surface Phenology Focused on the Northern Fertile Crescent

Land surface phenology describes events in a seasonal vegetation cycle and can be used in a variety of applications from predicting onset of future drought conditions, to revealing potential limits of historical dry farming, to guiding more accurate dating of archeological sites. Traditional methods of monitoring vegetation phenology use data collected in situ. However, vegetation health indices derived from satellite remote sensor data, such as the normalized difference vegetation index (NDVI), have been used as proxy for vegetation phenology due to their repeated acquisition and broad area coverage. Land surface phenology is accessible in the NDVI satellite record when images are processed to be intercomparable over time and temporally ordered to create a time series. This study utilized NDVI time series to classify areas of similar vegetation phenology in the northern Fertile Crescent, an area from the middle Mediterranean coast to southern/south-eastern Turkey to western Iran and northern Iraq. Phenological monitoring of the northern Fertile Crescent is critical due to the area\u27s minimal water resources, susceptibility to drought, and understanding ancient historical reliance on precipitation for subsistence dry farming. Delineation of phenological classes provides areal and temporal synopsis of vegetation productivity time series. Phenological classes were developed from NDVI time series calculated from NOAA\u27s Advanced Very High Resolution Radiometer (AVHRR) imagery with 8 × 8 km spatial resolution over twenty-five years, and by NASA\u27s Moderate Resolution Imaging Spectroradiometer (MODIS) with 250 × 250 m spatial resolution over twelve years. Both AVHRR and MODIS time series were subjected to data reduction techniques in spatial and temporal dimensions. Optimized ISODATA clusters were developed for both of these data reduction techniques in order to compare the effects of spatial versus temporal aggregation. Within the northern Fertile Crescent study area, the spatial reduction technique showed increased cluster cohesion over the temporal reduction method. The latter technique showed an increase in temporal smoothing over the spatial reduction technique. Each technique has advantages depending on the desired spatial or temporal granularity. Additional work is required to determine optimal scale size for the spatial data reduction technique

Polynomial trends of vegetation phenology in Sahelian to equatorial Africa using remotely sensed time series from 1983 to 2005

Popular science Our understanding of global warming can be achieved in different ways. One way is to study the phenological parameters of vegetation. Phenology or seasonality of vegetation can be identified from several parameters such as: the start of the growing season (SOS), end of the growing season (EOS), amplitude of the season (AMP), and length of the growing season (LOS). Changes of these parameters represent the cyclic changes of vegetation. Nowadays, imagery satellite data are reliable and widely-used sources to study the vegetation changes. Phenology parameters are derived from time series of vegetation indices (VI) that can be computed from satellite imagery. In this thesis, long-term dataset of GIMMS NDVI from 1983 to 2005 was used to extract and analyze vegetation phenology over Sahelian to equatorial areas. The TIMESAT software package was also used as an automated method to extract the parameters. Recent researches have shown the changes via analyzing the linear trends of the vegetation indices or lately through studying the linear trend of phenological parameters. Since changes of vegetation are not always simply linear, the overall aim of this thesis was to study vegetation changes through analysis of non-linear trends and more complex mathematical functions of phenology parameters, and via finding the relationship between the phenology parameters and soil moisture. Driving forces behind changes in phenology parameters including land cover, soil texture and rainfall were also taken in to consideration. The results illustrated that non-linear trends can detect notable proportions of vegetation changes in the study area. Not only significant portions of areas with linear trends could be represented using non-linear trends, but also these trends increased the precision of phenology change detection. Regarding the climate driver forces results showed that the vegetation phenology changes followed soil moisture variations. However the trends of vegetation changes has not especially followed land cover, soil texture and geographic characteristics although in some limited cases these driver forces are related to the changes.Global warming has both short and long term effects on seasonal phenological cycles of vegetation. Phenology parameters of vegetation such as start, end, length and amplitude of season can describe life cycle events of vegetation. In this thesis, long-term dataset of GIMMS NDVI time series from 1983 to 2005 was used to extract and analyze vegetation phenology over Sahelian to equatorial areas and TIMESAT software package was used as an automated method to extract the parameters. The overall aim of this thesis was to study vegetation changes through analysis of polynomial trends of phenology parameters. Phenology parameters were analyzed to detect hidden changes in vegetation dynamics. Through comparing polynomial trends of vegetation parameters and soil moisture, the relationship between the phenology parameters and soil moisture was detected and the role of climate driver forces (including land cover, soil texture and rainfall) behind the changes in phenology parameters were investigated. The results illustrated that polynomial trends can detect notable proportions of vegetation changes in the Sahel using remotely sensed data. Significant portions of areas with linear trends could be represented through quadratic and cubic trends, and these trends increased the precision of phenology change detection. Furthermore, in some areas vegetation changes were not detected neither through linear regressions nor polynomial trends. In such areas, polynomial hidden trends could be applied for detecting the fluctuations of vegetation parameters. In summation, applying polynomial trend analysis to time-series of satellite data is a powerful tool for investigating trends and variations in vegetation in semi-arid to sub-humid regions, like the Sahel. Regarding the climate driver forces, results showed that the vegetation phenology changes followed soil moisture variations, and in most occurrences, moderate correlations were found between SOS, EOS, and soil moisture. The trends of vegetation changes did not spatially follow land cover and soil types of the study area. However, in some limited cases, land cover, soil texture and geographic characteristics such as elevation were related to the changes