Dual Scale Trend Analysis Distinguishes Climatic from Anthropogenic Effects on the Vegetated Land Surface

We present a dual scale trend analysis for characterizing and comparing two contrasting areas of change in Russia and Kazakhstan that lie less than 800 km apart. We selected a global NASA MODIS (moderate resolution imaging spectroradiometer) product (MCD43C4 and MCD43A4) at a 0.05◦ (∼5.6 km) and 500 m spatial resolution and a 16-day temporal resolution from 2000 to 2008. We applied a refinement of the seasonal Kendall trend method to the normalized difference vegetation index (NDVI) image series at both scales. We only incorporated composites during the vegetative growing season which was delineated by start of season and end of season estimates based on analysis of normalized difference infrared index data. Trend patterns on two scales pointed to drought as the proximal cause of significant declines in NDVI in Kazakhstan. In contrast, the area of increasing NDVI trend in Russia was linked through the dual scale analysis with agricultural land cover change. The coarser scale analysis was relevant to atmospheric boundary layer processes, while the finer scale data revealed trends that were more relevant to human decision-making and regional economics

Vegetation drought monitoring from MODIS imagery and soil moisture data in Oklahoma Mesonet sites

Drought is a normal and recurrent climatic phenomenon, and is considered one of the most costly natural disasters in the United States. Grassland vegetation is sensitive to weather and climate, and persistent drought impacts goods and ecological services that grasslands provide (e.g., wildlife habitats, feedstock for the livestock industry, and recreational services). Droughts have extremely large spatial and temporal variations in areal coverage and intensity making drought monitoring a challenging task. Using soil and atmospheric data from the Oklahoma Mesonet and surface reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, this study examined the hypothesis that the satellite-derived Land Surface Water Index (LSWI) is sensitive to drought conditions and can potentially be used as an indicator or tool for drought monitoring. The sensitivity of LSWI to summer drought was first analyzed at 10 Mesonet sites that are homogeneous and representative of different types of grassland vegetation, soils and climate across Oklahoma. A summer drought event is defined, based on threshold values of LSWI and the Fractional Water Index (FWI) derived from soil moisture data at each site.Secondly, the LSWI-based drought algorithm was evaluated at103 Oklahoma Mesonet sites. Finally, the LSWI-based droughtalgorithm was used to map spatial patterns and temporal dynamicsof drought-affected land surface during 2001-2010 acrossOklahoma. The results from this study demonstrated the potentialof LSWI-based drought algorithm for tracking and mappingdrought-affected grassland vegetation in Oklahoma with 3%commission error in the Oklahoma Mesonet sites during 2001-2010.La sequía es un fenómeno climático normal y recurrente, y es considerado uno de los desastres naturales más costosos en Estados Unidos. La vegetación de pastizales es sensible al estado del tiempo y el clima, y la persistencia de la sequía afecta a los bienes y servicios ecológicos que proporcionan los pastizales (por ejemplo, son hábitats de vida silvestre, proveen materia prima para la industria ganadera, así como servicios de esparcimiento). La cobertura de área e intensidad de las sequías presentan grandes variaciones espaciales y temporales, haciendo que el monitorea de sequías sea una tarea difícil. Usando datos atmosféricos y de suelos de la Oklahoma Mesonet, y datos de reflectancia de la superficie terrestre del espectrorradiómetro de imágenes de resolución moderada (MODIS, por sus siglas en inglés) a bordo de los satélites Terra y Aqua, este estudio examinó la hipótesis de que el índice de agua de la superficie del terreno (LSWI, por sus siglas en inglés) es sensible a condiciones de sequía y potencialmente puede utilizarse como un indicador o herramienta para la monitoreo de sequías. La sensibilidad del LSWI a la sequía estival se analizó inicialmente en 10 sitios Mesonet que son homogéneos y representativos de los diferentes tipos de vegetación de pastizales, los suelos y el clima a través de Oklahoma. Un evento de sequía estival se define, en base a los valores de umbral de LSWI y el Índice de Agua fraccional (FWI) derivado de los datos de humedad del suelo en cada sitio. Posteriormente, el algoritmo de sequía basado en LSWI se evaluó en 103 sitios Oklahoma Mesonet. Por último, se utilizó el algoritmo de sequía basado en LSWI para mapear los patrones espaciales y la dinámica temporal de la superficie de la tierra afectada por la sequía durante 2001-2010 a través de Oklahoma. Los resultados de este estudio demostraron el potencial del algoritmo de sequía basado en LSWI para el seguimiento y la cartografía de vegetación de pradera afectada por la sequía en Oklahoma con un 3% de error de comisión en los sitios Oklahoma Mesonet durante 2001-201

Water Resource Variability and Climate Change

Climate change affects global and regional water cycling, as well as surficial and subsurface water availability. These changes have increased the vulnerabilities of ecosystems and of human society. Understanding how climate change has affected water resource variability in the past and how climate change is leading to rapid changes in contemporary systems is of critical importance for sustainable development in different parts of the world. This Special Issue focuses on “Water Resource Variability and Climate Change” and aims to present a collection of articles addressing various aspects of water resource variability as well as how such variabilities are affected by changing climates. Potential topics include the reconstruction of historic moisture fluctuations, based on various proxies (such as tree rings, sediment cores, and landform features), the empirical monitoring of water variability based on field survey and remote sensing techniques, and the projection of future water cycling using numerical model simulations

ECOSYSTEM RESPONSES TO CLIMATE VARIABILITY AND MANAGEMENT PRACTICES: DROUGHT ASSESSMENT (REMOTE SENSING), FIELD MEASUREMENTS (EDDY COVARIANCE) AND MODELING (DNDC)

Climate variability and management practices in isolation or in combination influence the properties of ecosystems and the flows of energy and materials through them. The goal of this dissertation is to better understand the ecosystem responses to climatic variability and management practices using different approaches such as remote sensing, eddy covariance techniques and modelling. Remote sensing indices were tested and evaluated for developing better drought monitoring. Specifically, water related vegetation index (LSWI) was employed to assess the ecosystem responses to the drought events occurred in Oklahoma from 2000-2013. Field measurements data in combination with the EC system were used to understand how the sink-source potential of the ecosystem changes when grassland ecosystem is converted to winter wheat. DeNitrification- DeComposition (DNDC) model was used to analyze greenhouse gas emissions from pasture land amended with fertilizers compared to the native pastures in the scenario of climatic variability. We used 14 years of MODIS, Mesonet soil moisture and rainfall data at Marena and El Reno tallgrass prairie sites to study the impact of drought events on grassland phenology and growth through analyzing sensitivity differences of vegetation indices to drought. A new approach of drought assessment, counting number of days with LSWI < 0 and LSWI-based drought severity classification, is proposed in this study. The number of days with LSWI < 0 was found higher during the summer droughts of 2006 and 2012 and negative LSWI represented the higher intensity drought categories (D2, D3 and D4) defined by USDM, which demonstrated that it could be used to describe the hydrological condition of the ecosystem as an effective additional vegetation based indicator for drought assessment. This study also investigates the potential of the LSWI-based algorithm, for agricultural drought monitoring under varying soil and land cover conditions of 113 Mesonet stations of Oklahoma. We compared LSWI and the number of days with negative LSWI (DNLSWI) to summer time precipitation, precipitation anomalies, and the U.S. Drought Monitor. Additionally, the assessment of the algorithm with USDM was performed separately for different land cover type and climate divisions. Therefore, results from this study will help in improving the capability of remote sensing vegetation drought monitoring by establishing LSWI as a complimentary tool to existing NDVI based drought products as well as help to identify the sensitivity of LSWI to the diversity of the ecosystems of Oklahoma. We quantified and compared the carbon and water fluxes from winter wheat and tallgrass prairie ecosystems and discussed the possibility of change in carbon and water budgets of the southern plains under the land use change scenario (conversion of grassland into winter wheat). Both ecosystems were sinks of carbon during their respective growing seasons. At the annual scale, the wheat ecosystem was a net source of carbon (128 ± 46 g C m-2 yr-1) when fluxes from summer fallow period were considered. Results suggest that the differences in magnitudes and patterns of CO2 and H2O fluxes between winter wheat and tallgrass prairie ecosystems can exert an influence on the carbon and water budgets of the whole region under land use change scenario. Another hypothesis tested in our study was that the application of fertilizers in the managed pasture would increase the primary productivity of the ecosystem for few years but this increase in carbon sink would be counteracted by the increasing rate of greenhouse gas emissions in the long run. Here we used DNDC, a process-based model that simulates the emissions and consumption of gases within the ecosystem based on the interactions of local climate, local soils and on-site management practices. The fertilization of pasture increased the productivity that increased the roughages demands resulted by increased stocking density of cattle. Similarly, higher flux of N2O from the managed pasture was resulted as the effect of fertilizer addition which amplified in magnitude in wet years than dry and normal years. The advantage from increased soil organic carbon due to the fertilizer application, measured in terms of global warming potential (GWP) was outweighed by the GWP calculated from the increased magnitude of N2O fluxes thereby giving the positive net global warming potential (NGWP). Therefore, pasture management policies should consider maintaining emissions level as minimum as possible while optimizing the productivity

Doctor of Philosophy

dissertationUnderstanding the spatially and temporally variant phenological responses and cycles can greatly assist the administrative planning, policy making and management in grazing, planting, and ecosystem conservation. The linkages of analysis as a basis for management have received increasing attention in the context of climate change. This research focuses on analyzing phenological responses of vegetation as constrained and moderated by environmental factors, such as landscape and season, in the geographically diverse Upper Colorado River Basin (UCRB). Due to the geographic diversity of phenological forcing in the UCRB, several homogeneous phenological subregions (phenoregions) are delineated, and the phenological responses of vegetation are analyzed on a per phenoregion basis. A multivariate adaptive regression splines (MARS) approach is adopted to model and interpret the regionally and seasonally specific relationships between environmental drivers (temperature, precipitation and solar radiant energy) and vegetation abundance, indicated by a Vegetation Index (VI). Short-term predictions of vegetation abundance are made using the models. Taking into consideration the scale of the study area and the time-step of the models, 1 km 7-day interval eMODIS data and the 1 km NASA AMES Ecocast data are used to articulate the dependent and independent variables. The series of models are integrated into a prototype phenological Decision Support System (DSS) to provide predicted vegetation abundance over the growing season and the trends of climatic variables leading to potential grazing management strategies. The implementation of the DSS is a unique attempt to integrate phenological theory and GIS technology, the combination of which makes this DSS analytically-based, intuitive and more user-friendly

Landsat-based operational wheat area estimation model for Punjab, Pakistan

Wheat in Punjab province of Pakistan is grown during the Rabi (winter) season within a heterogeneous smallholder agricultural system subject to a range of pressures including water scarcity, climate change and variability, and management practices. Punjab is the breadbasket of Pakistan, representing over 70% of national wheat production. Timely estimation of cultivated wheat area can serve to inform decision-making in managing harvests with regard to markets and food security. The current wheat area and yield reporting system, operated by the Punjab Crop Reporting Service (CRS) delivers crop forecasts several months after harvest. The delayed production data cannot contribute to in-season decision support systems. There is a need for an alternative cost-effective, efficient and timely approach on producing wheat area estimates, in ensuring food security for the millions of people in Pakistan. Landsat data, medium spatial and temporal resolutions, offer a data source for characterizing wheat in smallholder agriculture landscapes. This dissertation presents methods for operational mapping of wheat cultivate area using within growing season Landsat time-series data. In addition to maps of wheat cover in Punjab, probability-based samples of in-situ reference data were allocated using the map as a stratifier. A two-stage probability based cluster field sample was used to estimate area and assess map accuracies. The before-harvest wheat area estimates from field-based sampling and Landsat map were found to be comparable to official post-harvest data produced by the CRS Punjab. This research concluded that Landsat medium resolution data has sufficient spatial and temporal coverage for successful wall-to-wall mapping of wheat in Punjab’s smallholder agricultural system. Freely available coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems; commercial high spatial resolution satellite data are often advocated as an alternative for characterizing fine-scale land tenure agricultural systems such as that found in Punjab. Commercial 5 m spatial resolution RapidEye data from the peak of the winter wheat growing season were used as sub-pixel training data in mapping wheat with the growing season free 30 m Landsat time series data from the 2014-15 growing season. The use of RapidEye to calibrate mapping algorithms did not produce significantly higher overall accuracies ( ± standard error) compared to traditional whole pixel training of Landsat-based 30 m data. Continuous wheat mapping yielded an overall accuracy of 88% (SE = ±4%) in comparison to 87% (SE = ±4%) for categorical wheat mapping, leading to the finding that sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other similar landscapes, training data for supervised classification may be collected directly from Landsat images with probability based stratified random sampling as reference data without the need for high-resolution reference imagery. The research concluded by exploring the use of automated models in wheat area mapping and area estimation using growing season Landsat time-series data. The automated classification tree model resulted in wheat / not wheat maps with comparable accuracies compared to results achieved with traditional manual training. In estimating area, automated wheat maps from previous growing seasons can serve as a stratifier in the allocation of current season in-situ reference data, and current growing season maps can serve as an auxiliary variable in model-assisted area estimation procedures. The research demonstrated operational implementation of robust automated mapping in generating timely, accurate, and precise wheat area estimates. Such information is a critical input to policy decisions, and can help to ensure appropriate post-harvest grain management to address situations arising from surpluses or shortages in crop production

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

Reconstruction of a Long-term spatially Contiguous Solar-Induced Fluorescence (LCSIF) over 1982-2022

Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for diagnosing the photosynthetic characteristics of terrestrial ecosystems. Despite the increasing spatial and temporal resolutions of these satellite retrievals, records of SIF are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function and structure. In this study, we leverage the two surface reflectance bands (red and near-infrared) available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2022) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2022). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data, a neural network is then built to reproduce the Orbiting Carbon Observatory-2 SIF using AVHRR and MODIS, and used to map SIF globally over the entire 1982-2022 period. Compared with the previous MODIS-based CSIF product relying on four reflectance bands, our two-band-based product has similar skill but can be advantageously extended to the bias-corrected AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv; all based empirically on red and near-infrared bands) shows a higher or comparable correlation of LCSIF with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity of LCSIF for representing terrestrial photosynthesis. Globally, LCSIF-AVHRR shows an accelerating upward trend since 1982, with an average rate of 0.0025 mW m-2 nm-1 sr-1 per decade during 1982-2000 and 0.0038 mW m-2 nm-1 sr-1 per decade during 2001-2022. Our LCSIF data provide opportunities to better understand the long-term dynamics of ecosystem photosynthesis and their underlying driving processes

A high-resolution spatial assessment of the impacts of drought variability on vegetation activity in Spain from 1981 to 2015

59 Pags.- 12 Tabls.- 35 Figs. © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.Drought is a major driver of vegetation activity in Spain, with significant impacts on crop yield, forest growth, and the occurrence of forest fires. Nonetheless, the sensitivity of vegetation to drought conditions differs largely amongst vegetation types and climates. We used a high-resolution (1.1 km) spatial dataset of the normalized difference vegetation index (NDVI) for the whole of Spain spanning the period from 1981 to 2015, combined with a dataset of the standardized precipitation evapotranspiration index (SPEI) to assess the sensitivity of vegetation types to drought across Spain. Specifically, this study explores the drought timescales at which vegetation activity shows its highest response to drought severity at different moments of the year. Results demonstrate that – over large areas of Spain – vegetation activity is controlled largely by the interannual variability of drought. More than 90 % of the land areas exhibited statistically significant positive correlations between the NDVI and the SPEI during dry summers (JJA). Nevertheless, there are some considerable spatio-temporal variations, which can be linked to differences in land cover and aridity conditions. In comparison to other climatic regions across Spain, results indicate that vegetation types located in arid regions showed the strongest response to drought. Importantly, this study stresses that the timescale at which drought is assessed is a dominant factor in understanding the different responses of vegetation activity to drought.This research has been supported by the Spanish Commission of Science and Technology and FEDER (grant no. PCIN-2015-220), the Spanish Commission of Science and Technology and FEDER (grant no. CGL2014-52135-C03-01), the Spanish Commission of Science and Technology and FEDER (grant no. CGL2017-83866-C3-3-R), the Spanish Commission of Science and Technology and FEDER (grant no. CGL2017-82216-R), WaterWorks 2014 (grant no. 690462, IMDROFLOOD), the JPI Climate (grant no. 690462, INDECIS), and WaterWorks 2015 (FORWARD grant).Peer reviewe