A new station-enabled multi-sensor integrated index for drought monitoring

Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available

Examining Ecosystem Drought Responses Using Remote Sensing and Flux Tower Observations

Indiana University-Purdue University Indianapolis (IUPUI)Water is fundamental for plant growth, and vegetation response to water availability influences water, carbon, and energy exchanges between land and atmosphere. Vegetation plays the most active role in water and carbon cycle of various ecosystems. Therefore, comprehensive evaluation of drought impact on vegetation productivity will play a critical role for better understanding the global water cycle under future climate conditions. In-situ meteorological measurements and the eddy covariance flux tower network, which provide meteorological data, and estimates of ecosystem productivity and respiration are remarkable tools to assess the impacts of drought on ecosystem carbon and water cycles. In regions with limited in-situ observations, remote sensing can be a very useful tool to monitor ecosystem drought status since it provides continuous observations of relevant variables linked to ecosystem function and the hydrologic cycle. However, the detailed understanding of ecosystem responses to drought is still lacking and it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. This dissertation addressed drought monitoring, ecosystem drought responses, trends of vegetation water constraint based on in-situ metrological observations, flux tower and multi-sensor remote sensing observations. This dissertation first developed a new integrated drought index applicable across diverse climate regions based on in-situ meteorological observations and multi-sensor remote sensing data, and another integrated drought index applicable across diverse climate regions only based on multi-sensor remote sensing data. The dissertation also evaluated the applicability of new satellite dataset (e.g., solar induced fluorescence, SIF) for responding to meteorological drought. Results show that satellite SIF data could have the potential to reflect meteorological drought, but the application should be limited to dry regions. The work in this dissertation also accessed changes in water constraint on global vegetation productivity, and quantified different drought dimensions on ecosystem productivity and respiration. Results indicate that a significant increase in vegetation water constraint over the last 30 years. The results highlighted the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate

The sensitivity of satellite solar‐induced chlorophyll fluorescence (SIF) to meteorological drought

Solar‐induced chlorophyll fluorescence (SIF) could provide information on plant physiological response to water stress (e.g., drought). There are growing interests to study the effect of drought on SIF. However, to what extent SIF responds to drought and how the responses vary under different precipitation, temperature and potential evapotranspiration conditions are not clear. In this regard, we evaluated the relationship between satellite‐based SIF product and four commonly used meteorological drought indices (Standardized Precipitation‐Evapotranspiration Index, SPEI; Standardized Precipitation Index, SPI; Temperature Condition Index, TCI; and Palmer Drought Severity Index, PDSI) under diverse climate regions in the continental United States. The four drought indices were used because they estimate meteorological drought conditions from either single or combined meteorological factors such as precipitation, temperature, and potential evapotranspiration, representing different perspectives of drought. The relationship between SIF and meteorological drought varied spatially and differed for different ecosystem types. The high sensitivity occurred in dry areas characterized by a high mean annual growing season temperature and low vegetation productivity. Through random forest regression analyses, we found that temperature, gross primary production, precipitation, and land cover are the major factors affecting the relationships between SIF and meteorological drought indices. Taken together, satellite SIF is highly sensitive to meteorological drought but the high sensitivity is constrained to dry regions

Assessing and Mapping the Spatial-Temporal Change in Forest Phenology of Arabuko-Sokoke Forest using Moderate Resolution Satellite

This study focuses on coastal forests in Kenya that have some of the highest variety of flora and fauna, specifically Arabuko Sokoke Forest. Arabuko Sokoke Forest is located 110 miles north of Mombasa and 18 kilometers south of Malindi. This forest is known to be a worldwide biodiversity hotspot that is home to endemic and rare plants and animals. Within the Arabuko Sokoke Forest ecosystem, there are two main issues that challenge the conservation of the area. First, there has been more competition for land, primarily for agriculture and development. Second, there is an increase demand for forest resources due to the rise in population. Therefore, this study aims to assess the spatial-temporal change in forest phenology over time around Arabuko Sokoke from 2003-2020 using the remotely sensed Vegetation Condition Index (VCI) and ground information. The specific objectives are to map spatial-temporal changes in forest phenology over the last 18 years of Arabuko Sokoke Forest; and to analyze the change of vegetation condition in combination with ground experiences to understand pressure and drivers of changes in order to develop future policy actions to minimize the deterioration. Based on the problems posed in Arabuko Sokoke Forest, I hypothesized there will be an immense change in vegetation phenology in the last 18 years driven mostly by climatic changes in addition to anthropogenic pressures. MODIS images were used to map the environmental changes over 5 distinct seasons: annual, long dry season (DJFM), long rainy season (AMJJ), short dry season (AS) and short rainy season (ON) from 2003- 2020. The vegetation condition index (VCI) was mapped in QGIS to visually represent the trends of each season over 18 years. Many of the years were consistent in trends in terms of vegetation health. The years 2007, 2016, 2017, and 2020, there were significant VCI trends with either low or high VCI grades, different than the other years. 2016 and 2017 in across all 5 seasons appeared to have low VCI grades. However, 2007 was found to have high VCI grades in multiple seasons. Although there may have been anthropogenic impacts at play, it was found that temperature had the largest influence in terms of changing the VCI over time. Future research poses that there should be an investigation of the exact causes of these VCI trends that were mapped. For instance, were they influenced by anthropogenic impacts, limited precipitation, poor policies, climate change, the influence of disease and how biodiversity would be impacted based on these drivers

Can Famine Be Averted? A Spatiotemporal Assessment of The Impact of Climate Change on Food Security in The Luvuvhu River Catchment of South Africa

Climate change has proved to be a threat to food security the world over. Using temperature and precipitation data, this paper examines the differential effects climate change has on different land uses in the Luvuvhu river catchment in South Africa. The paper uses the Normalised Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI), which were calculated from Landsat images, and the Standardised Precipitation Index (SPI) for a sample of years between 1980 and 2016 to assess how drought and flood frequency have affected the agricultural environment. The results indicate that the lowest SPI values were recorded in 1996/1997, 2001/2002 and 2014/2015, suggesting the occurrence of drought during these years, while the highest SPI values were recorded in 1997/1998, 2002/2003 and 2004/2005. The relationship between three-month SPI (SPI_3) and VCI was strongest in grassland, and subsistence farming areas with the correlation coefficients of 0.8166 (p = 0.0022) and −0.6172 (p = 0.0431), respectively, indicating that rainfall variability had a high negative impact on vegetation health in those land uses with shallow-rooted plants. The findings of this study are relevant to disaster management planning in South Africa, as well as development of farming response strategies for coping with climate hazards in the country.</jats:p

Indicator-to-impact links to help improve agricultural drought preparedness in Thailand

Droughts in Thailand are becoming more severe due to climate change. Developing a reliable Drought Monitoring and Early Warning System (DMEWS) is essential to strengthen a country&rsquo;s resilience to droughts. However, for a DMEWS to be valuable, the drought indicators it provides stakeholders must have relevance to tangible impacts on the ground. Here, we analyse drought indicator-to-impact relationships in Thailand, using a combination of correlation analysis and machine learning techniques (random forest). In the correlation analysis, we study the link between meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for crop-yield and forest-growth impacts. Our analysis shows that this link varies depending on land use, season, and region. The random forest models built to estimate regional crop productivity allow a more in-depth analysis of the crop-/region-specific importance of different drought indicators. The results highlight seasonal patterns of drought vulnerability for individual crops, usually linked to their growing season, although the effects are somewhat attenuated in irrigated regions. Integration of the approaches provides new detailed knowledge of crop-/region-specific indicator-to-impact links, which can form the basis of targeted mitigation actions in an improved DMEWS in Thailand, and could be applied in other parts of Southeast Asia and beyond.</p

Investigating the relationships between remotely sensed and in situ drought indicators to understand streamflow discharge anomalies

Master of ScienceDepartment of Biological & Agricultural EngineeringVahid RahmaniPredicting drought and streamflow are important aspects of water management to help mitigate the effects that a drought has on the environment and the people involved. The goals of this research are to assess remote sensing indicators and their ability to monitor drought and streamflow changes compared to in situ indicators in order to better estimate streamflow changes in times of drought in areas and at times without station-based data. Using in situ drought indicators such as station-based Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) helps water managers identify drought severity based on various environmental factors measured on a regular basis using station-based data. Remote sensing indicators on the other hand use satellite inputs to monitor such trends as vegetation coverage at a higher spatial resolution than an in situ index would. Remote sensing information is more readily available than most observed environmental information across the globe. The study region is located in central United States in the MINK (Missouri, Iowa, Nebraska, and Kansas) region. Data for the growing period (April-September) from 2003-2017 were used. The region varies greatly from east to west in both land cover and average precipitation amount (318 – 1397 mm per year) as the region becomes drier and changes from forests in the southeast to farmland and prairie in the west. The first part of the study evaluated various drought indices and their relationships with streamflow. The in situ indices evaluated include SPI and PDSI, which were available on a monthly basis for each climate division in the MINK region. The remote sensing indices include the Vegetation Condition Index (VCI) and the Soil Moisture Condition Index (SMCI). Each index has different data inputs, such a precipitation (PDSI and SPI) and temperature (PDSI) for the in situ indices and vegetation greenness (VCI) and soil moisture (SMCI) for the remote sensing indices. The indices were ultimately compared both spatially and temporally (annual basis) to streamflow in the form of discharge anomalies (PDA). In the second part of this study, analysis focused on how relationships between the remote sensing indices changed as land cover varies over the MINK region. Overall, results suggest that the in situ indices (PDSI and SPI) can estimate PDA changes (on an annual scale), while SMCI performed better than VCI overall though not as well as PDSI or SPI. The findings from this study have the potential to assist water managers and policy makers to better understand streamflow changes and increase drought preparedness

The use of machine learning algorithms to assess the impacts of droughts on commercial forests in KwaZulu-Natal, South Africa.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Droughts are a non-selective natural disaster in that their occurrence can be in both high and low precipitation areas. However, this study acknowledged that droughts are more recurrent and a regular feature in arid and semi-arid climates such as that of Southern Africa. Some of these countries rely strongly on commercial forests for their gross domestic product (GDP), especially South Africa and Mozambique which means droughts pose a significant threat to their economy and the society that depends on this economy. The risks associated with droughts have consequently created an increased demand for an efficient method of analysing and investigating droughts and the impacts they impose on forest vegetation. Therefore, this study aimed to examine the effects of droughts on all commercial forests within the province of KwaZulu-Natal (KZN) at a catchment and provincial scale by employing Kernel Support Vector Machine (Kernel –SVM), Rotation Forests (RTF) and Extreme Gradient Boosting (XGBoost) algorithms. These were based on Landsat and MODIS derived vegetation and conditional drought indices. The main aim of this study was achieved by the following objectives: (i) to improve methods for classifying droughts; (ii) to achieve medium spatial resolution drought analysis using Landsat sensors; (iii) to determine the accuracy of machine learning algorithms (MLAs) when employed on remote sensing data and (iv) to improve the usability of conditional drought indices and vegetation indices. The results obtained there-after demonstrated that the objectives of this study were met. With the MLAs performing better when using conditional drought indices compared to vegetation indices, therefore, highlighting drawbacks already associated with vegetation indices. Where at the catchment scale, Kernel – support vector machine (SVM) produced an overall accuracy (OA) of 94.44% when based on conditional drought indices compared to 81.48% when based on vegetation indices. On the same scale, Rotation forests (RTF) produced 96.30% and 81.84% when using conditional drought indices and vegetation indices, respectively. At a provincial scale, RTF produced an OA of 76.6% and 70.7% when using conditional drought indices and vegetation indices respectively. This was compared to extreme gradient boosting (XGBoost) which produced an OA of 81.9% and 69.3% when using conditional drought indices and vegetation indices respectively. These results also indicate that it is possible to analyse droughts at provincial and catchment scale. Although the results presented in this study were promising, more research is still required to improve the applicability of MLAs in drought analysis.Dedication is listed on page iii

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

Drought forecasts using satellite data based on deep learning over East Asia

Department of Urban and Environmental Engineering (Environmental Science and Engineering)This thesis/dissertation seeks to 1) forecast drought conditions effectively considering temporal patterns of drought indices and upcoming weather conditions through the deep learning approach, and 2) forecast drought by identifying the teleconnection effect based on the sea surface temperature through the deep learning approach. In this thesis/dissertation, there are four chapters. Chapter 1 summarizes the background of the research and overviews of the thesis research. In Chapter 2, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results. Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05??). In Chapter 3, the Drought forecasting model on a mid-and long-term scale (one-three lead time) over East Asia was developed using temporal patterns of drought indices and teleconnection phenomena of SST through the CNN. Reanalysis based drought index, SPI, were selected with a mid- and long-timescale (one to three months), and satellite-based variable, precipitation and SST across the Pacific Ocean. As the lead time increased, the accuracy tended to fall, but it showed good results compared to CFS. When compared to a drought case, the SST of 8 months ago influenced on the results. Chapter 4 provides a brief summary of these studiesclos