Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

HYDROLOGICAL MODELLING FOR THE PREVENTION AND THE MANAGEMENT OF WATER SHORTAGE IN AGRICULTURE

In recent decades, frequent and severe droughts have occurred in several countries of the world under nearly all climatic regimes. Since the middle 20th century, drought areas have globally increased, and, more specifically, in southern and central Europe. Drought risk is expected to increase in the near future as a result of the climate change, leading to a decline in precipitation and an increase in air temperatures, and consequently in evapotranspiration rates in several regions, including southern Europe and the Mediterranean region. Droughts can significantly affect the agricultural sector since they provoke losses in crop yields and livestock production, increased insect infestations, plant diseases and wind erosion. Moreover, low rainfall during the growing season may affect irrigated agriculture over subsequent years, as a result of low levels of water in reservoirs and groundwater aquifers. In Europe, the monitoring and assessment of drought is entrusted to the European Drought Observatory (EDO), that applies a multi-indicator approach, based on earth observations (EOs) and hydrological modelling data. EDO indicators are computed considering rainfed agriculture, predominant in middle and northern Europe, and are produced on a 5 km grid. In southern Europe, however, the implementation of drought-coping measures (irrigation) can partially or completely alleviate the impacts of potentially severe droughts. Therefore, for these conditions, specific water scarcity indicators explicitly considering irrigation among the water inputs to agro-ecosystems need to be developed and adopted to inform and support stakeholders and decision makers of irrigated regions. In this context, the main objective of the Ph.D. thesis is the presentation of the Transpirative Deficit Index (TDI), a newly developed indicator for the monitoring and the management of Water Scarcity and Drought phenomena based on the use of hydrological modelling, applied at a spatial scale of interest for end-users (250 m grid) and suited for the assessment of water scarcity and drought in Italy as well as in other southern European countries. In particular, TDI was developed as a new module integrated into the spatially distributed hydrological model IdrAgra, and in the Ph.D. research it was tested over the Irrigation District of Media Pianura Bergamasca (IDMPB), considering a simulation period of 22 years (1993-2014) and subdividing the territory by means of a grid with cells of 250 m 7250 m. As a first step in the thesis, D TDI was described as an agricultural drought index focusing on overcoming the limitation of other approaches, not taking into account with sufficient detail land cover and soil properties. The D TDI is based on the calculation of the spatially distributed actual transpiration deficit, to determine the level of drought experienced by crops within the single model cells; thus, it can provide a much more accurate measure of agricultural drought at the irrigation district scale than the one that could be achieved through meteorological drought indices such as SPI or SPEI. The auto-correlation analysis of D-TDI showed to be positive with a persistence of 30 days for the two more widespread crops in the study area, maize and permanent grass. The analysis demonstrated also that soils characterized by a high available water content can more easily compensate dry spells. Finally, a positive significant correlation between D-TDI and SPI was observed for maize, with a persistence of 40 days, while no correlation was observed for permanent grass, probably related to cutting cycles, that could mask the relation between storage capacity and short-time variability of the meteorological conditions. Successively, a methodology to compute crop yield using moderate spatial and temporal resolution Earth Observation (Landsat) data was set. In particular, the developed procedure, based on the integration of the Available Photosynthetically Active Radiation over the growing season, showed that statistical inventories and satellite data can be integrated to produce annual spatially distributed estimates of cropland productivity, while site-specific observational field data can be used to validate the relationship between APAR and productivity for specific crops (i.e. maize in this Ph.D. research). A phenological parameter extraction algorithm was developed to derive key phenology stages for the maize crop. However, the results presented in the study showed two main weaknesses: (1) cloud cover and noise in the original Landsat dataset were not appropriately removed by the Whittaker algorithm, and (2) SOS (Start of Season) and EOS (End of Season) extracted from satellite data were underestimated for a discrete numbers of fields with respect to observed ground-truths, probably as a consequence of the method adopted for setting the thresholds. A crop specific light use efficiency (\u3b5_b^*) was estimated as the ratio between the average maize yield over the study period taken from Regional Statistic Inventory (Regional Authority and ISTAT), and the average APAR value calculated for the maize pixels over the same spatial extension and time period. The \u3b5b* estimated value fell within the range of the coefficients calibrated with other satellite-based algorithms. Finally, TDI was applied as a water scarcity index (WS-TDI), thus including water availability for irrigation within the inputs of the IdrAgra model. The behaviour of D-TDI and WS-TDI was compared over the same area, analysing their spatialized trend in response to varying meteorological conditions, and in particular considering drought events and dry spells. The two indices proved to be suitable to monitor agricultural drought and water scarcity over a territory, and helped in identifying drought and/or water scarcity prone sub-districts, as a function of crop, soil type and water availability. Both D-TDI and WS-TDI could therefore be used as operational indicators to produce periodic maps that could help farmers and irrigation district managers in coping with agricultural drought and water scarcity and, eventually, in setting up proper adaptation measures. In particular, in case of availability of real time meteorological data and water discharges at the main surface water diversions, the indicators may be adopted by an authority responsible for the monitoring of the state of agriculture (ERSAF or ARPA in the Lombardy region) to promptly inform (through newsletters or a web site) stakeholders on the agricultural drought/dry spells and water scarcity/shortages phenomena evolution. Additionally, the indicators may be adopted in climate change studies, allowing to visualize the evolution of drought and water scarcity phenomena over the territory, as a consequence of changes in meteorological forcing and in the availability of water by irrigation sources. Finally, they could be used as useful tools to support planning decisions on water resources allocation or action plans to reduce water consumptions in specific portions of the territory (e.g. conversion of irrigation methods, introduction of different crop species, etc.), also in view of an adaptation to the climate change. WS TDI maps over a pilot study area were statistically compared with the maize yield maps derived from EO data (Landsat dataset): an ensemble correlation analysis proved a positive correlation between the two variables

Earth Resources: A continuing bibliography with indexes

This bibliography lists 475 reports, articles and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Earth Resources: A continuing bibliography (issue 32)

This bibliography list 580 reports, articles and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

North American Vegetation Dynamics Observed with Multi-Resolution Satellite Data

North American vegetation has been discovered to be a net carbon sink, with atypical behavior of drawing down more carbon from the atmosphere during the past century. It has been suggested that the Northern Hemisphere will respond favorably to climate warming by enhancing productivity and reducing the impact of fossil fuel emissions into the atmosphere. Many investigations are currently underway to understand and identify mechanisms of storage so they might be actively managed to offset carbon emissions which have detrimental consequences to the functioning of ecosystems and human well being. This paper used a time series of satellite data from multiple sensors at multiple resolutions over the past thlrty years to identify and understand mechanisms of change to vegetation productivity throughout North America. We found that humans had a marked impact to vegetation growth in half of the six selected study regions which cover greater than two million km2. We found climatic influences of increasing temperatures, and longer growing seasons with reduced snow cover in the northern regions of North America with forest fire recovery in the Northern coniferous forests of Canada. The Mid-latitudes had more direct land cover changes induced by humans coupled with climatic influences such as severe drought and altered production strategies of rain-fed agriculture in the upper Midwest, expansion of irrigated agriculture in the lower Midwest, and insect outbreaks followed by subsequent logging in the upper Northeast. Vegetation growth over long time periods (20+ years) in North America appears to be associated with long term climate change but most of the marked changes appear to be associated with climate variability on decadal and shorter time scales along with direct human land cover conversions. Our results document regional land cover land use change and climatic influences that have altered continental scale vegetation dynamics in North America

The application of Earth Observation for mapping soil saturation and the extent and distribution of artificial drainage on Irish farms

Artificial drainage is required to make wet soils productive for farming. However, drainage may have unintended environmental consequences, for example, through increased nutrient loss to surface waters or increased flood risk. It can also have implications for greenhouse gas emissions. Accurate data on soil drainage properties could help mitigate the impact of these consequences. Unfortunately, few countries maintain detailed inventories of artificially-drained areas because of the costs involved in compiling such data. This is further confounded by often inadequate knowledge of drain location and function at farm level. Increasingly, Earth Observation (EO) data is being used map drained areas and detect buried drains. The current study is the first harmonised effort to map the location and extent of artificially-drained soils in Ireland using a suite of EO data and geocomputational techniques. To map artificially-drained areas, support vector machine (SVM) and random forest (RF) machine learning image classifications were implemented using Landsat 8 multispectral imagery and topographical data. The RF classifier achieved overall accuracy of 91% in a binary segmentation of artifically-drained and poorly-drained classes. Compared with an existing soil drainage map, the RF model indicated that ~44% of soils in the study area could be classed as “drained”. As well as spatial differences, temporal changes in drainage status where detected within a 3 hectare field, where drains installed in 2014 had an effect on grass production. Using the RF model, the area of this field identified as “drained” increased from a low of 25% in 2011 to 68% in 2016. Landsat 8 vegetation indices were also successfully applied to monitoring the recovery of pasture following extreme saturation (flooding). In conjunction with this, additional EO techniques using unmanned aerial systems (UAS) were tested to map overland flow and detect buried drains. A performance assessment of UAS structure-from-motion (SfM) photogrammetry and aerial LiDAR was undertaken for modelling surface runoff (and associated nutrient loss). Overland flow models were created using the SIMWE model in GRASS GIS. Results indicated no statistical difference between models at 1, 2 & 5 m spatial resolution (p< 0.0001). Grass height was identified as an important source of error. Thermal imagery from a UAS was used to identify the locations of artifically drained areas. Using morning and afternoon images to map thermal extrema, significant differences in the rate of heating were identified between drained and undrained locations. Locations of tiled and piped drains were identified with 59 and 64% accuracy within the study area. Together these methods could enable better management of field drainage on farms, identifying drained areas, as well as the need for maintenance or replacement. They can also assess whether treatments have worked as expected or whether the underlying saturation problems continues. Through the methods developed and described herein, better characterisation of drainage status at field level may be achievable

Analysis of Spatial-Temporal Variations of Drought in Oklahoma

Drought is a recurrent natural hazard that has impacts on agriculture, hydrology, ecosystem, and social-economy. A comprehensive analysis of drought is valuable for drought assessment and mitigation. Oklahoma is a state that frequently experiences drought. The goal of this dissertation is to analyze the spatial-temporal patterns of drought in Oklahoma. Specifically, it developed a new drought index and evaluated it against a number of widely-used drought indices. Then, the spatial-temporal patterns of drought in Oklahoma were investigated using the most suitable drought index. Finally, the impacts of climate oscillations on the drought were quantified and used to develop drought forecasts. A new drought index called the Precipitation Evapotranspiration Difference Condition Index (PEDCI) was developed. It overcomes a number of the limitations of other drought indices. The comparison of PEDCI and six widely used drought indices (Palmer’s Drought Severity Index, Z-Index, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index, percent normal, and percentiles) demonstrated that the performance of drought indices varies temporally and spatially. The SPEI is the drought index that is the most representative of soil moisture conditions. The correlations with winter wheat yield indicated that drought indices such as SPEI, Z-Index and PEDCI, which are based on precipitation and evapotranspiration, are most appropriate for representing the impact of drought conditions on crop yield. Oklahoma was divided into four regions (southeast, southwest, northeast, and northwest Oklahoma) for the spatial and temporal analysis of drought. Drought frequency in northwest Oklahoma is higher than in other regions, and the frequency in spring is higher than in other seasons. There is a decadal-scale drought cycle in Oklahoma. Droughts are caused by both decreases in precipitation and increases in evapotranspiration, especially in recent years. Finally, drought is influenced by multiple climate oscillations. Seven regression models were developed for producing drought forecasts. The CCA-based regression model using multiple teleconnections at different lags was more skillful than the other drought forecast models. While skill is limited in some seasons, this method has promise for providing drought early warning in Oklahoma

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Earth resources: A continuing bibliography with indexes (issue 59)

This bibliography lists 518 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1988. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors