Satellite Remote Sensing Data for Decision Support in Emerging Agricultural Economies: How Satellite Data Can Transform Agricultural Decision Making [Perspectives]
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A data compression technique for multitemporal Landsat imagery which extracts phenological growth pattern information for agricultural crops is described. The principal component greenness transformation was applied to multitemporal agricultural Landsat data for information retrieval. The transformation was favorable for applications in agricultural Landsat data analysis because of its physical interpretability and its relation to the phenological growth of crops. It was also found that the first and second greenness eigenvector components define a temporal small-grain trajectory and nonsmall-grain trajectory, respectively.
Climate-smart agriculture (CSA) is an approach to help agricultural systems worldwide, concurrently addressing three challenge areas: increased adaptation to climate change, mitigation of climate change, and ensuring global food security - through innovative policies, practices, and financing. It involves a set of objectives and multiple transformative transitions for which there are newly identified knowledge gaps. We address these questions raised by CSA within three areas: conceptualization, implementation, and implications for policy and decision-makers. We also draw up scenarios on the future of the CSA concept in relation to the 4 per 1000 Initiative (Soils for Food Security and Climate) launched at UNFCCC 21st Conference of the Parties (COP 21). Our analysis shows that there is still a need for further interdisciplinary research on the theoretical foundation of the CSA concept and on the necessary transformations of agriculture and land use systems. Contrasting views about implementation indicate that CSA focus on the ''triple win'' (adaptation, mitigation, food security) needs to be assessed in terms of science-based practices. CSA policy tools need to incorporate an integrated set of measures supported by reliable metrics. Environmental and social safeguards are necessary to make sure that CSA initiatives conform to the principles of sustainability, both at the agriculture and food system levels.
Early applications of bioregenerative life support technologies for space exploration will likely start with supplemental food production for the crew. This could include fresh, perishable foods that cannot be stored for long and but have a high impact on the diet acceptability bioavailable nutrients. Because of the limited working volume in spacecraft, these plants must be small in size. A combination of CIF (Center Innovation Fund) and NASA Post Doctoral funding was used in FY15 to develop horticultural approaches for propagation, production and fruiting of several dwarf plum lines and evaluate their suitability as candidates for long duration space missions. Collaborators at the USDA Agricultural Research Service transformed Prunus domestica with the FT1 (Flowering Locus T1) flowering gene from Populus trichocarpa (PtFTl), which resulted in early flowering, driving the plant out of its juvenile growth phase and into reproductive development years earlier than would normally occur. The result is a plum line that has potential as a component of food production system on long-duration space missions since it completes complete generation (seed-to-seed) within less than a year and maintains a dwarf-bush or vine-like growth habit. Further, there appears to be no obligatory requirement for a dormancy period, resulting in continuous fruit production on a given plant. This potential is described in Graham et al (2015, in press).
Mexican agriculture has been greatly transformed by the widespread introduction of 'Green Revolution' technologies (irrigation, chemical fertilizers, and improved seeds), through land reform, and by land use policies oriented to export crops and grain production. Drought prone Mexico provides an excellent case to study how technological and social changes alter the impact of drought on food and agricultural system. A goal is to document and understand how relationships between climate and agriculture in Mexico have changed in the last fifty years. The results for several locations will be interpreted in light of the prospects of regional climate change due to global warming. This analysis will be complimented by four case studies of vulnerability to drought which will use local records and interviews to try and show how environmental, technological, and social changes may have altered the impacts of climate on local agricultural systems.
Expanding cities are transforming periurban environments such as agricultural land, natural grasslands, forests, wetlands, and and land, into urban surfaces, such as asphalt and concrete. This transformation is part of a process defined as "urban heat island". The urban surfaces get much hotter during the daylight hours in the summer than the natural or vegetated environment. The heat builds up creating a dome effect over the city making it many degrees hotter than it's surrounding area. The impacts from this, which include higher usage of air conditioners, water, etc., are numerous and costly. As cities expand, this problem is exacerbated. It is necessary to incorporate better quality data into urban analysis and for establishing methods that systematically and objectively monitor growth and change due to increased urbanization. NASA initiated Project Atlanta in 1997 "as an interdisciplinary remote sensing study to observe and measure the growth and development of the urban heat island effect over Atlanta, and its associated impacts". This project has recently included Salt Lake City, among others, in the study of the development and effects of "urban heat islands". NASA has made available to Salt Lake City, high resolution, 10 meter, multispectral thermal data collected in June 1998. The data collection was part of a special NASA over-flight, a mission supported by the U.S. EPA in conjunction with their Urban Heat Island (UHI) Mitigation Initiative. Salt Lake City is one of three pilot cities selected to participate in this unique initiative. Hence, this project constitutes a rare opportunity to capitalize upon state-of-the-art NASA technology and link it to an urban community very concerned about rapid growth and development. This data will enhance existing data and be used for improving technical tools used to plan for Utah's future.
Observations of the surface of the Earth began more than a half century ago with the earliest space missions. The global geopolitical environment at the beginning of the space age fueled advances in rocketry and human exploration, but also advances in remote sensing. At the same time that space-based Earth Observations were developing, global investments in infrastructure that were initiated after World War II accelerated large projects such as the construction of highways, the expansion of cities and suburbs, the damming of rivers, and the growth of big agriculture. These developments have transformed the Earth s surface at unprecedented rates. Today, we have a remarkable library of 50 years of observations of the Earth taken by satellite-based sensors and astronauts, and these images and observations provide insight into the workings of the Earth as a system. In addition, these observations record the footprints of human activities around the world, and illustrate how our activities contribute to the changing face of the Earth. Starting with the iconic "Blue Marble" image of the whole Earth taken by Apollo astronauts, we will review a timeline of observations of our planet as viewed from space.
Registered, multitemporal Landsat data of a study area in central Pennsylvania were analyzed to detect and assess changes in the forest canopy resulting from insect defoliation. Images taken July 19, 1976, and June 27, 1977, were chosen specifically to represent forest canopy conditions before and after defoliation, respectively. Several image manipulation and data transformation techniques, developed primarily for estimating agricultural and rangeland standing green biomass, were applied to these data. The applicability of each technique for estimating the severity of forest canopy defoliation was then evaluated. All techniques tested had highly correlated results. In all cases, heavy defoliation was discriminated from healthy forest. Areas of moderate defoliation were confused with healthy forest on northwest (NW) aspects, but were distinct from healthy forest conditions on southeast (SE)-facing slopes.
A procedure for calculating a kxn rank k matrix B for data compression using the Bhattacharyya bound on the probability of error and an iterative construction using Householder transformation was developed. Two sets of remotely sensed agricultural data are used to demonstrate the application of the procedure. The results of the applications gave some indication of the extent to which the Bhattacharyya bound on the probability of error is affected by such transformations for multivariate normal populations.
A technique utilizing transformed Landsat digital data for detection of agricultural drought was empirically defined during the 1976 South Dakota drought. During 1977, the procedure was expanded to the Great Plains for evaluation as a technique for detecting and monitoring vegetative water stress over large areas. The technique, Green Index Number (GIN), uses Landsat digital data from 5 by 6 nautical mile sampling frames (segments) to indicate when the vegetation within the segment is undergoing drought. At known growth stages for wheat, segments were classified as drought or non-drought areas. The remote-sensing-based information was compared to a weekly ground-based index (Crop Moisture Index) provided by the United States Department of Commerce. This comparison demonstrated a high degree of agreement between the 18-day remote sensing technique and the ground-based weekly data. Maps based on GIN of parts of the USSR and Australia were produced with a two-week lag and later compared with other crop assessments of crop conditions in these areas. These maps were judged to be in general agreement with the other data sources.
A technique utilizing transformed LANDSAT digital data for detection of agricultural vegetative water stress was developed during the 1976 South Dakota drought, and expanded to the U.S. Great Plains the following year to evaluate its effectiveness in detecting and monitoring vegetative stress water stress over large areas. This technique, the green index number (GIN), indicated when the vegetation within a segment was undergoing stress. Segments were classified as either moisture-stressed or normal using remote sensing techniques combined with a knowledge of crop condition. The remote sensing-based information was compared to a weekly ground-based index (the crop moisture index) provided by the U.S. Dept. of Commerce. The approaches used and the results from the GIN monitoring program are presented.
In an extension of previous simulation studies, a transformation of actual TM data in the six reflective bands is described which achieves three objectives: a fundamental view of TM data structures is presented, the vast majority of data variability is concentrated in a few (three) features, and the defined features can be directly associated with physical scene characteristics. The underlying TM data structure, based on three TM scenes as well as simulated data, is described, as are the general spectral characteristics of agricultural crops and other scene classes in the transformed data space.
A sustainable and just future, envisioned by the UN's 2030 Agenda for Sustainable Development, puts agricultural systems under a heavy strain. The century-old quandary to provide ever-growing human populations with sufficient food takes on a new dimension with the recognition of environmental limits for agricultural resource use. To highlight challenges and opportunities toward sustainable food security in the twenty first century, this perspective paper provides a historical account of the escalating pressures on agriculture and freshwater resources alike, supported by new quantitative estimates of the ascent of excessive human water use. As the transformation of global farming into sustainable forms is unattainable without a revolution in agricultural water use, water saving and food production potentials are put into perspective with targets outlined by the Sustainable Development Goals (SDGs). The literature body and here-confirmed global estimates of untapped opportunities in farm water management indicate that these measures could sustainably intensify today's farming systems at scale. While rigorous implementation of sustainable water withdrawals (SDG 6.4) might impinge upon 5% of global food production, scaling-up water interventions in rainfed and irrigated systems could over-compensate such losses and further increase global production by 30% compared to the current situation (SDG 2.3). Without relying on future technological fixes, traditional on-farm water and soil management provides key strategies associated with important synergies that needs better integration into agro-ecological landscape approaches. Integrated strategies for sustainable intensification of agriculture within planetary boundaries are a potential way to attain several SDGs, but they are not yet receiving attention from high-level development policies.
A communications-theory approach is taken to analyze the dispersion and concentration of signal values in various data spaces, irrespective of specific class membership. Entropy is used to quantify information, and mutual information is used to measure the information represented by subsets of spectral variables. Several different comparisons of information content are made. These include comparisons of system design capacities, of data volumes occupied by agricultural data in the spaces defined by original bands and by transformed spectral (Tasseled Cap) variables, of the information contents of original bands and Tasseled Cap variables, and of the information contents of TM and MSS for the given agricultural data sets. Also, the effects of sample size, scene content, and quantization level are examined.
A communications-theory approach is taken to analyze the dispersion and concentration of signal values in various data spaces, irrespective of specific class membership. Entropy is used to quantify information, and mutual information is used to measure the information represented by subsets of spectral variables. Several different comparisons of information content are made. These include comparisons of system design capacities, of data volumes occupied by agricultural data in the spaces defined by original bands and by transformed spectral (Tasseled Cap) variables, of the information contents of original bands and Tasseled Cap variables, and of the information contents of TM and MSS for the given agricultural data sets. Also, the effects of sample size, scene content, and quantization level are examined.
Papers are presented on a variety of techniques for the machine processing of remotely sensed data. Consideration is given to preprocessing methods such as the correction of Landsat data for the effects of haze, sun angle, and reflectance and to the maximum likelihood estimation of signature transformation algorithm. Several applications of machine processing to agriculture are identified. Various types of processing systems are discussed such as ground-data processing/support systems for sensor systems and the transfer of remotely sensed data to operational systems. The application of machine processing to hydrology, geology, and land-use mapping is outlined. Data analysis is considered with reference to several types of classification methods and systems.
Research in Controlled Ecological Life Support Systems (CELSS) conducted by NASA indicate that plant based systems are feasible candidates for human support in space. Ames has responsibility for research and development, systems integration and control, and space flight experiment portions of the CELSS program. Important areas for development of new methods and technologies are biomass production, waste processing, water purification, air revitalization, and food processing. For the plant system, the approach was to identify the flexibility and response time for the food, water, and oxygen production, and carbon dioxide consumption processes. Tremendous increases in productivity, compared with terrestrial agriculture, were realized. Waste processing research emphasizes recycle (transformation) of human wastes, trash, and inedible biomass to forms usable as inputs to the plant production system. Efforts to improve efficiency of the plant system, select new CELSS crops for a balanced diet, and initiate closed system research with the Crop Growth Research Chambers continue. The System Control and Integration program goal is to insure orchestrated system operation of the biological, physical, and chemical operation of the biological, physical, and chemical component processors of the CELSS. Space flight studies are planned to verify adequate operation of the system in reduced gravity or microgravity environments.
This paper presents relationships among Landsat MSS bands and selected transformations of them, with emphasis on the Tasseled-Cap Transformation and its Brightness, Greenness variables. It also discusses relationships between reflectance measurements made in the Landsat spectral bands and actual Landsat data. Agronomically oriented analyses of reflectance measurements of wheat throughout a growing season are presented, with a comparison of various green measures, correlation with crop development stage, and examination of the effects of moisture stress. The final example addresses the use of transformed variables in a newly developed approach to forestry change detection.