Post-Soviet changes in cropping practices in the irrigated drylands of the Aral Sea basin

Water withdrawals for irrigated crop production constitute the largest source of freshwater consumption on Earth. Monitoring the dynamics of irrigated crop cultivation is crucial for tracking crop water consumption, particularly in water-scarce areas. We analyzed changes in water-dependent crop cultivation for 650 000 km2 of Central Asian drylands, including the entire basin of the Amu Darya river, once the largest tributary to the Aral Sea before large-scale irrigation projects grossly reduced the amount of water reaching the river delta. We used Landsat time series to map overall cropland extent, dry season cropping, and cropping frequency in irrigated croplands annually from 1987 to 2019. We scrutinized the emblematic change processes of six localities to discern the underlying causes of these changes. Our unbiased area estimates reveal that between 1988 and 2019, irrigated dry season cropping declined by 1.34 million hectares (Mha), while wet season and double cropping increased by 0.64 Mha and 0.83 Mha, respectively. These results show that the overall extent of cropland in the region remained stable, while higher cropping frequency increased harvested area. The observed changes’ overall effect on water resource use remains elusive: Following the collapse of the Soviet Union, declining dry season cultivation reduced crop water demand while, more recently, increasing cropping frequency raised water consumption. Our analysis provides the first fine-scale analysis of post-Soviet changes in cropping practices of the irrigated areas of Central Asia. Our maps are openly available and can support future assessments of land-system trajectories and, coupled with evapotranspiration estimates, changes in crop water consumption.Volkswagen Foundationhttp://dx.doi.org/10.13039/501100001663Fonds De La Recherche Scientifique - FNRShttp://dx.doi.org/10.13039/501100002661Peer Reviewe

Impact of agricultural land use in Central Asia: a review

International audienceAbstractAgriculture is major sector in the economy of Central Asia. The sustainable use of agricultural land is therefore essential to economic growth, human well-being, social equity, and ecosystem services. However, salinization, erosion, and desertification cause severe land degradation which, in turn, degrade human health and ecosystem services. Here, we review the impact of agricultural land use in the five countries of Central Asia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, during 2008–2013 in 362 articles. We use the Land Use Functions framework to analyze the type and relative shares of environmental, economic, and social topics related to agricultural land use. Our major findings are (1) research on land use in Central Asia received high levels of international attention and the trend in the number of publications exceeded the global average. (2) The impacts of land use on abiotic environmental resources were the most explored. (3) Little research is available about how agricultural land use affects biotic resources. (4) Relationships between land degradation, e.g., salinization and dust storms, and human health were the least explored. (5) The literature is dominated by indirect methods of data analysis, such as remote sensing and mathematical modeling, and in situ data collection makes up only a small proportion

Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets

For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany) was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) images and official agricultural statistics. Encouraging results were obtained demonstrating the potential of the proposed approach. For example, the spatial distribution of winter crop acreage at sub-pixel level was mapped with a cross-validated coefficient of determination of 0.8 with respect to the reference information from high resolution imagery. For the eight years for which reference information was available, the root mean squared error (RMSE) of winter crop acreage at sub-pixel level was 10%. When combined with current and future sensors, such as MODIS and Sentinel-3, the unmixing of AVHRR data can help in the building of an extended time series of crop distributions and cropping patterns dating back to the 80s.JRC.H.4-Monitoring Agricultural Resource

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales

The state of tobacco : a remote sensing approach to understanding tobacco crop production in Kentucky.

Agricultural policy allows for governing bodies to better control the landscape, economy, and security of resources. Because of this power, it is essential for policy and its effects to be thoroughly understood. This study examines the Tobacco Transition Payment Program (TTPP, “tobacco buyout”), in effect from 2005 to 2014, using a mixed methods approach. The TTPP lifted the existing geographic restrictions of tobacco production and deregulated market prices formerly controlled by the government. Kentucky’s economic, social, and agricultural landscapes changed significantly in the wake of this legislation. To explore these changes, this study employs semi-structured interviews and remote sensing analyses for a full understanding of the tobacco buyout in Kentucky. Remote sensing, and specifically multispectral imagery, offer an effective and economical way to classify crops, which is important in understanding what exists on the physical landscape. Using Landsat imagery from 2015, I employed a supervised classification of data to quantify the extent of tobacco production. I then integrate the classified landscape with survey and interview data regarding trends among tobacco farmers. This research will not only provide an extension of the existing narrative for the buyout, and further explore TTPP policy’s influence on the Kentucky agricultural landscape, but also exemplify remote sensing as a tool for policy assessment

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

Future prospects of precision agriculture in Nepal

Precision agriculture is a management system based on information and technology which analyses the spatial and temporal variability within the field and addresses them systematically for optimizing productivity, profitability, and environmental sustainability. It is an emerging concept of agriculture that implies a precise application of inputs at the right place, at the right time, and in the right amount to minimize the production cost, to boost profitability and reduce risks. The three main elements of precision agriculture are data and information, technology, and decision support systems. This system of management is known as ‘Site-specific management’ which makes use of technologies like global positioning system, global information system, remote sensors, yield monitors, guidance technology, variable-rate technology, hardware, and software. Agriculture is the mainstay of Nepal but still is not proficient enough to appease the daily consumption needs. The ongoing system of farming practices in Nepal is deemed insufficient to explore the available resources in its optimum potential. Many cultivable lands in the country are still a virgin, and many indigenous crop varieties have remained unexplored in their wilderness that is rich in biodiversity. These possibilities embark great room for increasing agricultural productivity through the precision farming system if adopted the technology on a large scale within the country. The national economy can be flustered and the environment can also be conserved using precision agriculture. It can address all agricultural and environmental issues. It is a technically sophisticated system and requires great technical knowledge for successful adoption and implementation. This study examines the history, global scenario, scope of precision agriculture, and its importance, opportunities, threats, and challenges in Nepal

A novel approach to estimate glacier mass balance in the Tien Shan and Pamir based on transient snowline observations

Glaciers are recognised as an excellent proxy for climate change and their centennial massloss has accelerated during the past decades. The Central Asian mountain ranges Tien Shan and Pamir host over 25,000 glaciers that have been observed to respond heterogeneous to climate change. Glacier changes in the region have very important consequences on the water availability for the densely populated lowlands. Despite the significance and severity that climate change exerts on the Central Asian water towers, the glacier response is still poorly understood, hampering sound interpretations and predictions of future threats and opportunities. A significant data gap in the field measurement series from the mid-1990s to around 2010, limits the analysis of long-term trends. Despite the recent efforts to re-established the historical cryospheric monitoring network, continuous long-term glacier mass balance time series remain sparse for Central Asia. Thus, improved temporal and spatial coverage of glacier monitoring is essential. Remote sensing techniques are a powerful tool to study a large number of remotely located and unmeasured glaciers and provide a possibility to partly bridge the aforementioned deficit in data availability. However, the coarse temporal resolution of geodetic mass balance assessments is not suitable to improve the understanding of ongoing processes. This accentuates the indispensable need for improved and extended annual to seasonal observations of mass change of inaccessible and remote glaciers on a cost and labour effective basis as well as for a more elaborated and enhanced, process-orientated methodology. This work provides a combination of detailed in situ measurements and remote sensing based glacier mass change observation from local to regional scale. A multi-level strategy is applied to complement data from long-term glaciological surveys and remote sensing (snowline observations and geodetic mass balance measurements) with numerical modelling to obtain information at high temporal and spatial resolution for individual glaciers. Through modelling constrained with transient snowlines, annual mass balance time series for a large amount of glaciers located in the Tien Shan and Pamir were made available. Such mass balance estimates provide valuable baseline data for climate change assessments, runoff projection, hazard evaluation and enhance process understanding. A better understanding of the regional annual variability of glacier response to climate change in the Pamir and Tien Shan became possible based on the outcome of this thesis. In the presented thesis the results are discussed in detail, the weaknesses and strengths of the developed methodology are unfolded and the relevant perspective and future research outlined.Gletscher sind ausgezeichnete Indikatoren für den Klimawandel. Ihr langjähriger Massen- verlust hat sich in den letzten Jahrzehnten weltweit akzentuiert. Die zentralasiatischen Bergketten Tien Shan und Pamir beherbergen u¨ber 25’000 Gletscher. Studien zeigen, dass diese Gletscher heterogen auf den Klimawandel reagieren. Gletscherver¨anderungen in der Region haben wichtige Auswirkungen auf die Wasserverfügbarkeit für das dicht besiedelte Flachland. Trotz den bedeutenden Konsequenzen welche durch den Klimawandel auf diese regionalen Wasserspeicher ausgeübt wird, ist die Veränderung der Gletscher im Tien Shan und Pamir immer noch relativ unbekannt, was fundierte Interpretationen und Vorhersagen zukünftiger Gefahren und Chancen erschwert. Eine prägnante Datenlücke in den existierenden Messreihen von Mitte der 1990er Jahren bis ca. 2010 schränkt eine detaillierte Analyse langfristiger Entwicklungen weiter ein. Trotz der jüngsten Bemühungen, das historische Kryosphäremessnetz wieder herzustellen, bleiben kontinuierliche Langzeitmessungen für die Gletscher in Zentralasien limitiert. Eine verbesserte zeitliche und räumliche Abdeckung der Gletscherbeobachtungen ist daher unerlässlich. Fernerkundungstechniken sind gängige Methoden, um eine große Anzahl abgelegener und unerforschter Gletscher zu untersuchen. Mit solchen Methoden kann das Defizit an Datenverfügbarkeit der Region teilweise kompensiert werden. Die grobe zeitliche Auflösung der geodätischen Massenbilanzberechnungen und das somit limitierte Prozessverständnis unterstreichen jedoch den unabdingbaren Bedarf nach verbesserten und erweiterten jährlichen bis saisonalen Massenbilanzbeobachtungen. Ab- schätzungen auf ausgedehnter räumlicher Skala, sowie eine stärkere Prozess orientierte Forschung sind nötig. Die vorliegende Arbeit beschreibt eine Kombination aus detaillierten Feldmessungen und Fernerkundungsbeobachtungen der Gletschermassenänderung im Tien Shan und Pamir. Die angewandte Strategie basiert auf mehreren Ebenen aus lokalen bis regionalen Studien. Mit dieser Strategie werden Daten aus langzeit-glaziologischen Feldmessungen und aus der Fernerkundung (Schneelinienbeobachtungen, geodätische Massenbilanzmessungen) mit numerischen Modellierungen komplementieren. Dabei werden Informationen für ausgewählte Gletscher mit hoher zeitlicher und räumlicher Auflösung extrahiert. Durch das Modellieren mit wiederholten Schneelinienbeobachtungen, welche zur Kalibrierung verwendet werden, konnten jährliche Massenbilanzzeitreihen für eine große Anzahl von Gletschern im Studiengebiet berechnet werden. Solche grossräumigen und zeitlich hochaufgelösten Abschätzungen liefern wertvolle Grundlagen für detaillierte Studien über die Auswirkungen des Klimawandels, ermöglichen fundierte Abflussprojektionen und erlauben verbesserte Gefahrenanalysen. Basierend auf den Ergebnissen dieser Arbeit, wird ein besseres Verständnis der regionalen jährlichen Variabilität der Gletscherreaktionen auf den Klimawandel im Pamir und Tien Shan ermöglicht. In der hier vorgelegten Arbeit werden die Resultate im Detail diskutiert, die Schwächen und Stärken der entwickelten Methodik offengelegt und die relevanten Perspektiven abgefasst

Triennial Report: 2012-2014

Triennial Report Purpose [Page] 3 Geographical Information Science Center of Excellence [Page] 5 SDSU Faculty [Page] 6 EROS Faculty [Page] 13 Research Professors [Page] 19 Postdoctoral Fellows [Page] 24 GSE Ph.D Program [Page] 36 Ph.D. Fellowships [Page] 37 Ph.D. Students [Page] 38 Recent Ph.D. Graduates [Page] 46 Masters Students [Page] 56 Previous Ph.D. Students [Page] 58 Center Scholars Program [Page] 59 Research Staff [Page] 60 Administrative and Information Technology Staff [Page] 62 Computer Resources [Page] 66 Research Funding [Page] 67 Glancing Back, Looking Forward [Page] 68 Appendix I Alumni Faculty and Staff Appendix II Cool Faculty Research and Locations Appendix III Non-Academic Fun Things To Do Appendix IV Publications 2012-2014 Appendix V Directory Appendix VI GIScCE Birthplace Map Appendix VII How To Get To The GIScC

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