Accurate estimates of cultivated area and crop yield are critical to our understanding of agricultural production and food security, particularly for semi-arid regions like the Sahel of West Africa, where crop production is mainly rain-fed and food security is closely correlated with the inter-annual variations in rainfall. Several global and regional land cover products, based on satellite remotely-sensed data, provide estimates of the agricultural land use intensity, but the initial comparisons indicate considerable differences among them, relating to differences in the satellite data quality, classification approaches, and spatial and temporal resolutions. Here, we quantify the accuracy of available cropland products across Sahelian West Africa using an independent, high-resolution, visually interpreted sample dataset that classifies all points across West Africa using a 2-km sample grid (~500,000 points for the study area). We estimate the “quantity” and “allocation” disagreements for the cropland class of eight land cover products in five Western Sahel countries (Burkina Faso, Mali, Mauritania, Niger, and Senegal). The results confirm that coarse spatial resolution (300 m, 500 m, and 1000 m) land cover products have higher disagreements in mapping the fragmented agricultural landscape of the Western Sahel. Earlier products (e.g., GLC2000) are less accurate than recent products (e.g., ESA CCI 2013, MODIS 2013 and GlobCover 2009). We also show that two of the finer spatial resolution maps (GFSAD30, and GlobeLand30) using advanced classification approaches (random forest, decision trees, and pixel-object combined) are currently the best available products for cropland identification. However, none of the eight land cover databases examined is consistent in reaching the targeted 75% accuracy threshold in the five Sahelian countries. The majority of currently available land cover products overestimate cultivated areas by an average of 170% relative to the cropland area in the reference data.
[学术文献] A global dataset of air temperature derived from satellite remote sensing and weather stations 进入全文
Air temperature at 2 m above the land surface is a key variable used to assess climate change. However, observations of air temperature are typically only available from a limited number of weather stations distributed mainly in developed countries, which in turn may often report time series with missing values. As a consequence, the record of air temperature observations is patchy in both space and time. Satellites, on the other hand, measure land surface temperature continuously in both space and time. In order to combine the relative strengths of surface and satellite temperature records, we develop a dataset in which monthly air temperature is predicted from monthly land surface temperature for the years 2003 to 2016, using a statistical model that incorporates information on geographic and climatic similarity. We expect this dataset to be useful for various applications involving climate monitoring and land-climate interactions.
Transformative approaches to adaptation in agriculture will be needed to maintain and enhance global food security, avoid maladaptation and reduce growing risks of crisis and conflict. Today, the agriculture sector practices adaptation with relatively limited incremental adjustments to existing systems to better manage current climate variability and cope with near-term climate risks. Increasingly, severe climate impacts are beginning to test the limits of what we can adapt to through such relatively minor adjustments. These impacts will increasingly require more dramatic shifts at greater scale, speed, and intensity to manage risk, strengthen food security and protect lives and livelihoods—especially among the poorest and most vulnerable, who often depend on climate-sensitive sectors such as agriculture, fishing and tourism. This working paper explores the concept of transformative adaptation for agriculture and why it is needed. It looks at how transformative outcomes could be achieved by aligning adaptation projects along pathways and adjusting planning processes to incorporate longer-term, more systemic approaches.
International Journal of Applied Earth Observation and Geoinformation
Geology is defined as the ‘study of the planet Earth – the materials of which it is made, the processes that act on these materials, the products formed, and the history of the planet and its life forms since its origin’ (Bates and Jackson, 1976). Remote sensing has seen a number of variable definitions such as those by Sabins and Lillesand and Kiefer in their respective textbooks (Sabins, 1996, Lillesand and Kiefer, 2000). Floyd Sabins (Sabins, 1996) defined it as ‘the science of acquiring, processing and interpreting images that record the interaction between electromagnetic energy and matter’ while Lillesand and Kiefer (Lillesand and Kiefer, 2000) defined it as ‘the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation’. Thus Geological Remote Sensing can be considered the study of, not just Earth given the breadth of work undertaken in planetary science, geological features and surfaces and their interaction with the electromagnetic spectrum using technology that is not in direct contact with the features of interest.
[学术文献] The role of big data and knowledge management in improving projects and project-based organizations 进入全文
Procedia Computer Science
Knowledge management plays a significant role in organizations; supporting organizations to deal effectively with changes, increasing their productivity and paving the way to development and innovation. Several scientific studies have addressed the relevance of applying knowledge management initiatives to improve projects as well as organizations that conduct projects.This paper will look at the interaction between knowledge management and big data within the context of projects. In this regard, this paper will discuss, among other things, (1) how big data can contribute to enhance knowledge management in projects and project-based organizations (2) what kind of pitfalls, challenges and opportunities that are associated with the interplay between knowledge management and big data (3) how this interplay can improve projects so that the projects can be carried out effectively and efficiently. These three questions are addressed by taking into consideration some of the important, underlying issues that are essential for ensuring improved decision making and performance in projects and project-based organizations.