Application of Satellite Sensing to Agricultural Research and Development

Overview done by the FAO Remote Sensing Unit for TAC of the state of the art of remote sensing applied to agricultural research and development with emphasis on satellite sensing and potential international cooperative programs to capitalize on the technology. Agenda document presented at TAC's Tenth Meeting, July 1975

USE OF A SPATIALLY WEIGHTED MULTIVARIATE CLASSIFICATION OF SOIL PROPERTIES, TERRAIN AND REMOTE SENSING DATA TO FORM LAND MANAGEMENT UNITS

ABSTRACT Research has been conducted to develop a methodology that can delineate land management units (LMU's) that is, zones within a paddock which can be identified, mapped and managed according to their land-use or productive capabilities. Soil sampling and analysis is a crucial component in depicting the landscape characteristics, however it is a time consuming and costly exercise to undertake. Data from a 10m resolution digital elevation model (DEM) and high resolution digital multi spectral imagery (DMSI) has been used in association with field sampled data on soil properties to investigate the variability in the landscape at large scale. The paper describes the design and implementation of a two stage methodology based on Oliver and Webster's (1989) spatially weighted multivariate classification, for delineating LMU's intended for precision agricultural applications. Utilising data on physical and chemical soil properties, topographic variables derived from a DEM and spectral information from DMSI collected at 250 stratified random sampling locations within a 1670 ha property in Western Australia, the methodology initially classifies sampling points into LMU's based on a geographically weighted similarity matrix. The second stage delineates higher resolution LMU boundaries by using the geographic location, DMSI and DEM data on a 10m grid across the remaining study area and assigning each pixel to an appropriate LMU. The method groups sample points and pixels with respect to their variables and their spatial relationship on the ground, thus forming contiguous, homogenous LMU's that can be adopted in precision agricultural applications

Computerized information system for poverty monitoring : user's manual

Co-published by SPARSS

Fusing Landsat and SAR Data for Mapping Tropical Deforestation through Machine Learning Classification and the PVts-β Non-Seasonal Detection Approach

Altres ajuts: The work of Yonatan Tarazona Coronel has been partially funded by American Program in GIS and Remote Sensing and National Program of Scholarships and Educational Credit (PRONABEC-Peru) through RJ: Nº 4276-2018-MINEDU/VMGI-PRONABEC-OBE and RJ: Nº 942-2019-MINEDU/VMGI-PRONABEC-OBE.This article focuses on mapping tropical deforestation using time series and machine learning algorithms. Before detecting changes in the time series, we reduced seasonality using Photosynthetic Vegetation (PV) index fractions obtained from Landsat images. Single and multi-temporal filters were used to reduce speckle noise from Synthetic Aperture Radar (SAR) images (i.e., ALOS PALSAR and Sentinel-1B) before fusing them with optical images through Principal Component Analysis (PCA). We detected only one change in the two PV series using a non-seasonal detection approach, as well as in the fused images through five machine learning algorithms that were calibrated with Cross-Validation (CV) and Monte Carlo Cross-Validation (MCCV). In total, four categories were obtained: forest, cropland, bare soil, and water. We then compared the change map obtained with time series and that obtained with the classification algorithms with the best calibration performance, revealing an overall accuracy of 92.91% and 91.82%, respectively. For statistical comparisons, we used deforestation reference data. Finally, we conclude with some discussions and reflections on the advantages and disadvantages of the detections made with time series and machine learning algorithms, as well as the contribution of SAR images to the classifications, among other aspects

Complexities and Controversies in Himalayan Research: A Call for Collaboration and Rigor for Better Data

The Himalaya range encompasses enormous variation in elevation, precipitation, biodiversity, and patterns of human livelihoods. These mountains modify the regional climate in complex ways; the ecosystem services they provide influence the lives of almost 1 billion people in 8 countries. However, our understanding of these ecosystems remains rudimentary. The 2007 Intergovernmental Panel on Climate Change report that erroneously predicted a date for widespread glacier loss exposed how little was known of Himalayan glaciers. Recent research shows how variably glaciers respond to climate change in different Himalayan regions. Alarmist theories are not new. In the 1980s, the Theory of Himalayan Degradation warned of complete forest loss and devastation of downstream areas, an eventuality that never occurred. More recently, the debate on hydroelectric construction appears driven by passions rather than science. Poor data, hasty conclusions, and bad science plague Himalayan research. Rigorous sampling, involvement of civil society in data collection, and long-term collaborative research involving institutions from across the Himalaya are essential to improve knowledge of this region

The Legacy of Leaded Gasoline in Bottom Sediment of Small Rural Reservoirs

The historical and ongoing lead (Pb) contamination caused by the 20th-century use of leaded gasoline was investigated by an analysis of bottom sediment in eight small rural reservoirs in eastern Kansas, USA. For the reservoirs that were completed before or during the period of maximum Pb emissions from vehicles (i.e., the 1940s through the early 1980s) and that had a major highway in the basin, increased Pb concentrations reflected the pattern of historical leaded gasoline use. For at least some of these reservoirs, residual Pb is still being delivered from the basins. There was no evidence of increased Pb deposition for the reservoirs completed after the period of peak Pb emissions and (or) located in relatively remote areas with little or no highway traffic. Results indicated that several factors affected the magnitude and variability of Pb concentrations in reservoir sediment including traffic volume, reservoir age, and basin size. The increased Pb concentrations at four reservoirs exceeded the U.S. Environmental Protection Agency threshold-effects level (30.2 mg kg-1) and frequently exceeded a consensus-based threshold-effects concentration (35.8 mg kg-1) for possible adverse biological effects. For two reservoirs it was estimated that it will take at least 20 to 70 yr for Pb in the newly deposited sediment to return to baseline (pre-1920s) concentrations (30 mg kg-1) following the phase out of leaded gasoline. The buried sediment with elevated Pb concentrations may pose a future environmental concern if the reservoirs are dredged, the dams are removed, or the dams fail