Urban expansion and its impact on urban agriculture – remote sensing based change analysis of Kizinga and Mzinga Valley – Dar Es Salaam, Tanzania

Urban agriculture is a phenomenon that can be observed world-wide, particularly in cities of devel-oping countries. It is contributing significantly to food security and food safety and has sustained livelihood of the urban and peri-urban low income dwellers in developing countries for many years. Population increase due to rural-urban migration and natural, coupled with formal as well as infor-mal urbanization are competing with urban farming for available space and scarce water resources. A multitemporal multisensoral urban change analysis over the period of 25 years (1982-2007) was performed in order to measure and visualize the urban expansion along the Kizinga and Mzinga valley in the South of Dar es Salaam. Airphotos and VHR satellite data were analyzed by using a combination of a composition of anisotropic textural measures and spectral information. The study revealed that unplanned built-up area is expanding continuously and vegetation covers and agricultural lands decline at a fast rate. The validation showed that the overall classification accuracy varied depending on the database. The extracted built-up areas were used for visual in-terpretation mapping purposes and served as information source for another research project. The maps visualize an urban congestion and expansion of nearly 18% of the total analyzed area that had taken place in the Kizinga valley between 1982 and 2007. The same development can be ob-served in the less developed and more remote Mzinga valley between 1981 and 2002. Both areas underwent fast changes where land prices still tend to go up and an influx of people both from rural and urban areas continuously increase density with the consequence of increasing multiple land use interests.

Can we infer plant facilitation from remote sensing? a test across global drylands

Facilitation is a major force shaping the structure and diversity of plant communities in terrestrial ecosystems. Detecting positive plant–plant interactions relies on the combination of field experimentation and the demonstration of spatial association between neighboring plants. This has often restricted the study of facilitation to particular sites, limiting the development of systematic assessments of facilitation over regional and global scales. Here we explore whether the frequency of plant spatial associations detected from high-resolution remotely sensed images can be used to infer plant facilitation at the community level in drylands around the globe. We correlated the information from remotely sensed images freely available through Google Earth with detailed field assessments, and used a simple individual-based model to generate patch-size distributions using different assumptions about the type and strength of plant–plant interactions. Most of the patterns found from the remotely sensed images were more right skewed than the patterns from the null model simulating a random distribution. This suggests that the plants in the studied drylands show stronger spatial clustering than expected by chance. We found that positive plant co-occurrence, as measured in the field, was significantly related to the skewness of vegetation patch-size distribution measured using Google Earth images. Our findings suggest that the relative frequency of facilitation may be inferred from spatial pattern signals measured from remotely sensed images, since facilitation often determines positive co-occurrence among neighboring plants. They pave the road for a systematic global assessment of the role of facilitation in terrestrial ecosystems. Read More: http://www.esajournals.org/doi/10.1890/14-2358.1

Evolution of 1996-1999 La Niña and El Niño Conditions Off the Western Coast of South America: A Remote Sensing Perspective

We present the evolution of oceanographic conditions off the western coast of South America between 1996 and 1999, including the cold periods of 1996 and 1998-1999 and the 1997-1998 El Niño, using satellite observations of sea level, winds, sea surface temperature (SST), and chlorophyll concentration. Following a period of cold SST and low sea levels in 1996, both were anomalously high between March 1997 and May 1998. The anomalies were greatest between 5 degrees S and 15 degrees S, although they extended beyond 40 degrees S. Two distinct peaks in sea level and SST occurred in June-July 1997 and December 1997 to January 1998, separated by a relaxation period (August-November) of weaker anomalies. Satellite winds were upwelling favorable throughout the time period for most of the region and in fact increased between November 1997 and March 1998 between 5 degrees S and 25 degrees S. Satellite-derived chlorophyll concentrations are available for November 1996 to June 1997 (Ocean Color and Temperature Sensor (OCTS)) and then from October 1997 to present (Sea-viewing Wide Field-of-view Sensor (SeaWiFS)). Near-surface chlorophyll concentrations fell from May to June 1997 and from December 1997 to March 1998. The decrease was more pronounced in northern Chile than off the coast of Peru or central Chile and was stronger for larger cross-shelf averaging bins since nearshore concentrations remained relatively high.

Radiance, irradiance, and remote sensing reflectance during the North Sea Coast Harmful Algal Bloom (NORCOHAB II) HEINCKE cruise HE302

In this study four data quality flags are presented for automated and unmanned above-water hyperspectral optical measurements collected underway in the North Sea, The Minch, Irish Sea and Celtic Sea in April/May 2009. Coincident to these optical measurements a DualDome D12 (Mobotix, Germany) camera system was used to capture sea surface and sky images. The first three flags are based on meteorological conditions, to select erroneous incoming solar irradiance (ES) taken during dusk, dawn, before significant incoming solar radiation could be detected or under rainfall. Furthermore, the relative azimuthal angle of the optical sensors to the sun is used to identify possible sunglint free sea surface zones. A total of 629 spectra remained after applying the meteorological masks (first three flags). Based on this dataset, a fourth flag for sunglint was generated by analysing and evaluating water leaving radiance (LW) and remote sensing reflectance (RRS) spectral behaviour in the presence and absence of sunglint salient in the simultaneously available sea surface images. Spectra conditions satisfying "mean LW (700-950 nm) < 2 mW/m**2/nm/Sr" or alternatively "minimum RRS (700-950 nm) < 0.010/Sr", mask the most measurements affected by sunglint, providing efficient flagging of sunglint in automated quality control. It is confirmed that valid optical measurements can be performed 0° <= theta <= 360° although 90° <= theta <= 135° is recommended.

A fractional vegetation cover remote sensing product on pan-arctic scale with link to geotiff image

The paper presents first results of a pan-boreal scale land cover harmonization and classification. A methodology is presented that combines global and regional vegetation datasets to extract percentage cover information for different vegetation physiognomy and barren for the pan-arctic region within the ESA Data User Element Permafrost. Based on the legend description of each land cover product the datasets are harmonized into four LCCS (Land Cover Classification System) classifiers which are linked to the MODIS Vegetation Continuous Field (VCF) product. Harmonized land cover and Vegetation Continuous Fields products are combined to derive a best estimate of percentage cover information for trees, shrubs, herbaceous and barren areas for Russia. Future work will concentrate on the expansion of the developed methodology to the pan-arctic scale. Since the vegetation builds an isolation layer, which protects the permafrost from heat and cold temperatures, a degradation of this layer due to fire strongly influences the frozen conditions in the soil. Fire is an important disturbance factor which affects vast processes and dynamics in ecosystems (e.g. biomass, biodiversity, hydrology, etc.). Especially in North Eurasia the fire occupancy has dramatically increased in the last 50 years and has doubled in the 1990s with respect to the last five decades. A comparison of global and regional fire products has shown discrepancies between the amounts of burn scars detected by different algorithms and satellite data.

Shipborne ocean color remote sensing

In this paper, modernized shipborne procedures are presented to collect and process above-water radiometry for remote sensing applications. A setup of five radiometers and a bidirectional camera system, which provides panoramic sea surface and sky images, is proposed for the collection of high-resolution radiometric quantities. Images from the camera system can be used to determine sky state and potential glint, whitecaps, or foam contamination. A peak in the observed remote sensing reflectance RRS spectra between 750-780 nm was typically found in spectra with relatively high surface reflected glint (SRG), which suggests this waveband could be a useful SRG indicator. Simplified steps for computing uncertainties in SRG corrected RRS are proposed and discussed. The potential of utilizing "unweighted multimodel averaging," which is the average of four or more common SRG correction models, is examined to determine the best approximation RRS. This best approximation RRS provides an estimate of RRS based on various SRG correction models established using radiative transfer simulations and field investigations. Applying the average RRS provides a measure of the inherent uncertainties or biases that result from a user subjectively choosing any one SRG correction model. Comparisons between inherent and apparent optical property derived observations were used to assess the robustness of the SRG multimodel averaging ap- proach. Correlations among the standard SRG models were completed to determine the degree of association or similarities between the SRG models. Results suggest that the choice of glint models strongly affects derived RRS values and can also influence the blue to green band ratios used for modeling biogeochemical parameters such as for chlorophyll a. The objective here is to present a uniform and traceable methodology for determining ship- borne RRS measurements and its associated errors due to glint correction and to ensure the direct comparability of these measurements in future investigations. We encourage the ocean color community to publish radiometric field measurements with matching and complete metadata in open access repositories.