A Multi-Temporal Object-Based Image Analysis to Detect Long-Lived Shrub Cover Changes in Drylands

Climate change and human actions condition the spatial distribution and structure of vegetation, especially in drylands. In this context, object-based image analysis (OBIA) has been used to monitor changes in vegetation, but only a few studies have related them to anthropic pressure. In this study, we assessed changes in cover, number, and shape of Ziziphus lotus shrub individuals in a coastal groundwater-dependent ecosystem in SE Spain over a period of 60 years and related them to human actions in the area. In particular, we evaluated how sand mining, groundwater extraction, and the protection of the area affect shrubs. To do this, we developed an object-based methodology that allowed us to create accurate maps (overall accuracy up to 98%) of the vegetation patches and compare the cover changes in the individuals identified in them. These changes in shrub size and shape were related to soil loss, seawater intrusion, and legal protection of the area measured by average minimum distance (AMD) and average random distance (ARD) analysis. It was found that both sand mining and seawater intrusion had a negative effect on individuals; on the contrary, the protection of the area had a positive effect on the size of the individuals’ coverage. Our findings support the use of OBIA as a successful methodology for monitoring scattered vegetation patches in drylands, key to any monitoring program aimed at vegetation preservation

Remote Sensing for Site-Specific Crop Management: Evaluating the Potential of Digital Multi-Spectral Imagery for Monitoring Crop Variability and Weeds within Paddocks

This paper analyses the potential and limitations of airborne remote sensing systems for detecting crop growth variability and weed infestation within paddocks at specified capture times. The detection of areas of crop growth variability can help farmers become aware of regions within their paddock where they may be experiencing above and below average yields due to changes in soil or management conditions. For instance, the early detection of weed infestation within cereal crops is crucial for lessening their impact on the final yield. Transect sampling within a canola paddock of a broad acre agricultural property in the South West of Western Australia was conducted synchronous with the capture of 1m spatial resolution DMSI. The four individual bands (blue, green, red and near- infrared) of the DMSI were correlated with LAI and weed density counts collected in the paddock. Statistical analyses show the LAI of canola had strong negative correlations with the blue (-0.93) and red (-0.89) bands and a strong positive correlation was found with the near-infrared band (0.82). The strong correlations between the canola LAI and selected bands of the DMSI indicate that this may be a suitable technique for monitoring canola variability to derive information layers that can be used in creating meaningful "within-field" management units. Likewise, DMSI could be used as a non-invasive tool for in season crop monitoring. The correlation analysis with the weed density (e.g. self sown wheat, ryegrass and clover) attributed to only one negative weak correlation with the red band (-0.38). The less successful detection of weeds is attributed to the minimal weeddensity within the paddock (e.g. mean 34 plants m-2) and indistinct spectral difference from canola at the early time of imagery capture required by farmers for effective variable rate applications of herbicides.LAI, remote sensing, crop density, vegetation indices, weed mapping., Crop Production/Industries,

ERTS-1 investigation of wetlands ecology

The author has identified the following significant results. Data from aircraft can be used for large scale mapping where detailed information is necessary, whereas Landsat-1 data are useful for rapid mapping of gross wetland boundaries and vegetative composition and assessment of seasonal change plant community composition such as high and low growth forms of Spartina alterniflora, Juncus roemarianus, and Spartina cynosuroides. Spoil disposal and wetland ditching activities may also be defined. Wetland interpretation is affected by tidal stage; drainage patterns are more easily detected at periods of low water. Species discrimination is easier at periods of high water during the growing season; upper wetland boundaries in fresh water tidal marshes are more easily delineated during the winter months when marsh vegetation is largely dead or dormant. Fresh water discharges from coastal streams may be inferred from the species composition of contiguous wetlands

Spectral characterization of the Nigerian shoreline using Landsat imagery

The challenges of shoreline mapping include the high costs of acquiring up-to-date survey data over the coastal area. As a result, in many developing countries, the shoreline has not been consistently mapped. The variety of methods used for this mapping and the large time differences between the surveys (on the order of decades) could result in inaccuracies in shoreline data. This study presents the development of a shoreline characterization procedure for the Nigerian coastline using satellite remote sensing technology. The study goal is to produce a complete, consistent and continuous shoreline map using publicly available data processed in a GIS environment. A spectral analysis using different satellite bands was conducted to define the land/water boundary and characterize the coastal area around the shoreline. The satellite-derived shorelines were compared to charted shorelines for adequacy and consistency. The procedure was developed based on study sites along the Nigerian coastline. Although the shoreline characterization procedure is developed based on datasets from Nigeria, the procedure should be suitable for use in mapping other developing areas around world

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems

Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop

The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985. It was attended by 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years. Written summaries of 27 of the presentations are in these Proceedings. Many of the results presented at the Workshop are preliminary because most investigators have been working with this fundamentally new type of data for only a relatively short time. Nevertheless, several conclusions can be drawn from the Workshop presentations concerning the value of imaging spectrometry to Earth remote sensing. First, work with AIS has shown that direct identification of minerals through high spectral resolution imaging is a reality for a wide range of materials and geological settings. Second, there are strong indications that high spectral resolution remote sensing will enhance the ability to map vegetation species. There are also good indications that imaging spectrometry will be useful for biochemical studies of vegetation. Finally, there are a number of new data analysis techniques under development which should lead to more efficient and complete information extraction from imaging spectrometer data. The results of the Workshop indicate that as experience is gained with this new class of data, and as new analysis methodologies are developed and applied, the value of imaging spectrometry should increase