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Donald G. Moore

Publications and source records attributed to Donald G. Moore.

7 recordsLinked to original sources
USGS1993

Identifying high production, low production and degraded rangelands in Senegal with normalized difference vegetation index data

Seasonal herbaceous vegetation production on Senegal's native rangelands exhibits high spatial and temporal variability. This variability can be monitored using normalized difference vegetation index (NDVI) data computed from 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) image data. Although annual fluctuations in rainfall account for some of the variability, numerous long-term production patterns are evident in the AVHRR time-series data. Different n productivity reflect variations in the region's climate, topography, soils, and land use. Areas of overgrazing and intensive cultivation have caused long-term soil and vegetation degradation. Rangelands of high and low productivity, and degraded rangelands were identified using NDVI. Time-series image data from 1987 though 1992 were used to map relative rangeland productivity. The results were compared to detailed resource maps on soils, vegetation and land use. Much of the variation in rangeland productivity correlated well to the known distribution of resources. The study developed an approach that identified a number of areas of degraded soils and low vegetation production.

Pecora 12 Symposium↗
USGS1991

Monitoring grasshopper and locust habitats in Sahelian Africa using GIS and remote sensing technology

Development programmes in Sahelian Africa are beginning to use geographic information system (GIS) technology. One of the GIS and remote sensing programmes introduced to the region in the late 1980s was the use of seasonal vegetation maps made from satellite data to support grasshopper and locust control. Following serious outbreaks of these pests in 1987, the programme addressed a critical need, by national and international crop protection organizations, to monitor site-specific dynamic vegetation conditions associated with grasshopper and locust breeding. The primary products used in assessing vegetation conditions were vegetation index (greenness) image maps derived from National Oceanic and Atmospheric Administration satellite imagery. Vegetation index data were integrated in a GIS with digital cartographic data of individual Sahelian countries. These near-real-time image maps were used regularly in 10 countries for locating potential grasshopper and locust habitats. The programme to monitor vegetation conditions is currently being institutionalized in the Sahel.

International Journal of Geographical Information ↗
USGS1990

Geographic information system as country-level development and monitoring tool, Senegal example

Geographic information systems (GIS) allow an investigator the capability to merge and analyze numerous types of country-level resource data. Hypothetical resource analysis applications in Senegal were conducted to illustrate the utility of a GIS for development planning and resource monitoring. Map and attribute data for soils, vegetation, population, infrastructure, and administrative units were merged to form a database within a GIS. Several models were implemented using a GIS to: analyze development potential for sustainable dryland agriculture; prioritize where agricultural development should occur based upon a regional food budget; and monitor dynamic events with remote sensing. The steps for implementing a GIS analysis are described and illustrated, and the use of a GIS for conducting an economic analysis is outlined. Using a GIS for analysis and display of results opens new methods of communication between resource scientists and decision makers. Analyses yielding country-wide map output and detailed statistical data for each level of administration provide the advantage of a single system that can serve a variety of users.

Conference Paper↗
USGS1987

A conceptual method for monitoring locust habitat

A procedure to map and monitor vegetation conditions in near-real time was developed at the United States Geological Survey;s Earth Resources Observation Systems Data Center for use in locust control efforts. Meteorological satellite dat were acquired daily for 3 weeks in October and November 1986 over a 1.4-million-square-kilometer study area centered on Botswana in southern Africa. Advanced Very High Resolution Radiometer data were screened to remove cloud-contaminated data and registered to a 1-kilometer geographic base. Each day the normalized difference vegetation index (NDVI) was calculated to determine the presence and relative amounts of green vegetation in the area. Over a 10-day cycle, subsequent dates of NDVI data were composited to fill in data removed by the cloud-screening process. At any pixel location, the maximum NDVI value was retained. At the end of the 10-day cycle, a composite vegetation-greenness map was produced and another cycle started. Greenness-change maps were produced by comparing two 10-day composite greenness images. Automated map production procedures were used to merge the NDVI image data with cartographic data (boundaries, roads, tick marks) digitized from 1:1,000,000-scale operational navigation charts. The vegetation-greenness map shoes the current distribution of vegetation in the region and can be used to locate potential locust breeding area. The change map shows areas where increases and decreases in greenness have occurred between processing cycles. Significant areas of locust damage in remote regions are characterized by an unexpected decrease in greenness. These maps can be used by locust control teams to efficiently target areas for reconnaissance. In general, the procedures and products have utility for resource managers who are required to monitor vegetation resources over large geographic regions.

Pecora XI Symposium↗
USGS1983

Infrared remote sensing for monitoring rainfall

Evaluations of thermal infrared satellite data from TIROS-N and the Heat Capacity Mapping Mission (HCMM) showed that rainfall distribution patterns could be reliably detected on images acquired up to at least three days after the event. The temperature relationship decreased eight days after the event when soil variations influenced the signal. A time-series analysis reduced thermal variability normally observed over diverse landscapes and increased the sensitivity of the procedures. The method of repetitive low-resolution thermal observations could be operationally employed over large geographic regions with currently available satellite systems. The results would augment the existing rain gauge stations by increasing the spatial sensitivity and the reliability of detection and mapping individual rainfall events.

Agricultural Water Management↗