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The use of satellite data from the ERTS-1 satellite for mapping the cotton acreage in the southern deserts of California is discussed. The differences between a growing, a defoliated, and a plowed down field can be identified using an optical color combiner. The specific application of the land use maps is to control the spread of the pink bollworms by establishing planting and plowdown dates.
A hand-held Global Positioning System receiver was used to determine the precise locations of villages, houses, gardens, and other cultural and environmental features in poorly mapped South American rain forests. The Magellan NAV 1000 unit profides extremely accurate latitude and longitude information, but determination of altitude is problematical. Overall, the receiver effectively allows anthropologists to obtain essential locational data useful for categorizing land uses, mapping tribal boundaries, and other applications in regions where environmental conditions are harsh and/or accessibility is difficult.
The author has identified the following significant results. Computer compatible tapes for an early ERTS-1 multispectral scanner image over the San Francisco Bay area are being used to classify and analyze land use in this metropolitan region. NASA aircraft underflight data and land use maps from the Census Cities ERTS-1 experiment are providing training samples. The tape record has been reformatted for use with the LARSYSSA pattern recognition and classification algorithms developed at the Purdue University Laboratory for Applications of Remote Sensing. Trial land use classification systems are being compared with color infrared photography and ground truth data for San Jose and vicinity.
The Maryland Automated Geographic Information System is described as well as the development of a general land cover classification with level 2 separation for vegetation categories for a 95,000-acre study area in Howard and Anne Arundel Counties. An accuracy analysis was performed on two subsections of the entire study area. The 1:24,000 scale land use map and the high in altitude infrared photography were determined to the best source of ground truth for the analysis. Results show that (1) delineation of deciduous and coniferous forested areas from LANDSAT data compared well with results from infrared photography; (2) general classification of agricultural areas showed acceptable results; (3) delineation of residential areas had the highest error rates (difference in interpretation and cover type assignment methods was a primary factor); and (4) LANDSAT-derived land cover data is to be used as an ancillary data source in a Maryland statewide land cover inventory.
In ‘Satellite observations and modeling to understand the Lower Mekong River Basin streamflow variability’ [1] hydrological fluxes, meteorological variables, land cover land use maps, and soil characteristics and parameters data were compiled and processed for the Lower Mekong River Basin. In this work, daily streamflow time series data at nine gauges located at five different countries in the Mekong region (Thailand, Laos People׳s Democratic Republic (PDR), Myanmar, Cambodia, and Viet Nam) is presented. Satellite-based daily precipitation and air temperature (minimum & maximum) data is processed and provided over the entire basin as part of the dataset provided in this work. Moreover, land cover land use raster data that contains 18 classes that cover agriculture, urban, range and forests land cover land use classes for the basin is offered. In addition, a soil data that contains physical and chemical characteristics needed by physically based hydrological models to simulate the cycling of water and air is also provided.
Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity. Nevertheless, the global extent of natural wetland loss remains remarkably uncertain. Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km 2 (confidence interval 2.9–3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16–23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration.
The object of this report is to acquaint potential users with two computer programs, developed at NASA, Marshall Space Flight Center. They were used in producing a land use survey and maps of central north Alabama from Earth Resources Technology Satellite (ERTS) digital data. The report describes in detail the thought processes and analysis procedures used from the initiation of the land use study to its completion, as well as a photogrammetric study that was used in conjunction with the computer analysis to produce similar land use maps. The results of the land use demonstration indicate that, with respect to computer time and cost, such a study may be economically and realistically feasible on a statewide basis.
Urban flooding poses as one of the biggest issues for cities today, as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017 in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating the rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.
Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.
The author has identified the following significant results. In the first part of the investigation, spatial and spectral features were developed which were employed to automatically recognize terrain features through a clustering algorithm. In this part of the investigation, the size of the cell which is the number of digital picture elements used for computing the spatial and spectral features was varied. It was determined that the accuracy of terrain recognition decreases slowly as the cell size is reduced and coincides with increased cluster diffuseness. It was also proven that a cell size of 17 x 17 pixels when used with the clustering algorithm results in high recognition rates for major terrain classes. ERTS-1 data from five diverse geographic regions of the United States were processed through the clustering algorithm with 17 x 17 pixel cells. Simple land use maps were produced and the average terrain recognition accuracy was 82 percent.
Evaluating potential for making broad land use maps and earth resource surveys from spaceborne and airborne photography
The ground tracks and S190 swaths are presented of selected revolutions over areas containing earth resources experiment package (EREP) sites. The following eight EREP disciplines are shown: sensor performance evaluation, forestry, geology, hydrology, land use mapping, oceanography, pollution, and weather. Most of the data reported consists of passes over the continental United States.
Potential applications of remote sensing data were reviewed, and available imagery was interpreted to provide input to a demonstration data base. A literature review was conducted to determine the types and qualities of imagery required to satisfy identified data needs. Ektachrome imagery available over the demonstration areas was reviewed to establish the feasibility of interpreting cultural features, range condition, and timber type. Using the same imagery, a land use map was prepared for the demonstration area. The feasibility of identifying commercial timber areas using a density slicing technique was tested on multispectral imagery available for a portion of the demonstration area.
Photographic scales providing resource data for decision making processes of land use and a legend system for barren lands, water resources, natural vegetation, agricultural, urban, and industrial lands in hierarchical framework are applied to various remote sensing techniques. Two natural vegetation resource and land use maps for a major portion of Maricopa County, Arizona are also produced.
The author has identified the following significant results. An investigation is underway to determine the applicability of ERTS-1 data to urban and regional planning problems, using data for East Central Florida. Small scale land use mapping is feasible. Urban and commercial areas are sufficiently distinguishable that ERTS-1 appears to be a useful tool for monitoring urban and commercial growth. Development patterns of cities, growth patterns of cities, and distribution and changes in certain sectors within cities can be analyzed effectively. Digital analysis methods are proving useful.
ERTS-1 applications in water depth calculation, terrain classification, land use mapping, water pollution, and mineral exploration
ERTS-1 imagery has been successfully used for the identification of large areas of citrus groves and rice fields in the Valencia region of Eastern Spain. Results are encouraging and will facilitate the elaboration of a land use map with a fair degree of definition once methods prove to be fully operational.
Initial experimental analysis of ERTS-1 imagery has demonstrated that remote sensing from space is a means of delineating and inventorying Ohio's strip-mined areas, detecting power plant smoke plumes, and proving the data necessary for periodically compiling land use maps for the entire state. The nature and extent of these problems throughout Ohio, how ERTS data can contribute to their solution, and estimates of the long term significance of these initial findings to overall resource management interests in Ohio are summarized.