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The applications are reported of new remote sensing techniques for earth resources surveys and environmental monitoring. Applications discussed include: vegetation systems, environmental monitoring, and plant protection. Data processing systems are described.
The author has identified the following significant results. In the Plains of Tokachi, where the scale of agricultural field was comparatively large in Japan, LANDSAT data with its accuracy have proved to be useful enough to observe the actual condition of agricultural land use and changes more accurately than present methods. Species and ages of grasses in pasture were identified and soils were classified into several types. The actual land cover and ecological environment were remarkably changeable at the rapidly industrialized area by the urbanization in the flat plane and also by the forest works and road construction in the mountainous area. The practical use of the recognition results was proved as the base map of the field survey or the retouching work of the vegetation and land use. There was a 10% cut in cost, labor, and time. Vegetation cover in Tokyo districts was estimated by both the multiregression model and the parametric model. Multicorrelation coefficient between observed value and estimated value was 0.87 and standard deviation was + or - 15%. Vegetation cover in Tokyo was mapped into five levels with equal intervals of 20%.
The electric power industry is one of the heaviest adopters of Unmanned Aerial Vehicles (UAV) technology, utilizing their versatility to enhance power line maintenance programs. For reliable power grids, routine maintenance operations and rapid repair operations are required across substantial geographical distances. Both maintenance and repair decisions are driven by assessment operations, such as inspections of mechanical structures with visible-band sensors, inspections for the presence of high voltage coronas and hot spots with multispectral sensors, and surveying of equipment and adjacent features (particularly vegetation) with photogrammetric and Light Detection and Ranging (LiDAR) sensors. Assessment was historically accomplished with manned helicopter flights or foot patrols. Recently, this critical economic segment harnessed advances in Unmanned Aerial Systems (UAS)technology to accomplish assessment tasks with faster results, lower cost, and higher levels of safety than historical methods. Adoption of UAVs in high-voltage electric power transmission activities is well underway. Adoption of UAVs for low voltage distribution activities is still emerging, with the important exception of post-storm damage assessment following major hurricanes (Harvey and Maria in 2017 and Michael in 2018). This report reviews the broad UAS adoption in high-voltage electric power transmission operations and early attempts at adoption in distribution operations. The ability to fly beyond visual line of sight (BVLOS) may increase the advantages of UAV inspection over former options, and in the United States, regulators have approved two pilot projects to demonstrate BVLOS methods and their benefits in operating and maintaining the power grid.
The urban heat island effect refers to the phenomenon of substantially increased temperatures in urban areas compared to their surrounding suburban or rural counterparts. The City of San José’s Department of Parks, Recreation and Neighborhood Services (PRNS) and Department of Transportation (DOT) work to mitigate the UHI effect through urban forestry initiatives. The PRNS and DOT partnered with NASA DEVELOP to identify areas in need of tree plantings. We examined land surface temperature (LST) throughout the city using data from the Thermal Infrared Sensor (TIRS) and TIRS-2 on NASA’s Landsat 8 and 9 satellites from 2013 to 2024. We also assessed canopy cover in parks using LiDAR data collected in 2020 for the United States Geological Survey’s 3D Elevation Program, and measured vegetation greenness using the Normalized Difference Vegetation Index (NDVI) with PlanetScope imagery from 2018 to 2024. We evaluated social and environmental factors that influence the distribution of heat event impacts by creating a heat vulnerability index. We found that heat is concentrated in urban areas and that poor vegetation health is associated with high LST. We also found that socially vulnerable communities are disproportionately located in areas of high environmental risk. These analyses allow the partners to prioritize tree plantings in parks near areas of high social and environmental risk. We determined that Earth observations can be used to inform urban forestry decision making, but because methodologies for using LiDAR to assess canopy cover vary greatly, it is difficult to make comparisons across different canopy cover assessments.
A common problem for archaeologists studying ancient settlement in the Maya Lowlands is overcoming the dense vegetation in order to obtain an accurate regional perspective of the presence of archaeological sites, their exact locations and their overall extents. Most often this is done by extensive ground surveys in which many individuals chop parallel paths through the vegetation in search of sites. Once a site is found an effort is made to mark its location on a regional map and to explore its perimeter. Obtaining locational information has been made dramatically easier in recent years with the advent of improved Global Positioning Systems (GPS), however the process of initial identification of sites and the determination of their borders is exceedingly labor intensive and has remained relatively unchanged since the beginning of settlement surveys in the region in the 1950's. Currently, we are revolutionizing settlement survey in the Maya Lowlands by using remotely sensed data from IKONOS, Quickbird, and Eol, satellites. The Ancient Maya built their cities, towns and even their smallest hamlets using excavated limestone and lime plasters. We propose that the decay of these structures provides a unique microenvironment for the growth of vegetation as the levels of moisture and nutrition within the ruins vary substantially from those in the surrounding forest. These microenvironmental differences on the ground are likewise represented by compositional differences in the forest canopy both in the species present and in leaf color (representing moisture/nutritional stress) visible through the analysis of high- resolution satellite data. In this way the detailed analysis of forest composition can reveal a detailed picture of the ancient settlements that lie beneath it. Preliminary examinations using this technique have been very successful and we are refining these techniques in order to efficiently comprehend the details of Ancient Maya settlement in the Lowlands.
A common problem for archaeologists studying ancient settlement in the Maya Lowlands is overcoming the dense vegetation in order to obtain an accurate regional perspective of the presence of archaeological sites, their exact locations and their overall extents. Most often this is done by extensive ground surveys in which many individuals chop parallel paths through the vegetation in search of sites. Once a site is found an effort is made to mark its location on a regional map and to explore its perimeter. Obtaining locational information has been made dramatically easier in recent years with the advent of improved Global Positioning Systems (GPS), however the process of initial identification of sites and the determination of their borders is exceedingly labor intensive and has remained relatively unchanged since the beginning of settlement surveys in the region in the 1950 s. Currently, we are revolutionizing settlement survey in the Maya Lowlands by using remotely sensed data from IKONOS, Quickbird, and Eo 1, satellites as well as airborne AIRSAR radar data. The Ancient Maya built their cities, towns and even their smallest hamlets using excavated limestone and lime plasters. We propose that the decay of these structures provides a unique microenvironment for the growth of vegetation as the levels of moisture and nutrition within the ruins vary substantially from those in the surrounding forest. These microenvironmental differences on the ground are likewise represented by compositional differences in the forest canopy both in the species present and in leaf color (representing moisture/nutritional stress) visible through the analysis of high-resolution satellite data. In this way the detailed analysis of forest composition can reveal a detailed picture of the ancient settlements that lie beneath it. Preliminary examinations using this technique have been very successful and we are refining these techniques in order to efficiently comprehend the details of Ancient Maya settlement in the Lowlands.
From September 1972 to January 1973, ERTS-1 precisely located a 500 sq km area on the Red Sea coastal plain of Saudi Arabia within which the Desert Locust (Schistocerca gregaria, Forsk.) bred successfully and produced many small swarms. Growth of vegetation shown by satellite imagery was confirmed from ground surveys and raingauge data. The experiment demonstrates the feasibility of detecting potential locust breeding sites by satellite, and shows that an operational satellite would be a powerful tool for routine survey of the 3 x 10 to the 7th power sq km invasion area of the Desert Locust in Africa and Asia, as well as of other locust species in the arid and semi-arid tropics.
The Forest Light (FLIGHT) radiative transfer model was used to examine the uncertainty of vegetation structure measurements from NASA's planned ICESat-2 photon counting light detection and ranging (LiDAR) instrument across a synthetic Larix forest gradient in the taiga-tundra ecotone. The simulations demonstrate how measurements from the planned spaceborne mission, which differ from those of previous LiDAR systems, may perform across a boreal forest to non-forest structure gradient in globally important ecological region of northern Siberia. We used a modified version of FLIGHT to simulate the acquisition parameters of ICESat-2. Modeled returns were analyzed from collections of sequential footprints along LiDAR tracks (link-scales) of lengths ranging from 20 m-90 m. These link-scales traversed synthetic forest stands that were initialized with parameters drawn from field surveys in Siberian Larix forests. LiDAR returns from vegetation were compiled for 100 simulated LiDAR collections for each 10 Mg · ha(exp -1) interval in the 0-100 Mg · ha(exp -1) above-ground biomass density (AGB) forest gradient. Canopy height metrics were computed and AGB was inferred from empirical models. The root mean square error (RMSE) and RMSE uncertainty associated with the distribution of inferred AGB within each AGB interval across the gradient was examined. Simulation results of the bright daylight and low vegetation reflectivity conditions for collecting photon counting LiDAR with no topographic relief show that 1-2 photons are returned for 79%-88% of LiDAR shots. Signal photons account for approximately 67% of all LiDAR returns, while approximately 50% of shots result in 1 signal photon returned. The proportion of these signal photon returns do not differ significantly (p greater than 0.05) for AGB intervals greater than 20 Mg · ha(exp -1). The 50m link-scale approximates the finest horizontal resolution (length) at which photon counting LiDAR collection provides strong model fits and minimizes forest structure uncertainty in the synthetic Larix stands. At this link-scale AGB greater than 20 Mg · ha(exp -1) has AGB error from 20-50% at the 95% confidence level. These results suggest that the theoretical sensitivity of ICESat-2 photon counting LiDAR measurements alone lack the ability to consistently discern differences in inferred AGB at 10 Mg · ha(exp -1) intervals in sparse forests characteristic of the taiga-tundra ecotone.
Safe Unmanned Aerial Vehicle (UAV) operations near the ground require navigation methods that avoid fixed obstacles such as buildings, power lines and trees. Aerial lidar surveys of ground structures are available with the precision and accuracy to geolocate obstacles, but the high volume of raw survey data can exceed the compute power of onboard processors and the rendering ability of ground-based flight planning maps. Representing ground structures with bounding polyhedra instead of point clouds greatly reduces the data size and can enable effective obstacle avoidance, as long as the bounding geometry envelopes the structures with high spatial fidelity. This report describes in detail four methods to compute bounding geometries of ground obstacles from lidar point clouds. The four methods are: 1) 2.5D Maximum Elevation Box, 2) 2.5D Ground Map Extrusion, 3) 3D Bounding Cylinder, and 4) 3D Bounding Box. The methods are applied to five point cloud datasets from lidar surveys of UAV flight research sites in Georgia and Virginia with an average point spacing that ranges from 0.1m to 0.6m. The methods are assessed using survey areas with geometrically heterogeneous ground structures: buildings, vegetation, power lines, and sub-meter structures such as road signs and guy wires. The 2.5D Maximum Elevation Box method is useful for simple structures. The 2.5D Ground Map Extrusion method efficiently encloses vegetation, but requires handdrawn ground footprints. The 3D Bounding Cylinder method excels at enclosing linear structures such as power lines and fences. The 3D Bounding Box method excels at enclosing planar structures such as buildings. The methods are compared on the basis of data compression and boundary fidelity on selected areas. The 2.5D methods yield the highest data compression but the polyhedra produced by them enclose significant amounts of empty space. Boundary fidelity is superior for the 3D methods, though this fidelity comes at the cost of a roughly thirtyfold lower data compression ratio than the 2.5D Maximum Elevation Box method. A mix of these output geometries is proposed for autonomous UAV navigation with limited on-board computing. Both the accuracy and spatial detail of emerging satellite-based survey technology lower than that of aerial lidar scanning survey technology. Sub-meter structures and thin linear structures are not reliably mapped at present by satellite-based surveys.
Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.
Many studies have shown that radar images have increased classification accuracy over spectral classifications using only LANDSAT Multispectral Band Scanner (MSS) images. It was the objective to determine if a SIRA image taken over Hayfork when used alone or inconjuction with LANDSAT MSS data would increase separation of units not identified by LANSAT spectral mixture models. Areas in the LANDSAT model of varying vegetation density (0 to 50%) that had proven to be accurate by field surveys were compared. It was found in the Hayfork area that SIRA did not increase or help delineation of vegetation or ultramific units over LANDSAT MSS.
Alaskan applications of ERTS data are summarized. Areas discussed are: (1) land use; (2) archaeology; (3) vegetation mapping; (4) ice reporting and mapping; (5) permafrost; (6) mineral and oil exploration; (7) geological surveys; (8) seismology; (9) geological faults and structures; (10) hydrology and water resources; (11) glaciology; (12) water circulation in Cook Inlet; and (13) fish and mammal populations.
ERTS data affords a unique opportunity to perform urgently needed resource surveys and land use planning at a critical juncture in the history of Alaska's social and economic development. The available facilities for photographic, optical and digital processing of ERTS data are described, along with the interpretive techniques which have been developed. Examples of the applications of these facilities and techniques are given for three environmental disciplines: vegetation mapping for potential archeological sites; marine and sea ice surveys on the Alaskan continental shelf for the determination of surface circulation and sedimentation patterns and their effects on navigation, pollution assessment, fisheries, location of habors and construction of off-shore structures; snow surveys for inventories of water resources and flood potential in Alaska watersheds.
The Peten region of northern Guatemala contains some of the most significant Mayan archeological sites in Latin America. It was in this region that the Maya civilization began, flourished, and abruptly disappeared. Remote sensing technology is helping to locate and map ancient Maya sites that are threatened today by accelerating deforestation and looting. Thematic Mapper, IKONOS, and QuickBird satellite, and airborne STAR-3i and AIRSAR radar data, combined with Global Positioning System (GPS) technology, are successfully detecting ancient Maya features such as sites, roadways, canals, and water reservoirs. Satellite imagery is also being used to map the bajos, which are seasonally flooded swamps that cover over 40% of the land surface. Through the use of various airborne and satellite sensor systems we have been able to detect and map ancient causeways, temples, reservoirs, and land forms, and locate these features on the ground through GPS technology. Recently, we have discovered that there is a strong relationship between a tropical forest vegetation signature in satellite imagery and the location of archeological sites. We believe that the use o f limestone and lime plasters in ancient Maya construction affects the moisture, nutrition, and plant species of the surface vegetation. We have mapped these vegetation signatures in the imagery and verified through field survey that they are indicative of archeological sites. Through the use of remote sensing and GIS technology it is possible to identify unrecorded archeological features in a dense tropical forest environment and monitor these cultural features for their protection.
The Pet6n region of northern Guatemala contains some of the most significant Mayan archeological sites in Latin America. It was in this region that the Maya civilization began, flourished, and abruptly disappeared. Remote sensing technology is helping to locate and map ancient Maya sites that are threatened today by accelerating deforestation and looting. Thematic Mapper, IKONOS, and QuickBird satellite, and airborne STAR-3i and AIRSAR radar data, combined with Global Positioning System (GPS) technology, are successfully detecting ancient Maya features such as sites, roadways, canals, and water reservoirs. Satellite imagery is also being used to map the bajos, which are seasonally flooded swamps that cover over 40% of the land surface. Through the use of various airborne and satellite sensor systems we have been able to detect and map ancient causeways, temples, reservoirs, and land forms, and locate these features on the ground through GPS technology. Recently, we have discovered that there is a strong relationship between a tropical forest vegetation signature in satellite imagery and the location of archeological sites. We believe that the use of limestone and lime plasters in ancient Maya construction affects the moisture, nutrition, and plant species of the surface vegetation. We have mapped these vegetation signatures in the imagery and verified through field survey that they are indicative of archeological sites. Through the use of remote sensing and GIS technology it is possible to identify unrecorded archeological features in a dense tropical forest environment and monitor these cultural features for their protection.
The changes in the surface albedo associated with land-use alterations from 1880-1980 and the climatic implications of these changes are discussed. Changes in atmospheric CO2, the hydrologic cycle, atmospheric particulates and aerosols are reviewed. A global survey of the growth of agriculture and forestry data is presented. The anthopogenic vegetation changes in western Africa are examined in detail. Maps and tables of anthopogenic vegetation and albedo changes are presented for western Africa, South America, and worldwide. It is found that the mean regional albedo increased from 17.4 in 1880 to 17.8 in 1980. No apparent secular decrease in precipitation linked to vegetation clearing is observed. It is suggested that disruption of the hydrologic cycle as a result of devegetation may have led to reductions in soil moisture.
The author has identified the following significant results. Scene 1072-21173 of the Anaktuvuk Pass region of the Brooks Range, Alaska, was studied from the point of view of a resource survey for purposes of land use planning as part of the effort to develop ERTS data processing and interpretation techniques. Other data sources and surface observations were utilized to produce a resource survey of a remote and undeveloped region of Alaska. Three vegetative types are apparent: moist tundra, low brush, and high brush. Watersheds are easily defined on the multispectral imagery. Features related indirectly to economic minerals are discernible from ERTS-1 imagery supported by ground truth data. These include mountains, outwash plains and alluvial deposits, drainage patterns, lineaments and probable bedding planes. This region falls within present land class categories which are not inconsistent with the imperatives of the resources. These land class categories include native village withdrawals, regional deficiency area, national interest study area for possible inclusion in a national system, public interest areas, utility corridor, and state land selection.
The purpose of this study, based on Landsat satellite data was to characterize variations and trends over 30 consecutive years (1985-2016) in perennial vegetation green cover at over 400 confirmed Mojave Desert spring locations. These springs were surveyed between in 2015 and 2016 on lands managed in California by the U.S. Bureau of Land Management (BLM) and on several land trusts within the Barstow, Needles, and Ridgecrest BLM Field Offices. The normalized difference vegetation index (NDVI) from July Landsat images was computed at each spring location and a trend model was first fit to the multi-year NDVI time series using least squares linear regression.Â