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At least 1,621 records · Page 90Linked to original sources

USGS2017

The logic of selecting an appropriate map projection in a Decision Support System (DSS)

There are undeniable practical consequences to consider when choosing an appropriate map projection for a specific region. The surface of a globe covered by global, continental, and regional maps are so singular that each type distinctively affects the amount of distortion incurred during a projection transformation because of the an assortment of effects caused by distance, direction, scale , and area. A Decision Support System (DSS) for Map Projections of Small Scale Data was previously developed to help select an appropriate projection. This paper reports on a tutorial to accompany that DSS. The DSS poses questions interactively, allowing the user to decide on the parameters, which in turn determines the logic path to a solution. The objective of including a tutorial to accompany the DSS is achieved by visually representing the path of logic that is taken to a recommended map projection derived from the parameters the user selects. The tutorial informs the DSS user about the pedigree of the projection and provides a basic explanation of the specific projection design. This information is provided by informational pop-ups and other aids.

Book chapter↗

USGS1985

Topographic mapping of the Moon

Contour maps of the Moon have been compiled by photogrammetric methods that use stereoscopic combinations of all available metric photographs from the Apollo 15, 16, and 17 missions. The maps utilize the same format as the existing NASA shaded-relief Lunar Planning Charts (LOC-1, -2, -3, and -4), which have a scale of 1:2 750 000. The map contour interval is 500m. A control net derived from Apollo photographs by Doyle and others was used for the compilation. Contour lines and elevations are referred to the new topographic datum of the Moon, which is defined in terms of spherical harmonics from the lunar gravity field. Compilation of all four LOC charts was completed on analytical plotters from 566 stereo models of Apollo metric photographs that cover approximately 20% of the Moon. This is the first step toward compiling a global topographic map of the Moon at a scale of 1:5 000 000. ?? 1985 D. Reidel Publishing Company.

Earth, Moon and Planets↗

USGS2011

Improved mapping of National Atmospheric Deposition Program wet-deposition in complex terrain using PRISM-gridded data sets

High-elevation regions in the United States lack detailed atmospheric wet-deposition data. The National Atmospheric Deposition Program/National Trends Network (NADP/NTN) measures and reports precipitation amounts and chemical constituent concentration and deposition data for the United States on annual isopleth maps using inverse distance weighted (IDW) interpolation methods. This interpolation for unsampled areas does not account for topographic influences. Therefore, NADP/NTN isopleth maps lack detail and potentially underestimate wet deposition in high-elevation regions. The NADP/NTN wet-deposition maps may be improved using precipitation grids generated by other networks. The Parameter-elevation Regressions on Independent Slopes Model (PRISM) produces digital grids of precipitation estimates from many precipitation-monitoring networks and incorporates influences of topographical and geographical features. Because NADP/NTN ion concentrations do not vary with elevation as much as precipitation depths, PRISM is used with unadjusted NADP/NTN data in this paper to calculate ion wet deposition in complex terrain to yield more accurate and detailed isopleth deposition maps in complex terrain. PRISM precipitation estimates generally exceed NADP/NTN precipitation estimates for coastal and mountainous regions in the western United States. NADP/NTN precipitation estimates generally exceed PRISM precipitation estimates for leeward mountainous regions in Washington, Oregon, and Nevada, where abrupt changes in precipitation depths induced by topography are not depicted by IDW interpolation. PRISM-based deposition estimates for nitrate can exceed NADP/NTN estimates by more than 100% for mountainous regions in the western United States.

Environmental Monitoring and Assessment↗

USGS1986

A spherical electron-channelling pattern map for use in quartz petrofabric analysis

Electron channelling patterns (ECP's) are formed in the scanning electron microscope (SEM) by the interaction between the incident electrons and the lattice of crystalline specimens. The patterns are unique for a particular crystallographic orientation and are therefore of considerable potential in petrofabric studies provided they can be accurately indexed. Indexing requires an ECP-map of the crystallographic stereogram or unit triangle covering all possible orientations and hence ECP patterns. Due to the presence of long-range distortions in planar ECP-maps, it is more convenient to construct the maps over a spherical surface. This also facilitates the indexing of individual ECP's. A spherical ECP-map for quartz is presented together with an example of its use in petrofabric analysis.

Journal of Structural Geology↗

USGS2020

Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes

Effective and efficient methods are needed to map agricultural subsurface drainage systems. Visible-color (VIS-C), multispectral (MS), and thermal infrared (TIR) imagery obtained by unmanned aerial vehicles (UAVs) may provide a means for determining drainage pipe locations. Aerial surveys using a UAV with VIS-C, MS, and TIR cameras were conducted at 29 agricultural field sites in the Midwest U.S.A. to evaluate the potential of this technology for mapping buried drainage pipes. Overall results show VIS-C imagery detected at least some drain lines at 48 % of the sites (14 out of 29), MS imagery detected drain lines at 59 % of the sites (17 out of 29), and TIR imagery detected drain lines at 69 % of the sites (20 out of 29). Three key findings, listed as follows and emphasized in this article by site examples, were extracted from the overall results. (1) Although TIR generally worked best, there were sites where either VIS-C or MS proved more effective than TIR for mapping subsurface drainage systems. Consequently, to ensure the greatest chance for successfully determining drainage pipe patterns in a field, UAV surveys need to be carried out with all three types of cameras, VIS-C, MS, and TIR. (2) Timing of UAV surveys relative to recent rainfall can sometimes have an important impact on drainage pipe detection results. (3) Linear features representing drain lines and farm field operations can be confused with one another and are often both depicted on site aerial imagery. Knowledge of subsurface drainage system installation and farm field operations can be employed to distinguish linear features representing drain lines from those representing farm field operations. The overall results and extracted key findings from this study clearly indicate that VIS-C, MS, and TIR imagery obtained with UAVs have significant potential for use in mapping agricultural drainage pipe systems.

Indiana, Iowa, Michigan, Ohio↗

USGS2003

Atmospheric deposition maps for the Rocky Mountains

Variability in atmospheric deposition across the Rocky Mountains is influenced by elevation, slope, aspect, and precipitation amount and by regional and local sources of air pollution. To improve estimates of deposition in mountainous regions, maps of average annual atmospheric deposition loadings of nitrate, sulfate, and acidity were developed for the Rocky Mountains by using spatial statistics. A parameter-elevation regressions on independent slopes model (PRISM) was incorporated to account for variations in precipitation amount over mountainous regions. Chemical data were obtained from the National Atmospheric Deposition Program/National Trends Network and from annual snowpack surveys conducted by the US Geological Survey and National Park Service, in cooperation with other Federal, State and local agencies. Surface concentration maps were created by ordinary kriging in a geographic information system, using a local trend and mathematical model to estimate the spatial variance. Atmospheric-deposition maps were constructed at 1-km resolution by multiplying surface concentrations from the kriged grid and estimates of precipitation amount from the PRISM model. Maps indicate an increasing spatial trend in concentration and deposition of the modeled constituents, particularly nitrate and sulfate, from north to south throughout the Rocky Mountains and identify hot-spots of atmospheric deposition that result from combined local and regional sources of air pollution. Highest nitrate (2.5-3.0kg/ha N) and sulfate (10.0-12.0kg/ha SO4) deposition is found in northern Colorado.

Atmospheric Environment↗

USGS2020

Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment

Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails ( Rallus obsoletus yumanensis ) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.

Arizona, California, Nevada↗

USGS2026

3D semantic mapping of surface geological features

Semantic mapping in 3D is fundamental to a wide range of geoscientific studies and applications, including geomorphology, hazard assessment, and environmental monitoring. However, automatically segmenting geological features from large-scale photogrammetric datasets remains a significant challenge. We present a methodology to address this gap. Using overlapping images collected over environments of interest, Structure-from-Motion (SfM) produces georeferenced point clouds and estimates camera poses. Existing large vision models, such as Segment Anything Model, segment objects in the images, generating pixel-segmentation associations. To produce pixel-point associations, we project the points back onto the camera image planes. As objects are independently segmented across multiple images with different perspectives, we develop a segmentation mosaicking algorithm to build probabilistic point-segmentation associations that combines the pixel-segmentation associations and pixel-point associations. Our methodology is validated using both synthetic data generated by Kubric and real-world UAV-SfM data. The implementation is designed to be compatible with existing SfM software, including Agisoft and OpenDroneMap, for photogrammetry mapping in geoscience studies. As a case study, we apply our method to the semantic mapping of precariously balanced rocks (PBRs), which provide upper-bound constraints on historical ground motion shaking intensity. To support object-level identification of PBRs, we additionally integrated Grounding DINO, enabling text-prompted segmentation of features of interest within UAV imagery. This case study demonstrates the effectiveness of our method in generating a 3D semantic map of PBRs, enabling spatial distribution of PBR fragility for earthquake hazard analysis.

Computers & Geosciences↗

USGS2005

Characterization of waste rock associated with acid drainage at the Penn Mine, California, by ground-based visible to short-wave infrared reflectance spectroscopy assisted by digital mapping

Prior to remediation at the abandoned Cu-Zn Penn Mine in the Foothills massive sulfide belt of the Sierra Nevada, CA, acid mine drainage (AMD) was created, in part, by the subaerial oxidation of sulfides exposed on several waste piles. To support remediation efforts, a mineralogical study of the waste piles was undertaken by acquiring reflectance spectra (measured in the visible to short-wave infrared range of light (0.35-2.5 ??m) using a portable, digitally integrated pen tablet PC mapping system with differential global positioning system and laser rangefinder support. Analysis of the spectral data made use of a continuum removal and band-shape comparison method, and of reference spectral libraries of end-member minerals and mineral mixtures. Identification of secondary Fe-bearing minerals focused on band matching in the region between 0.43 and 1.3 ??m. Identification of sheet and other silicates was based on band-shape analysis in the region between 1.9 and 2.4 ??m. Analysis of reflectance spectra of characterized rock samples from the mine helped in gauging the spectral response to particle size and mixtures. The resulting mineral maps delineated a pattern of accumulation of secondary Fe minerals, wherein centers of copiapite and jarosite that formed at low pH (<3) were surrounded successively by goethite and hematite, which mark progressive increases in pH. This pattern represents the evolution of acid solutions discharged from the pyritic waste piles and the subsequent accumulation of secondary precipitates by hydrolysis reactions. The results highlight the high capacity of the pyritic waste to release further acid mine drainage into the environment, as well as the effectiveness of the mapping method to detect subtle changes in surface mineralogy and to produce maps useful to agencies responsible for remediating the site. ?? 2004 Elsevier B.V. All rights reserved.

Chemical Geology↗

USGS2013

Using simulated maps to interpret the geochemistry, formation and quality of the Blue Gem Coal Bed, Kentucky, USA

This study presents geostatistical simulations of coal-quality parameters, major oxides and trace metals for an area covering roughly 812 km 2 of the Blue Gem coal bed in southeastern Kentucky, USA. The Blue Gem, characterized by low ash yield and low sulfur content, is an important economic resource. Past studies have characterized the Blue Gem's geochemistry, palynology and petrography and inferred a depositional setting of a planar peat deposit that transitioned to slightly domed later in its development. These studies have focused primarily on vertical geochemical trends within the coal bed. Simulated maps of chemical elements derived from 45 measured sample locations across the study area provide an opportunity to observe changes in the horizontal direction within the coal bed. As the Blue Gem coal bed shows significant vertical chemical trends, care was taken in this study to try to select samples from a single, middle portion of the coal. By revealing spatial distribution patterns of elements across the middle of the bed, associations between different components of the coal can be seen. The maps therefore help to provide a picture of the coal-forming peat bog at an instant in geologic time and allow interpretation of a depositional setting in the horizontal direction. Results from this middle portion of the coal suggest an association of SiO 2 with both K 2 O and TiO 2 in different parts of the study area. Further, a pocket in the southeast of the study area shows elevated concentrations of elements attributable to observed carbonate-phase minerals (MgO, CaO, Ba and Sr) as well as elements commonly associated with sulfide-phase minerals (Cu, Mo and Ni). Areas of relatively high ash yield are observed in the north and south of the mapped area, in contrast to the low ash yields seen towards the east. Additionally, we present joint probability maps where multiple coal-quality parameters are plotted simultaneously on one figure. This application allows researchers to investigate associations of more than two components in a straight-forward manner useful in guiding resource exploration.

Kentucky↗

USGS2015

Mapping of coal quality using stochastic simulation and isometric logratio transformation with an application to a Texas lignite

Coal is a chemically complex commodity that often contains most of the natural elements in the periodic table. Coal constituents are conventionally grouped into four components (proximate analysis): fixed carbon, ash, inherent moisture, and volatile matter. These four parts, customarily measured as weight losses and expressed as percentages, share all properties and statistical challenges of compositional data. Consequently, adequate modeling should be done in terms of a logratio transformation, a requirement that is commonly overlooked by modelers. The transformation of choice is the isometric logratio transformation because of its geometrical and statistical advantages. The modeling is done through a series of realizations prepared by applying sequential simulation for the purpose of displaying the parts in maps incorporating uncertainty. The approach makes realistic assumptions and the results honor the data and basic considerations, such as percentages between 0 and 100, all four parts adding to 100% at any location in the study area, and a style of spatial fluctuation in the realizations equal to that of the data. The realizations are used to prepare different results, including probability distributions across a deposit, E-type maps displaying average properties, and probability maps summarizing joint fluctuations of several parts. Application of these maps to a lignite bed clearly delineates the deposit boundary, reveals a channel cutting across, and shows that the most favorable coal quality is to the north and deteriorates toward the southeast.

International Journal of Coal Geology↗

USGS2016

Mapping technological and biophysical capacities of watersheds to regulate floods

Flood regulation is a widely valued and studied service provided by watersheds. Flood regulation benefits people directly by decreasing the socio-economic costs of flooding and indirectly by its positive impacts on cultural (e.g., fishing) and provisioning (e.g., water supply) ecosystem services. Like other regulating ecosystem services (e.g., pollination, water purification), flood regulation is often enhanced or replaced by technology, but the relative efficacy of natural versus technological features in controlling floods has scarcely been examined. In an effort to assess flood regulation capacity for selected urban watersheds in the southeastern United States, we: (1) used long-term flood records to assess relative influence of technological and biophysical indicators on flood magnitude and duration, (2) compared the widely used runoff curve number (RCN) approach for assessing the biophysical capacity to regulate floods to an alternative approach that acknowledges land cover and soil properties separately, and (3) mapped technological and biophysical flood regulation capacities based on indicator importance-values derived for flood magnitude and duration. We found that watersheds with high biophysical (via the alternative approach) and technological capacities lengthened the duration and lowered the peak of floods. We found the RCN approach yielded results opposite that expected, possibly because it confounds soil and land cover processes, particularly in urban landscapes, while our alternative approach coherently separates these processes. Mapping biophysical (via the alternative approach) and technological capacities revealed great differences among watersheds. Our study improves on previous mapping of flood regulation by (1) incorporating technological capacity, (2) providing high spatial resolution (i.e., 10-m pixel) maps of watershed capacities, and (3) deriving importance-values for selected landscape indicators. By accounting for technology that enhances or replaces natural flood regulation, our approach enables watershed managers to make more informed choices in their flood-control investments.

Ecological Indicators↗

USGS2021

Mapping climate change vulnerability of aquatic-riparian ecosystems using decision-relevant indicators

Climate change has and is projected to continue to alter historical regimes of temperature, precipitation, and hydrology. To assess the vulnerability of climate change from a land management perspective and spatially identify where the most extreme changes are anticipated to occur, we worked in collaboration with land managers to develop a climate change vulnerability map for the midwestern United States with a focus on riparian systems. The map is intended for use by regional administrators to help them work across various program areas (e.g. fisheries, endangered species) to prioritize locations needing support for adaptation planning. The tool can also be utilized locally by managers to better understand the effects that projected climate scenarios have on the hydrology of management units as they develop adaptation strategies. The vulnerability map is watershed-based (360 watershed units within the region) and combines 15 climate change indicators that were selected by U.S. Fish and Wildlife Service natural resource managers based upon known and anticipated effects to species and habitats. The projected change in each of these indicators from the historical period (1986–2005) to the future period (2040–2059) was aggregated into a composite score for each watershed. Landscape-scale metrics reflective of a watershed’s adaptive capacity were combined with the climate change indicators to produce a vulnerability score. We found sub-regional variation in vulnerability to climate change with the greatest vulnerability in Iowa, central Illinois, and northwest Ohio. Greater vulnerability was seen in the higher greenhouse gas concentration scenario, Representative Concentration Pathway (RCP) 8.5 compared to the lower greenhouse gas concentration scenario RCP 4.5, when looking at the mean of the five downscaled climate models used in this study. By quantifying and mapping climate change vulnerability, natural resource managers can better understand the degree of vulnerability for individual watersheds and identify areas of prioritization in regional and local planning efforts.

Iowa, Illinois, Indiana, Michigan, Minnesota, Miss↗

USGS2004

Spatially quantitative seafloor habitat mapping: Example from the northern South Carolina inner continental shelf

Naturally occurring hard bottom areas provide the geological substrate that can support diverse assemblages of sessile benthic organisms, which in turn, attract many reef-dwelling fish species. Alternatively, defining the location and extent of bottom sand bodies is relevant for potential nourishment projects as well as to ensure that transient sediment does not affect reef habitats, particularly in sediment-starved continental margins. Furthermore, defining sediment transport pathways documents the effects these mobile bedforms have on proximal reef habitats. Thematic mapping of these substrates is therefore crucial in safeguarding critical habitats and offshore resources of coastal nations. This study presents the results of a spatially quantitative mapping approach based on classification of sidescan-sonar imagery. By using bottom video for image-to-ground control, digital image textural features for pattern recognition, and an artificial neural network for rapid, quantitative, multivariable decision-making, this approach resulted in recognition rates of hard bottom as high as 87%. The recognition of sand bottom was less successful (31%). This approach was applied to a large (686 km2), high-quality, 2-m resolution sidescan-sonar mosaic of the northern South Carolina inner continental shelf. Results of this analysis indicate that both surficial sand and hard bottoms of variable extent are present over the study area. In total, 59% of the imaged area was covered by hard bottom, while 41% was covered by sand. Qualitative spatial correlation between bottom type and bathymetry appears possible from comparison of our interpretive map and available bathymetry. Hard bottom areas tend to be located on flat, low-lying areas, and sandy bottoms tend to reside on areas of positive relief. Published bio-erosion rates were used to calculate the potential sediment input from the mapped hard bottom areas rendering sediment volumes that may be as high as 0.8 million m3/yr for this portion of the South Carolina coast. ?? 2003 Elsevier Ltd. All rights reserved.

South Carolina↗

USGS2024

Using climate-fire analog mapping to inform climate change adaptation strategies for wildland fire in protected areas of the conterminous US

Potential changes in wildland fire regimes due to anthropogenic climate change can be projected using data from climate models, but directly applying these meteorological variables to long-term planning and adaptive management activities may be difficult for decision makers. Analog mapping, in contrast, creates more intuitive assessments of changing fire regimes that also recognize the complex, multivariate, and multi-scalar nature of ecosystems. Here, we use data from 20 downscaled climate models under two climate forcing scenarios, Representative Concentration Pathways (RCP 4.5 and 8.5), to identify and map future climate-fire analogs for 655 protected areas in the conterminous U.S. based on annual temperature, cumulative precipitation amount and seasonality, and fire regime potentials derived from a simple process-based fire frequency model. Patterns of analogs were heavily influenced by gradients in latitude and topography, with longer time frames (end-of-century conditions) and the more extreme climate forcing scenario resulting in greater analog distances and more ensemble entropy (i.e., less consensus among climate models regarding the closest analog for a given management unit). Finer scale analyses for three protected areas (Yellowstone and Great Smoky Mountains National Parks, White Mountain National Forest) illustrate how climate-fire analog mapping can improve insight into the types of ecosystem responses that might occur under similar management conditions. Federally protected areas such as national parks, forests, and wildlife refuges have long served as reference sites for the study of fire regimes, a role that is likely to continue because many of these units are managed to allow at least some ecosystem processes to operate independently. The results suggest that analog mapping approaches are well-suited as part of qualitative assessments within climate- and fire-aware adaptive management processes. The use of analogs to depict relatable, real-world depictions of possible ecosystem changes in a given place, can help managers make more strategic choices about when and where to resist, accept, or direct climate change-driven ecological change.

Global Environmental Change Advances↗

USGS2024

Hyperspectral mapping of magmatic-hydrothermal sericite, Battle Mountain mining district, Nevada

We demonstrate the utility of multi-scale imaging spectroscopy data (hyperspectral data) to differentiate magmatic-hydrothermal sericite from other white micas based on grain size, in a case study of the Battle Mountain mining district in Humboldt and Lander Counties, Nevada. The Battle Mountain mining district contains porphyry, skarn, and distal disseminated deposits that formed in magmatic-hydrothermal systems, as well as sedimentary-rock hosted gold deposits of less certain origin. We generated spectral-based mineral maps of the Battle Mountain mining district to examine the spatial distribution of spectrally dominant minerals such as kaolinite, montmorillonite , and white micas (muscovite, sericite, and illite). We then used spectral-based mineral mapping to characterize the grain size and composition of white micas, enabling us to differentiate igneous muscovite from magmatic-hydrothermal sericite. By integrating our spectral-based maps with geologic mapping, airborne aeromagnetic data, and prior studies on mines and deposits in Battle Mountain, we determined that finer grained, higher Al sericite spatially correlated with the footprints of known magmatic-hydrothermal systems and is interpreted as forming from magmatic-hydrothermal alteration. Larger grained, lower Al muscovites are distal from known magmatic-hydrothermal systems and are interpreted as being igneous derived siliciclastic sediments that became constituents of the host sedimentary rocks. Techniques described in this contribution can be applied to any appropriate hyperspectral data, regardless of sensor type used to collect the data.

Nevada↗

USGS2013

Crater-based dating of geological units on Mars: methods and application for the new global geological map

The new, post-Viking generation of Mars orbital imaging and topographical data provide significant higher-resolution details of surface morphologies, which induced a new effort to photo-geologically map the surface of Mars at 1:20,000,000 scale. Although from unit superposition relations a relative stratigraphical framework can be compiled, it was the ambition of this mapping project to provide absolute unit age constraints through crater statistics. In this study, the crater counting method is described in detail, starting with the selection of image data, type locations (both from the mapper’s and crater counter’s perspectives) and the identification of impact craters. We describe the criteria used to validate and analyse measured crater populations, and to derive and interpret crater model ages. We provide examples of how geological information about the unit’s resurfacing history can be retrieved from crater size–frequency distributions. Three cases illustrate short-, intermediate, and long-term resurfacing histories. In addition, we introduce an interpretation-independent visualisation of the crater resurfacing history that uses the reduction of the crater population in a given size range relative to the expected population given the observed crater density at larger sizes. From a set of potential type locations, 48 areas from 22 globally mapped units were deemed suitable for crater counting. Because resurfacing ages were derived from crater statistics, these secondary ages were used to define the unit age rather than the base age. Using the methods described herein, we modelled ages that are consistent with the interpreted stratigraphy. Our derived model ages allow age assignments to be included in unit names. We discuss the limitations of using the crater dating technique for global-scale geological mapping. Finally, we present recommendations for the documentation and presentation of crater statistics in publications.

Icarus↗

USGS2017

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

ISPRS Journal of Photogrammetry and Remote Sensing↗