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At least 37 records · Page 2

Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 sq.km. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June–September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.

Katie Fankhauser↗

Iona Ecological Conservation: Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola

Following the end of the Angolan civil war in 2002, human and livestock populations have increased exponentially within Iona National Park. An ongoing drought since 2017 has brought these people and livestock into increasing competition with local wildlife for resources – highlighting a conservation challenge that will become more entrenched as the effects of anthropogenic climate change increase. In 2019, African Parks began co-managing Iona National Park in Angola with the Angolan government, hoping to enact scientifically grounded management strategies to meet this challenge. To accomplish this, African Parks needed contemporary and historic information on the spatial distribution of landcover types within Iona and adjacent areas. We constructed and applied a Random Forest classifier in Google Earth Engine to multispectral imagery gathered from Landsat 5, 7, 8 and Sentinel-1 and 2 to meet this need. Using the classifier, we generated a time-series of land cover maps between 1990–2023, from which landscape metrics and change detection analysis were calculated to show how certain habitats and formations had changed over time. The resulting maps have producer and user’s accuracies above 87% and show four broad landcover regions within the study area. Notably, we observed a decrease in the park’s diversity as per the Shannon Diversity Index – an index that considers the richness of classes, as well the evenness of their distribution. A lack of arid specific land cover indices and ground-truthed training data from earlier years limited the accuracy and resolution of our landcover maps. However, this project still demonstrates that Earth observations can be used to form the basis of conservation policy in arid environments, where ground-truth data may be difficult to obtain or non-existent.

remote sensing↗

Monitoring Vegetation Impacts of Night Penning in Idaho and Oregon Grazing Allotments Using Remote Sensing

The practice of night penning, which involves corralling livestock into a fenced, secure area overnight, aims to safeguard animals from predators. Although this method has proven successful in minimizing interactions between livestock and wildlife, the extent of its impact on vegetation communities is still being explored. In this study, we used the Rangeland Analysis Platform (RAP), which aggregates spatially distributed values of vegetation composition, cover, and productivity from the Landsat missions, to quantify the effects of night penning on rangeland vegetation communities. We identified control sites that had similar topographic characteristics to night pen sites used by ranchers in Idaho and Oregon. We then quantified and compared changes in annual net primary productivity (NPP) between night pen and control sites. Our study found that RAP NPP data did not show a noticeable difference between control sites and night penning sites. When analyzed quantitatively, night pen sites showed variability in NPP before and after night penning; however, there were few discernible differences between night pen sites and control sites, suggesting that climate conditions can have more of an effect on NPP measurements compared to land management decisions. In the summer of 2024, field crews from the Natural Resource Ecology Laboratory collected biomass data from these rangeland sites, and this will be used to compare and validate RAP products while continuing to investigate the effects of night penning.

Truman Anarella↗

Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola

Following the end of the Angolan Civil War (1975-2002), human habitation in Iona National Park has grown exponentially, as has the livestock population. An ongoing drought beginning in 2017 has brought people, livestock, and wildlife into increasing competition for resources within the park. This study used Earth observation data, primarily Landsat and Sentinel imagery, to examine landscape trends to improve wildlife preservation approaches in Iona National Park, Angola. In collaboration with the NGO African Parks, we developed a robust land use and land cover (LULC) classification model using remote sensing data to augment sparse ground-based data in this arid land region. We used Google Earth Engine and a random forest classifier to map vegetation types, water bodies, and potential wildlife habitats. This analysis resulted in a high spatial resolution LULC time-series between 1984-2023, highlighting critical periods of socioecological change over the past 40 years. These results increased the partner’s ability to make scientifically grounded decisions about resource allocation and conservation priorities. This analysis supports the feasibility of applying remote sensing techniques coupled with machine learning models in dry regions, where standard survey methods are frequently limited by accessibility and resource availability. However, we identified limitations in ground-truth data and the difficulty of recognizing certain vegetation types in arid areas. Despite these limitations, the study demonstrated Earth observations' ability to transform wildlife management techniques in distant and data-scarce locations, providing a reproducible foundation for similar ecosystems around the world.

Emmanuel Aklie↗

Greenhouse Gas Emissions from Food Systems: Building the Evidence Base

New estimates of greenhouse gas (GHG) emissions from the food system were developed at the country level, for the period 1990–2018, integrating data from crop and livestock production, on-farm energy use, land use and land use change, domestic food transport and food waste disposal. With these new country-level components in place, and by adding global and regional estimates of energy use in food supply chains, we estimate that total GHG emissions from the food system were about 16 CO2eq yr−1 in 2018, or one-third of the global anthropogenic total. Three quarters of these emissions, 13 Gt CO2eq yr−1, were generated either within the farm gate or in pre- and post-production activities, such as manufacturing, transport, processing, and waste disposal. The remainder was generated through land use change at the conversion boundaries of natural ecosystems to agricultural land. Results further indicate that pre- and post-production emissions were proportionally more important in developed than in developing countries, and that during 1990–2018, land use change emissions decreased while pre- and post-production emissions increased. We also report results on a per capita basis, showing world total food systems per capita emissions decreasing during 1990–2018 from 2.9 to 2.2 t CO2eq cap−1, with per capita emissions in developed countries about twice those in developing countries in 2018. Our findings also highlight that conventional IPCC categories, used by countries to report emissions in the National GHG inventory, systematically underestimate the contribution of the food system to total anthropogenic emissions. We provide a comparative mapping of food system categories and activities in order to better quantify food-related emissions in national reporting and identify mitigation opportunities across the entire food system.

greenhouse gas emissions↗

Future Climate Change Impacts on U.S. Agricultural Yields, Production, and Market

This study provides estimates of climate change impacts on U.S. agricultural yields and the agricultural economy through the end of the 21st century, utilizing multiple climate scenarios. Results from a process-based crop model project future increases in wheat, grassland, and soybean yield due to climate change and atmospheric CO2 change; corn and sorghum show more muted responses. Results using yields from econometric models show less positive results. Both the econometric and process-based models tend to show more positive yields by the end of the century than several other similar studies. Using the process-based model to provide future yield estimates to an integrated agricultural sector model, the welfare gain is roughly $16B/year (2019 USD) for domestic producers and $6.2B/year for international trade, but domestic consumers lose $10.6B/year, resulting in a total welfare gain of $11.7B/year. When yield projections for major crops are drawn instead from econometric models, total welfare losses of more than $28B/year arise. Simulations using the process-based model as input to the agricultural sector model show large future production increases for soybean, wheat, and sorghum and large price reductions for corn and wheat. The most important factors are those about economic growth, flooding, international trade, and the type of yield model used. Somewhat less, but not insignificant factors include adaptation, livestock productivity, and damages from surface ozone, waterlogging, and pests and diseases.

Agricultural sector↗

Chapter 7: Food systems

Food systems - including food production, distribution, consumption, and waste disposal - are critical to sustaining livelihoods and delivering nutrition worldwide. However, food systems contribute significantly to the climate crisis, accounting for over 30 percent of human-caused global greenhouse gas emissions (e.g., methane, carbon dioxide, and nitrous oxide). Climate change, in turn, has a significant and growing impact on food systems. Climate change increases heat stress and can lead to deteriorating soil health, for example, slowing agricultural productivity and reducing the nutritional content of crops and livestock. The cascading impacts of climate extremes on agricultural production are likely to destabilize global food security, endangering the livelihoods of billions of people and threatening public health.

Food systems↗

Use of satellite imagery for wildland resource evaluation

The author has identified the following significant results. Accurate identification and delineation of crested wheatgrass seedlings has enabled a broad inventory of this resource. The entire state of Nevada is being inventoried for crested wheatgrass seedlings. Irrigated fields and pastures are easily visible from ERTS-1 imagery and were quantified in total acres on 12,500 square miles of the state. Recent fire scars may be monitored and inventoried from satellite-borne imagery. Inventory and quantification of large native meadows of Nevada have been accomplished on one frame of ERTS-1 data. This inventory would not have been economically feasible with any known ground inventory method. The U-2 sequential data taken in the spring revealed several resource management oriented phenological changes in the vegetation. The green-up of grasses and shrubs was detected on the imagery and supplied a good indicator for livestock turn-out dates. Water level manipulations in the Ruby Marsh were readily detected by noting changes in vegetation growth and reflectance.

Tueller, P. T.↗

Observations of the plant growth and annual flooding in the inland delta of the Niger River, west Africa

The author has identified the following significant results. A large potential irrigation area with water delivery system in place was identified from ERTS-1 imagery. The site is in the Sahara Desert. Color additive change detection imagery was used to assess ground water potentials in the Savannah in conjunction with a major US AID-Mali livestock sector initiative. The potential capacity to map laterite deposits in the Inland Delta from space was established with the help of aerial and ground surveys. A color composite of the Inland Delta could have saved French biogeographers 3 5/6 man-years of professional field work had it been available, according to the man who prepared a plant community map of part of the southern Inland Delta.

Macleod, N. H.↗

Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation

The author has identified the following significant results. Emphasis has been given to an inventory of land resource types and land use at the ten Great Plains Corridor test sites. A resource and land use classification system was developed which uses available soil survey information and interpretations from NASA obtained high flight aerial photography to locate discrete areas of similar rangeland vegetation. Existing classification systems, even those developed for use with remote sensor data, were found to be inadequate for this project. This system is expected to be of general use for remote sensing related to land use and management. It has specific applicability to any effort aimed at regional use of ERTS-1 MSS digital data products. A preliminary assessment of the relative importance of rangelands in the Great Plains Corridor states indicates that the value of the livestock industry supported by this resource exceeds 23 billion dollars. The development of a Rangeland Feed Conditions index for this region could be used by more than 400,000 farm and ranch operators involved in the production of more than 40% of the nation's beef and much of the country's grain.

Rouse, J. W., Jr.↗

Remote sensing in agriculture

Some examples are presented of the use of remote sensing in cultivated crops, forestry, and range management. Areas of concern include: the determination of crop areas and types, prediction of yield, and detection of disease; the determination of forest areas and types, timber volume estimation, detection of insect and disease attack, and forest fires; and the determination of range conditions and inventory, and livestock inventory. Articles in the literature are summarized and specific examples of work being performed at the Marshall Space Flight Center are given. Primarily, aerial photographs and photo-like ERTS images are considered.

Downs, S. W., Jr.↗

Soil, water, and vegetation conditions in south Texas

The author has identified the following significant results. To distinguish dead from live vegetation, spectrophotometrically measured infinite reflectance of dead and live corn (Zea mays L.) leaves were compared over the 0.5 to 2.5 micron waveband. Dead leaf reflectance was reached over the entire 0.5 to 2.5 micron waveband by stacking only two to three leaves. Live leaf reflectance was attained by stacking two leaves for the 0.5 to 0.75 micron waveband (chlorophyll absorption region), eight leaves for the 0.75 to 1.35 micron waveband (near infrared region), and three leaves for the 1.35 to 2.5 micron waveband (water absorption region). LANDSAT-1 MSS digital data for 11 December 1973 overpass were used to estimate the sugar cane acreage in Hidalgo County. The computer aided estimate was 22,100 acres compared with the Texas Crop and Livestock Reporting Service estimate of 20,500 acres for the 1973-'74 crop year. Although there were errors of omission from harvested fields that were identified as bare soil and some citrus and native vegetation that were mistakenly identified as sugar cane, the mapped location of sugar cane fields in the county compared favorably with their location on the thematic map generated by the computer.

Wiegand, C. L.↗

Wheat productivity estimates using LANDSAT data

The author has identified the following significant results. Large area LANDSAT yield estimates were generated. These results were compared with estimates computed using a meteorological yield model (CCEA). Both of these estimates were compared with Kansas Crop and Livestock Reporting Service (KCLRS) estimates of yield, in an attempt to assess the relative and absolute accuracy of the LANDSAT and CCEA estimates. Results were inconclusive. A large area direct wheat prediction procedure was implemented. Initial results have produced a wheat production estimate comparable with the KCLRS estimate.

Nalepka, R. F.↗

Application of LANDSAT images to the study of level soils for recognizing drainage areas

Photographic images from LANDSAT 1 were applied to the study of soil in Desaguadero, Bolivia, in order to locate areas with high agricultural and livestock potential. Photointerpretation techniques were emphasized and advantages of information obtained via multispectral satellite images in various bands and combinations were demonstrated.

Espinoza, M. U.↗

Developing closed life support systems for large space habitats

In anticipation of possible large-scale, long-duration space missions which may be conducted in the future, NASA has begun to investigate the research and technology development requirements to create life support systems for large space habitats. An analysis suggests the feasibility of a regeneration of food in missions which exceed four years duration. Regeneration of food in space may be justified for missions of shorter duration when large crews must be supported at remote sites such as lunar bases and space manufacturing facilities. It is thought that biological components consisting principally of traditional crop and livestock species will prove to be the most acceptable means of closing the food cycle. A description is presented of the preliminary results of a study of potential biological components for large space habitats. Attention is given to controlled ecosystems, Russian life support system research, controlled-environment agriculture, and the social aspects of the life-support system.

Phillips, J. M.↗

Market assessment of photovoltaic power systems for agricultural applications in the Philippines

The market potential in the Philippines for stand alone photovoltaic (P/V) systems in agriculture was assessed. Applications include: irrigation, postharvest operation, food and fiber processing and storage, and livestock and fisheries operations. Power and energy use profiles for many applications as well as assessments of business, government and financial climate for P/V sales are described. Many characteristics of the Philippine agriculture and energy sector favorably influence the use of P/V systems. However, serious and significant barriers prevent achieving the technically feasible, cost competitive market for P/V systems in the agricultural sector. The reason for the small market is the limited availability capital for financing P/V systems. It is suggested that innovative financing schemes and promotional campaigns should be devised.

Cabraal, R. A.↗

Market assessment of photovoltaic power systems for agricultural applications in Colombia

The market potential for photovoltaic systems in the agricultural sector of Colombia is assessed. Consideration was given to over twenty specific livestock production, crop production, and rural services applications requiring less than 15 kW of power without backup power. Analysis revealed that near-term potential exists for photovoltaic technology in applications in coffee depulging, cattle watering, rural domestic users, rural water supply and small irrigation, rural telephones, rural health posts, and vaccine refrigeration. Market size would be in the 1200 to 2500 kWp range in the 1981 to 86 timeframe. Positive factors influencing the market size include a lack of electrical services, potential for developing the Llanos Orientales Territory, high fuel costs in remote areas, balance of system availability, the presence of wealthy land owners, and a large government-sponsored contract for photovoltaic (PV)-powered rural telephone systems. The anticipated eligibility of photovoltaic equipment for loans would be a further positive factor in market potential. Important negative factors include relatively inexpensive energy in developed locations, reliance on hydropower, lack of familiarity with PV equipment, a lack of financing, and established foreign competition in PV technology. Recommendations to American PV manufacturers attempting to develop the Colombian market are given.

Steigelmann, W.↗