nasa-gesdisc/es-publications-researchareas huggingface dataset:
Understanding the legal trade of cattle and camels and the derived risk of Rift Valley Fever introduction into and transmission within Egypt (2018)

Rift Valley Fever (RVF) is a mosquito-borne zoonosis, which may cause significant losses for the livestock sector and have serious public health implications. Egypt has been repeatedly affected by RVF epidemics, mainly associated to the importation of animals from sub-Saharan countries, where the disease is endemic. The objective of our study was the improvement of the surveillance and control strategies implemented in Egypt. In order to do that, first we evaluated the legal trade of live animals into and within Egypt. Then, we assessed the risk of Rift Valley Fever virus (RVFV) transmission within the country using a multi-criteria evaluation approach. Finally, we combined the animal trade and the risk of RVFV transmission data to identify those areas and periods in which the introduction of RVFV is more likely. Our results indicate that the main risk of RVFV introduction is posed by the continuous flow of large number of camels coming from Sudan. The risk of RVFV transmission by vectors is restricted to the areas surrounding the Nile river, and does not vary significantly throughout the year. Imported camels are taken to quarantines, where the risk of RVFV transmission by vectors is generally low. Then, they are taken to animal markets or slaughterhouses, many located in populated areas, where the risk of RVFV transmission to animals or humans is much higher. The measures currently implemented (quarantines, vaccination or testing) seem to have a limited effect in reducing the risk of RVFV introduction, and therefore other (risk-based) surveillance strategies are proposed.

Research areas: Public Health, Agriculture
Amazon wildfires: Scenes from a foreseeable disaster (2020)

The Amazon forests main protection against fire is its capacity to create a moist understory microclimate. Roads, deforestation, droughts, and climate change have made this natural firebreak less effective. The southern Amazon, in particular, has become more flammable and vulnerable to wildfires during recent droughts. The drought of 1997/98 first showed that fires could escape from agricultural fields and burn standing primary forests that were once considered impenetrable to fire. The spread of forest fires during other 21st-century droughts suggests that this pattern may well be the new normal. With the landscape becoming more flammable, reducing sources of ignition and the negative effects of deforestation is crucial for avoiding severe degradation of Amazon forests. Unfortunately, recent increases in deforestation suggest that Brazil is moving in the opposite direction. Keeping pace with the rapid changes in the regions fire regimes would require innovation; cooperation across political boundaries; and interagency communication on a scale never seen before. While Brazils past success in reducing deforestation suggests that it could be an effective leader in this regard, its sluggish response to the 2019 fires tells quite a different story. But the fact remains that the future of the Amazon depends on decisive action now.

Research areas: Habitat Conversion/Fragmentation, Wildfires, Droughts
Imposing consistent global definitions of urban populations with gridded population density models: Irreconcilable differences at the national scale (2022)

No common global definition exists of urban populations, resulting in a lack of shared standards across countries for equivalent comparisons. Therefore, I used global population models of Landscan, Worldpop, and Gridded Population of the World to generate a provisional classification of population density classes to define urban and rural by human population densities, which is an enduring attribute to differentiate urban lands from rural lands. I calibrated 2015 population density models to the United Nations 2015 global urban population estimate of 53.9% and then balanced among the population models to reach approximately the same population percentages for rural, exurban, suburban, and urban thresholds. Because the three population models varied in population distribution, with the greatest concentration of population densities in the Landscan model and the greatest dispersion in the Gridded Population of the World, different urban density thresholds were necessary for each population model. After calibration, Worldpop, which is available from years 2000 to 2020, closely matched global urban population estimates during those years. However, without an inconstant definition, for example across populous countries, low urban percentages were not plausible in India simultaneously with moderate urban percentages in China and high urban percentages in the United States. All three population models with adjusted thresholds agreed on a divergent reported urbanized or rural status for 32 countries, representing about 30% of the global population, and greatly reduced urban percentages for another 13 countries. Reconsideration of the urban status of these countries, and the surrounding regions, may change the narrative of urban condition trajectories, prospects, and related applications for research, planning, and management. While population models and adjustments to population density thresholds are not perfect, omitting multifaceted social, economic, political, and demographic histories, they do create a pathway for comparison of urban status across countries on an equal basis, unlike urban definitions that vary by country.

Research areas: Habitat Conversion/Fragmentation
Characterizing Small-Town Development Using Very High Resolution Imagery (2021)

While remotely sensed images of various resolutions have been widely used in identifying changes in urban and peri-urban environments, only very high resolution (VHR) imagery is capable of providing the information needed for understanding the changes taking place in remote rural environments, due to the small footprints and low density of man-made structures in these settings. However, limited by data availability, mapping man-made structures and conducting subsequent change detections in remote areas are typically challenging and thus require a certain level of flexibility in algorithm design that takes into account the specific environmental and image conditions. In this study, we mapped all buildings and corrals for two remote villages in Mozambique based on two single-date VHR images that were taken in 2004 and 2012, respectively. Our algorithm takes advantage of the presence of shadows and, through a fusion of both spectra- and object-based analysis techniques, is able to differentiate buildings with metal and thatch roofs with high accuracy (overall accuracy of 86% and 94% for 2004 and 2012, respectively). The comparison of the mapping results between 2004 and 2012 reveals multiple lines of evidence suggesting that both villages, while differing in many aspects, have experienced substantial increases in the economic status. As a case study, our project demonstrates the capability of a coupling of VHR imagery with locally adjusted classification algorithms to infer the economic development of small, remote rural settlements.

Research areas: Habitat Conversion/Fragmentation
Mapping land use land cover change in the Lower Mekong Basin from 1997 to 2010 (2020)

The Lower Mekong Basin (LMB) is biologically diverse, economically important, and home to about 65 million people. The region has undergone extensive environmental changes since the 1990s due to such factors as agricultural expansion and intensification, deforestation, more river damming, increased urbanization, growing human populations, expansion of industrial forest plantations, plus frequent natural disasters from flooding and drought. The Mekong river is also heavily used for human transportation, fishing, drinking water, and irrigation. This paper discusses use of pre-existing LULC maps from 1997 and 2010 to derive a LMB regional LULC change map for 9 classes per date using GIS overlay techniques. The change map was derived to aid SWAT hydrologic modeling applications in the LMB, given the 2010 map is currently used in multiple LMB SWAT models, whereas the 1997 map was previously used. The 2010 LULC map was constructed from Landsat and MODIS satellite data, while the 1997 map was from before the MODIS era and therefore based on available Landsat data. The 19972010 LULC change map showed multiple trends. Permanent agriculture had expanded in certain sub-basins into previously forested areas. Some agricultural areas were converted to industrial forest plantations. Extensive forest changes also occurred in some locations, such as areas changed to shifting cultivation or permanent crops. Also, the 1997 map under classified some urban areas, whereas the 2010 LULC map showed improved identification of such areas. LULC map accuracy were assessed for 213 randomly sampled locations. The 1997 and 2010 LULC maps showed high overall agreements with reference data exceeding 87%. The LULC change map yielded a moderately high level of overall agreement (78%) that improved to 83% once LULC classification scheme specificity was reduced (forests and agriculture were each mapped as singular classes). The change map regionally showed a 4% decrease in agriculture and a 4% increase in deciduous and evergreen forests combined, though deforestation hot spot areas also were evident. The project yielded LULC map data sets that are now available for aiding additional studies that assess LMB LULC change and the impacts such change may pose to water, agriculture, forestry, and disaster management efforts. More work is needed to map, quantify and assess LULC change since 2010 and to further update the 2010 LULC map currently used in the LMB SWAT models.

Research areas: Habitat Conversion/Fragmentation, Land Surface/Agriculture Indicators
Cartographic Technique and Artifice: The Case of the Chengdu Plain (2019)

Processes of urbanisation in many parts of monsoon Asia have given rise to extensive, hybrid urban-rural regions around and between major metropolitan centres over the past two decades. Such is their novelty and complexity that the state agencies responsible for planning them have found it difficult to describe and analyse them with existing cartographic conventions. Maps produced by such agencies have become more sophisticated with the incorporation of remotely sensed satellite data. The increasing availability of dynamic and crowd-sourced data, on public social media and private/state-owned telecommunication platforms, has further sensitised agencies to the temporal dimensions of planning such regions. Even so, it has been difficult to account for the fine-grained land-use mosaic, mixture of agricultural and non-agricultural patterns, small land holdings, relatively high population densities, and spatial extension across multiple administrative jurisdictions that characterise Asian extended urban-rural regions.

Research areas: Habitat Conversion/Fragmentation, Land Surface/Agriculture Indicators
Urban hydrodynamics in the planned township of New Town, West Bengal, India (2020)

Unsustainable urbanisation has radically deteriorated the natural hydrological phenomena within cityscapes of developing countries. Such neo-urban spaces have inadvertently disrupted the hydrological balance both within and around themselves. As a result, tumultuous hydro-dynamism is being faced in atmospheric and sub-surface locales of even small planned neo-urban townships like New Town, West Bengal, wherein rainfall regimes, evapo-transpiration rates(ET), land surface temperatures(LST) and groundwater levels, have been affected. Using Built-up Index to identify the spectacular urban growth, TRMM rainfall data, MODIS 11B3 LST and MODIS 16A2 ET rates have been further correlated 3 dimensionally as well as statistically, to understand the changing hydrological regime of the township for two time periods, i.e., 2001-02 and 201819. The variables were found to be significantly related, and partial aggressors for average piezometric fall. Results from average annual estimates depict that over the past 18 years of urban growth of New Town, rainfall has decreased by 10 mm(112 mm), LST has increased by 5 units(300 Celsius), while ET values have decreased by 3units(5 mm/year). Groundwater level data acquired from the Central Groundwater Board Reports, further state that other than groundwater over-extraction by the denizens, there is a 13% chance that the 46 mbgl fall may be attributed to the changing urban hydrology. Future predictions using annual average data state that rainfall will decrease to 102 mm, while LST might escalate to around 3537 and 29-300 Celsius during day-time and night-time, respectively. Piezometric fall and ET rates will plummet to around 20-30mbgl and 2-0.8 mm/year, respectively, causing undue stress to neighbouring zones.

Research areas: Habitat Conversion/Fragmentation, Land Surface/Agriculture Indicators
Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat (2023)

Understanding changes to aboveground biomass (AGB) in forests undergoing degradation is crucial for accurately and completely quantifying carbon emissions from forest loss and for environmental monitoring in the context of climate change. Monitoring forest degradation as compared to deforestation presents technical challenges because degradation involves widespread, low-intensity AGB removal under varying temporal dynamics. Charcoal production is a key driver for forest degradation in Africa and is projected to increase in the future years. In Sub-Saharan Africa (SSA), where charcoal production drives widespread ABG removal, the utility of optical remote sensing for degradation quantification is challenged by the large inter-seasonal variation and high complexities in ecosystem structure. Limited field measurements on tree structure and aboveground biomass density (AGBD) in many parts of the SSA also impose constraints. In this study, we present a novel data fusion approach combining 3D forest structure from NASA's GEDI Lidar with optical time-series data from Landsat to quantify biomass losses associated with charcoal-related forest degradation over a 10-year time period. We used machine learning models with Landsat spectral indices from the time period of limited hydric stress (LHS) as predictor variables. By applying the best performing Random Forest (RF) model to LandTrendr-stabilized annual LHS Landsat composites, we produced annual forest AGBD maps from 2007 to 2019 over the Mabalane district in southern Mozambique where the dry forest ecosystem was under active charcoal-related degradation since 2008. The RF model achieved an RMSE value of 7.05 Mg/ha (RMSE% = 42%) and R2 value of 0.64 using a 10-fold cross-validation dataset. We quantified a total AGB loss of 2.12  0.06 Megatons (Mt) over the 10-year period, which is only 6.35  2.56% less than the total loss estimated using field-based data as previously published for the same area and time. In addition to quantifying biomass loss, we constructed annual AGBD maps that enabled the characterization of disturbance and recovery. Our framework demonstrates that fusing GEDI and Landsat data through predictive modeling can be used to quantify past forest AGBD dynamics in low biomass forests. This approach provides a satellite-based method to support REDD+ monitoring and evaluation activities in areas where field data is limited and has the potential to be extended to investigate a variety of different disturbance events.

Research areas: Habitat Conversion/Fragmentation, Energy Production/Use
Comparative study on changes of croplands between North Korea and South Korea during 1990–2015 (2018)

Studies on long-term change of cropland is of great significance to the utilization of land resources and the implementation of scientific agricultural policies. The Korean Peninsula, adjacent to China, plays an important role in the international environment of Northeast Asia. The Korean Peninsula includes South Korea and North Koreatwo countries that have a great difference in their institutions and economic developments. Therefore, we aim to quantify the spatiotemporal changes of croplands in these two countries using Landsat Thematic Imager (TM) and Operational Land Imager (OLI) imagery, and to compare the differences of cropland changes between the two countries. This paper take full advantage of ODM approach (object-oriented segmentation and decision-tree classification based on multi-season imageries) to obtain the distribution of croplands in 1990 and 2015. Results showed that the overall classification accuracy of cropland data is 91.10% in 1990 and 92.52% in 2015. The croplands were mainly distributed in areas with slopes that were less than 8 and with elevations that were less than 300 m in the Korean Peninsula. However, in other region (slope > 8 or elevation > 300 m), the area and proportion of North Koreas croplands were significantly higher than that of South Korea. Croplands significantly increased by 15.02% in North Korea from 1990 to 2015. In contrast, croplands in South Korea slightly decreased by 1.32%. During the 25 years, policy shift, economic development, population growth, and urban sprawl played primary roles for cropland changes. Additionally, the regional differences of cropland changes were mainly due to different agriculture policies implemented by different countries. The achievements of this study can provide scientific guidance for the protection and sustainability of land resources.

Research areas: Habitat Conversion/Fragmentation, Agriculture
Global observed and modelled impacts of irrigation on surface (2019)

Agricultural irrigation has significant potential for altering local climate by reducing soil albedo, increasing evapotranspiration, and enabling greater leaf area. Numerous studies using regional or global climate models have demonstrated the cooling effects of irrigation on mean and extreme temperature, especially over regions where irrigation is extensive. However, these modelbased results have not been well validated due to the limitations of observational data sets. In this study, multiple satellitebased products, including the Moderate Resolution Imaging Spectroradiometer (MODIS) and Soil Moisture Active Passive (SMAP) data sets, are used to isolate and quantify the local impacts of irrigation on surface climate over irrigated regions, which are derived from the Global Map of Irrigation Areas (GMIA). The relationships among soil moisture, albedo, evapotranspiration, and surface temperature are explored. Strong evaporative cooling by irrigation lowers daytime surface temperature over arid and semiarid regions, such as California's Central Valley, the Great Plains, central Asia, and northwestern India. However, the cooling effects are less evident in areas of eastern China and the Lower Mississippi River Basin despite extensive irrigation over these regions. Results are also compared with irrigation experiments using the Community Earth System Model (CESM) to assess the model's ability to represent landatmosphere interactions in regards to irrigation. CESM greatly underestimates the surface temperature response to irrigation. The comparison between the offline and coupled simulations suggests that the irrigationinduced cooling can be regulated by the interactions between land surface and atmosphere, and amplified signals are found over the hot spot regions. Meanwhile, model resolution can also influence the magnitude of the local cooling by irrigation.

Research areas: Habitat Conversion/Fragmentation, Land Surface/Agriculture Indicators
Observations of increased cloud cover over irrigated agriculture in an arid environment (2017)

Abstract Irrigated agriculture accounts for 20% of global cropland area and may alter climate locally and globally, but feedbacks on clouds and rainfall remain highly uncertain, particularly in arid regions. Nonrenewable groundwater in arid regions accounts for 20% of global irrigation water demand, and quantifying these feedbacks is crucial for the prediction of long-term water use in a changing climate. Here, satellite data are used to show how irrigated crops in an arid environment alter land surface properties, cloud cover, and rainfall patterns. Land surface temperatures (LSTs) over the cropland are 57 K lower than their surroundings, despite a lower albedo, suggesting that Bowen ratio is strongly reduced (and latent heat fluxes increased) over the irrigated cropland. Daytime cloud cover is increased by up to 15% points (a relative increase of 60%), with increased cloud development in the morning and a greater afternoon peak in cloud. Cloud cover is significantly correlated with interannual variations in vegetation and LST. Afternoon rainfall also appears to be enhanced around the irrigation. The cloud feedback is the opposite of what has been previously observed in tropical and semiarid regions, suggesting different processes drive landatmosphere feedbacks in very dry environments. Increased cloud and rainfall, and associated increases in diffuse radiation and reductions in temperature, are likely to benefit vegetation growth. Predictions of changes in crop productivity due to climate change and the impacts of global land-use change on climate and the use of water resources would therefore benefit from including these effects.

Research areas: Habitat Conversion/Fragmentation, Land Surface/Agriculture Indicators, Agriculture
Near “real-time” estimation of excess commuting from open-source data: Evidence from China's megacities (2021)

Urban commuting has continuously fascinated scholars and decision-makers. As few people live and work in the same place, there is always excess commuting (i.e., the non-optimal or surplus work travel occurring in cities because people do not minimize their journeys to work for most residents). Traditional commuting data sources (e.g., questionnaires and census surveys) are challenged by small samples, high cost, and low spatiotemporal resolution. In contrast, the big social-sensing data (e.g., smart card and mobile phone data) only consider one or two traffic mode of a route, which is not consistent with the real-life condition. This article proposes a framework for modeling excess commuting based on open-source data of the ten most populous megacities in China. We downloaded residential points of interest (POIs) from Lianjia Real Estate website and obtained workplace POIs from China's AMAP, which is widespread used as Google map. The stratified sampling approach was employed to derive commuting pairs. Both commuting distance and time were obtained by the shortest path under public transportation from AMAP. Then, the linear programming method was employed to calculate the theoretical minimum commuting time and distance of each city. We analyzed the statistical property and spatial distributions of excess commuting and found that (1) commuting distances and time (ranging from 9.1 to18.1 km and from 44.8 to 74.3  minutes) of all ten megacities follow a left-skewed normal distribution; (2) in terms of commute cost, all cities show universal core-periphery patterns where the spatial heterogeneity of the commuting time is more significant than that of distance; (3) for each city, the excess commuting measured by time (i.e. from 0.61 to 0.79) is lower than that measured by distance (i.e. 0.68 to 0.89); and (4) the role of mixing land use, waterbody distribution, and centripetal urbanization on urban commuting distance and time is significant.

Research areas: Habitat Conversion/Fragmentation