nasa-gesdisc/es-publications-researchareas huggingface dataset:
Assessing the Influence of Tourism-Driven Activities on Environmental (2020)

Tourism is a primary socio-economic factor on many coastal islands. Tourism contributes to the livelihoods of the residents, but also influences natural resources and energy consumption and can become a significant driver of land conversion and environmental change. Understanding the influence of tourist-related activities is vital for sustainable tourism development. We chose Hainan Island in South China as a research area to study the influence of tourist-driven activities on environmental variables (as Land Surface Temperatures (LST) and related ecosystem variables) during the period of 2000 to 2019. In Hainan, the local economy relies heavily on tourism, with an ever-growing influx of tourists each year. We categorised location-based points of interest (POIs) into two classes, non-tourism sites and tourism-related sites, and utilised satellite data from the cloud-based platform Google Earth Engine (GEE) to extract LST and Normalized Difference Vegetation Index (NDVI) data. We analysed the LST variations, NDVI changes and the land use/land cover (LULC) changes and compared the relative difference in LST and NDVI between the tourism-related sites and non-tourism-related sites. The main findings of this study were: (1) The median LST in the tourism-related sites was relatively higher (1.3) than the LST in the non-tourism-related sites for the 20 years. Moreover, every annual mean LST of tourism-related sites was higher than the LST values in non-tourism-related sites, with an average difference of 1.2 C for the 20 years and a maximum difference of 1.7 C. We found higher annual LST anomalies for tourist-related sites compared to non-tourism sites after 2010, which indicated the likely positive differences in LST above the average LST during 20 years for tourism-related sites when compared against the non-tourism related sites, thus highlighting the potential influence of tourism activities on LST. (2) The annual mean NDVI value for tourism-related sites was significantly lower than for non-tourism places every year, with an average NDVI difference of 0.26 between the two sites. (3) The land cover changed significantly: croplands and forests reduced by 3.5% and 2.8% respectively, while the areas covered by orchards and urban areas increased by 2% and 72.3% respectively. These results indicate the influence of the tourism-driven activities includes the relatively high LST, vegetation degradation and land-use conversion particular to urban cover type. The outcome of this work provides a method that combines cloud-based satellite-derived data with location-based POIs data for quantifying the long-term influence of tourism-related activities on sensitive coastal ecosystems. It contributes to designing evidence-driven management plans and policies for the sustainable tourism development in coastal areas.

Research areas: Land Surface/Agriculture Indicators, Habitat Conversion/Fragmentation
Analyzing Satellite-Derived 3D Building Inventories and Quantifying (2022)

Earth observation (EO) data can provide large scale, high-resolution, and transferable methodologies to quantify the sprawl and vertical development of cities and are required to inform disaster risk reduction strategies for current and future populations. We synthesize the evolution of Bishkek, Kyrgyzstan, which experiences high seismic hazard, and derive new datasets relevant for seismic risk modeling. First, the urban sprawl of Bishkek (19792021) was quantified using built-up area land cover classifications. Second, a change detection methodology was applied to a declassified KeyHole Hexagon (KH-9) and Sentinel-2 satellite image to detect areas of redevelopment within Bishkek. Finally, vertical development was quantified using multi-temporal high-resolution stereo and tri-stereo satellite imagery, which were used in a deep learning workflow to extract buildings footprints and assign building heights. Our results revealed urban growth of 139 km2 (92%) and redevelopment of ~26% (59 km2) of the city (19792021). The trends of urban growth were not reflected in all the open access global settlement footprint products that were evaluated. Building polygons that were extracted using a deep learning workflow applied to high-resolution tri-stereo (Pleiades) satellite imagery were most accurate (F1 score = 0.70) compared to stereo (WorldView-2) imagery (F1 score = 0.61). Similarly, building heights extracted using a Pleiades-derived digital elevation model were most comparable to independent measurements obtained using ICESat-2 altimetry data and field-measurements (normalized absolute median deviation < 1 m). Across different areas of the city, our analysis suggested rates of building growth in the region of 200010,700 buildings per year, which when combined with a trend of urban growth towards active faults highlights the importance of up-to-date building stock exposure data in areas of seismic hazard. Deep learning methodologies applied to high-resolution imagery are a valuable monitoring tool for building stock, especially where country-level or open-source datasets are lacking or incomplete.

Research areas: Habitat Conversion/Fragmentation
Land-use change and communal conflicts in Sub-Saharan Africa (2017)

This paper addresses the correlation between land-use changes and communal conflicts in Sub-Saharan Africa. In particular, by articulating the analysis on a 0.5  0.5 regular grid, we estimate the probability of a communal conflict event at time t in any given cell i as a function of a set of variables for land-use variations, a spatial and a temporal lag of the dependent variable, a series of climate attributes and time-invariant geo-physical attributes (all measured at cell level), and a set of institutional attributes (measured at country level). The results show that a positive variation in land-use for agricultural and urban purposes significantly increases both the probability and the number of communal conflicts. On the contrary, the impact of increasing pasture land is not so clear, since results supporting the evidence of a negative relation between pasture use and communal conflicts risk are not robust to different model specifications.

Research areas: Habitat Conversion/Fragmentation
Evaluation of Terrestrial Water Storage Changes and Major Driving (2022)

Quantitative assessment of the terrestrial water storage (TWS) changes and the major driving factors have been hindered by the lack of direct observations in Inner Mongolia, China. In this study, the spatial and temporal changes of TWS and groundwater storage (GWS) in Inner Mongolia during 20032021 were evaluated using the satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow On combined with data from land surface models. The results indicated that Inner Mongolia has experienced a widespread TWS loss of approximately 1.82 mm/yr from 20032021, with a more severe depletion rate of 4.15 mm/yr for GWS. Meteorological factors were the driving factors for water storage changes in northeastern and western regions. The abundant precipitation increased TWS in northeast regions at 2.36 mm/yr. Anthropogenic activities (agricultural irrigation and coal mining) were the driving factors for water resource decline in the middle and eastern regions (especially in the agropastoral transitional zone), where the decrease rates were 4.09 mm/yr and 3.69 mm/yr, respectively. In addition, the severities of hydrological drought events were identified based on water storage deficits, with average severity values of 17 mm, 18 mm, 24 mm, and 33 mm for the west, middle, east, and northeast regions, respectively. This study established a basic framework for water resource changes in Inner Mongolia and provided a scientific foundation for further water resources investigation.

Research areas: Land Surface/Agriculture Indicators
A novel method to extract urban human settlements by integrating remote sensing and mobile phone locations (2020)

Satellite-based human settlement extraction methods have limited practical applications, due to merely studying the difference between human settlements and other land cover/use types in physical attributes (e.g., spectral signature and land surface temperature) instead of considering basic anthropogenic attributes (e.g., human distribution and human activities). To deal with this challenge, we proposed a novel method to accurately extract human settlements by integrating mobile phone locating-request (MPL) data and remotely sensed data. In this study, human settlements for selected cities were mapped at a medium resolution (30 m) by redistributing the MPL data using Landsat Normalized Difference Vegetation Index (NDVI) adjusted weights, with an overall accuracy of above 90.0%. Additionally, by extending the proposed method to the MPL and Moderate Resolution Imaging Spectroradiometer (MODIS) data, a coarse-resolution (250 m) map of human settlements in China was created with an overall accuracy of 95.2%. Compared with the widely used nighttime light based methods, the proposed method could solve the long-existing problems such as data saturation and blooming effects, as well as characterizing human settlements with fine spatial details. Our study provides an alternative approach to human settlement extraction by combining its physical and anthropogenic attributes, and it can be easily adjusted with multi-scale remotely sensed data and applied to human settlement extraction at different scales.

Research areas: Habitat Conversion/Fragmentation
World Settlement Footprint 3D-A first three-dimensional survey of the global building stock (2022)

Settlements, and in particular cities, are at the center of key future challenges related to global change and sustainable development. Widely used indicators to assess the efficiency and sustainability of settlement development are the compactness and density of the built-up area. However, at global scale, a temporally consistent and spatially detailed survey of the distribution and concentration of the building stock  meaning the total area and volume of buildings within a defined spatial unit or settlement, commonly referred to as building density  does not yet exist. To fill this data and knowledge gap, an approach was developed to map key characteristics of the world's building stock in a so far unprecedented level of spatial detail for every single settlement on our planet. The resulting World Settlement Footprint 3D dataset quantifies the fraction, total area, average height, and total volume of buildings for a measuring grid with 90 m cell size. The World Settlement Footprint 3D is generated using a modified version of the World Settlement Footprint human settlements mask derived from Sentinel-1 and Sentinel-2 satellite imagery at 10 m spatial resolution, in combination with 12 m digital elevation data and radar imagery collected by the TanDEM-X mission. The underlying, automated processing framework includes three basic workflows: one estimating the mean building height based on an analysis of height differences along potential building edges, a second module determining the building fraction and total building area within each 90 m cell, and a third part combining the height information and building area in order to determine the average height and total built-up volume at 90 m gridding. Optionally, a simple 3D building model (level of detail 1) can be generated for regions where data on the building footprints is available. A comprehensive validation campaign based on 3D building models obtained for 19 regions (~86,000 km2) and street-view samples indicating the number of floors for >130,000 individual buildings in 15 additional cities documents that the novel World Settlement Footprint 3D data provides valuable and, for the first time, globally consistent information on key characteristics of the building stock in both, large urban agglomerations as well as small-scale rural settlements. Thus, the new dataset represents a promising baseline dataset for a wide range of previously impossible environmental, socioeconomic, and climatological studies worldwide.

Research areas: Habitat Conversion/Fragmentation
Divergent projections of future land use in the United States arising from different models and scenarios (2016)

A variety of land-use and land-cover (LULC) models operating at scales from local to global have been developed in recent years, including a number of models that provide spatially explicit, multi-class LULC projections for the conterminous United States. This diversity of modeling approaches raises the question: how consistent are their projections of future land use? We compared projections from six LULC modeling applications for the United States and assessed quantitative, spatial, and conceptual inconsistencies. Each set of projections provided multiple scenarios covering a period from roughly 2000 to 2050. Given the unique spatial, thematic, and temporal characteristics of each set of projections, individual projections were aggregated to a common set of basic, generalized LULC classes (i.e., cropland, pasture, forest, range, and urban) and summarized at the county level across the conterminous United States. We found very little agreement in projected future LULC trends and patterns among the different models. Variability among scenarios for a given model was generally lower than variability among different models, in terms of both trends in the amounts of basic LULC classes and their projected spatial patterns. Even when different models assessed the same purported scenario, model projections varied substantially. Projections of agricultural trends were often far above the maximum historical amounts, raising concerns about the realism of the projections. Comparisons among models were hindered by major discrepancies in categorical definitions, and suggest a need for standardization of historical LULC data sources. To capture a broader range of uncertainties, ensemble modeling approaches are also recommended. However, the vast inconsistencies among LULC models raise questions about the theoretical and conceptual underpinnings of current modeling approaches. Given the substantial effects that land-use change can have on ecological and societal processes, there is a need for improvement in LULC theory and modeling capabilities to improve acceptance and use of regional- to national-scale LULC projections for the United States and elsewhere.

Research areas: Habitat Conversion/Fragmentation
Environmental justice and US Forest Service hazardous fuels reduction: A spatial method for impact assessment of federal resource management actions (2018)

Natural resource managers of federal lands in the USA are often tasked with various forms of social and economic impact analysis. Federal agencies in the USA also have a mandate to analyze the potential environmental justice consequences of their activities. Relatively little is known about the environmental justice impacts of natural resource management in rural areas. Quantitative environmental justice analyses have so far heavily favored urban populations, in part owing to the difficulty of quantitative analysis of rural U.S. Census data. We developed a spatial method for integrating rural U.S. Census data with natural resource management data to address this gap. The method learns from methodological advances in overcoming the spatial limitations of Census data, but prioritizes a simple, efficient technique that is applicable not only for identifying potential environmental justice problems, but also to a potentially broad spectrum of natural resource management activities and spatial scales. We pilot test the method by analyzing the hazardous fuels reduction activities of two national forests in central Oregon, USA. We find no evidence of systematic environmental justice issues on either forest, but identify local areas that warrant additional investigation.

Research areas: Habitat Conversion/Fragmentation
Comparative quantification of local climate regulation by green and blue urban areas in cities across Europe (2021)

Urban growth alters environmental conditions with major consequences for climate regulation and the exposure of population to heat. Nature-based solutions may be used to alleviate the increasing urban climate pressures, but the climate regulation services that these solutions can supply for and across different urban conditions remains understudied. We comparatively investigate the urban ecosystem service realization (considering the ecosystem service supply and demand spatial interactions) of local climate regulation by vegetated (green) and water-covered (blue) areas across 660 European cities. Results show relatively robust power-law relationships with city population density (average R2 of 0.34) of main indicators of ecosystem service realization. Country-wise fitting for city-average indicators strengthens these relationships, in particular for western European cities (average R2 of 0.66). Cross-city results also show strong power-law relationship of effectiveness in ecosystem service realization with socio-economic measures like Human Development Index and GPD per capita, in particular for the area fraction of city parts with high ecosystem service realization (R2 of 0.77). The quantified relationships are useful for comparative understanding of differences in ecosystem services realization between cities and city parts, and quantitative projection of possible change trends under different types of city growth so that relevant measures can be taken to counteract undesirable trends.

Research areas: Habitat Conversion/Fragmentation
Characterization of water use and water balance for the croplands of Kansas using satellite, climate, and irrigation data (2021)

Kansas is one of the most productive agricultural states in the United States, where agricultural irrigation is a primary user of underground and surface water. Because of low precipitation and declining groundwater levels in western and central Kansas, sustainable management of irrigation water resources is a critical issue in the agricultural productivity of the state. The objective of this study is to analyze and characterize the water use and water balance in the croplands of Kansas using satellite observations, meteorological data, and in situ irrigation water use records. We used actual evapotranspiration (ETa), precipitation, soil moisture, and irrigation water use to calculate water balance for Kansas in 2015 at scales of counties, climatic divisions, and groundwater management districts (GMD). The Operational Simplified Surface Energy Balance model was implemented to estimate 30-m resolution ETa. Results showed that the seasonal (May  September) precipitation, soil water storage change, and ETa are 528 mm, 80 mm, and 555 mm, respectively, on average of all croplands in the state. The annual net irrigation water consumption was 293 mm for irrigated croplands, indicating that irrigation water constitutes an substantial portion of the water supply in the state. The total volumetric irrigation water use was 3.24 km3 for all croplands within five GMDs in western and south-central Kansas, while only 0.38 km3 was outside of GMDs. The multiple regression models of ETa against precipitation and irrigation water use were statistically significant with R2 values of 0.71 and 0.87, respectively, at county and climate division scales. Regression models also indicated a higher rate of ETa response to irrigation water use than that to precipitation. Our study demonstrated the spatial patterns of crop water use and water balance in Kansas, which could provide useful information for management of irrigation agriculture and water resources for the state.

Research areas: Habitat Conversion/Fragmentation
Importance of the forest state in estimating biomass losses from tropical forests: combining dynamic forest models and remote sensing (2022)

Abstract. Disturbances, such as extreme weather events, fires,
floods, and biotic agents, can have strong impacts on the dynamics and
structures of tropical forests. In the future, the intensity of disturbances
will likely further increase, which may have more serious consequences for
tropical forests than those we have already observed. Thus, quantifying
aboveground biomass loss of forest stands due to stem mortality (hereafter
biomass loss rate) is important for the estimation of the role of tropical
forests in the global carbon cycle. So far, the long-term impacts of altered
stem mortality on rates of biomass loss have not been adequately described. This study aims to analyse the consequences of long-term elevated stem
mortality rates on forest dynamics and biomass loss rate. We applied an
individual-based forest model and investigated the impacts of permanently
increased stem mortality rates on the growth dynamics of humid, terra firme
forests in French Guiana. Here, we focused on biomass, leaf area index
(LAI), forest height, productivity, forest age, quadratic mean stem
diameter, and biomass loss rate. Based on the simulation data, we developed
a multiple linear regression model to estimate biomass loss rates of forests
in different successional states from the various forest attributes. The findings of our simulation study indicated that increased stem mortality
altered the succession patterns of forests in favour of fast-growing
species, which increased the old-growth forests' gross primary production,
though net primary production remained stable. The stem mortality rate had a
strong influence on the functional species composition and tree size
distribution, which led to lower values in LAI, biomass, and forest height
at the ecosystem level. We observed a strong influence of a change in stem
mortality on biomass loss rate. Assuming a doubling of stem mortality rate, the
biomass loss rate increased from 3.2 % yr1 to 4.5 % yr1 at
equilibrium. We also obtained a multidimensional relationship that allowed
for the estimation of biomass loss rates from forest height and LAI. Via an
example, we applied this relationship to remote sensing data on LAI and
forest height to map biomass loss rates for French Guiana. We estimated a
countrywide mean biomass loss rate of 3.0 % yr1. The approach described here provides a novel methodology for quantifying
biomass loss rates, taking the successional state of tropical forests into
account. Quantifying biomass loss rates may help to reduce uncertainties in
the analysis of the global carbon cycle.

Research areas: Land Surface/Agriculture Indicators
Measuring Sustainable Development Goals in cities, towns and rural areas: The new Degree of Urbanisation1 (2022)

The UN Sustainable Development Goals include a range of indicators that incorporate measurements for cities and urban and rural areas. Whereas the methodology for the indicators is harmonised, the definition of urban and rural areas were not National definitions of urban and rural areas differ significantly and make them unsuitable for international comparisons. In 2020, the UN Statistical Commission endorsed a harmonised definition of cities, towns and rural areas for international comparison, called the Degree of Urbanisation. This new method based on a population grid allows for a harmonised comparison of urbanisation across the globe. First estimates indicate that national definitions in several African and Asian countries show substantially higher rural population shares as compared to the harmonised definition. By contrast, rural population shares based on national definitions in Europe and the Americas tend to be similar of lower as compared to the harmonised definition. Comparing the population in large cities based on national definitions and the Degree of Urbanisation reveals a high level of agreement.

Research areas: Habitat Conversion/Fragmentation