This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated ... more This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated by Biome-BGC, LPJ, and CASA in an ensemble model experiment conducted using the Terrestrial Observation and Prediction System. We developed the Hierarchical Framework for Diagnosing Ecosystem Models to separate the simulated biogeochemistry into a cascade of functional tiers and examine their characteristics sequentially. The analyses indicate that the simulated biomass is usually two to three times higher in Biome-BGC than LPJ or CASA. Such a discrepancy is mainly induced by differences in model parameters and algorithms that regulate the rates of biomass turnover. The mean residence time of biomass in Biome-BGC is estimated to be 40–80 years in temperate/moist climate regions, while it mostly varies between 5 and 30 years in CASA and LPJ. A large range of values is also found in the simulated soil carbon. The mean residence time of soil carbon in Biome-BGC and LPJ is ∼200 years in cold regions, which decreases rapidly with increases of temperature at a rate of ∼10 yr °C−1. Because long-term soil carbon pool is not simulated in CASA, its corresponding mean residence time is only about 10–20 years and less sensitive to temperature. Another key factor that influences the carbon balance of the simulated ecosystem is disturbance caused by wildfire, for which the algorithms vary among the models. Because fire emissions are balanced by net ecosystem production (NEP) at steady states, magnitudes, and spatial patterns of NEP vary significantly as well. Slight carbon imbalance may be left by the spin-up algorithm of the models, which adds uncertainty to the estimated carbon sources or sinks. Although these results are only drawn on the tested model versions, the developed methodology has potential for other model exercises.
This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated ... more This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated by Biome-BGC, LPJ, and CASA in an ensemble model experiment conducted using the Terrestrial Observation and Prediction System. We developed the Hierarchical Framework for Diagnosing Ecosystem Models to separate the simulated biogeochemistry into a cascade of functional tiers and examine their characteristics sequentially. The analyses indicate that the simulated biomass is usually two to three times higher in Biome-BGC than LPJ or CASA. Such a discrepancy is mainly induced by differences in model parameters and algorithms that regulate the rates of biomass turnover. The mean residence time of biomass in Biome-BGC is estimated to be 40–80 years in temperate/moist climate regions, while it mostly varies between 5 and 30 years in CASA and LPJ. A large range of values is also found in the simulated soil carbon. The mean residence time of soil carbon in Biome-BGC and LPJ is ∼200 years in cold regions, which decreases rapidly with increases of temperature at a rate of ∼10 yr °C−1. Because long-term soil carbon pool is not simulated in CASA, its corresponding mean residence time is only about 10–20 years and less sensitive to temperature. Another key factor that influences the carbon balance of the simulated ecosystem is disturbance caused by wildfire, for which the algorithms vary among the models. Because fire emissions are balanced by net ecosystem production (NEP) at steady states, magnitudes, and spatial patterns of NEP vary significantly as well. Slight carbon imbalance may be left by the spin-up algorithm of the models, which adds uncertainty to the estimated carbon sources or sinks. Although these results are only drawn on the tested model versions, the developed methodology has potential for other model exercises.
Estimates of carbon emissions from factors such as deforestation, degradation and afforestation p... more Estimates of carbon emissions from factors such as deforestation, degradation and afforestation pose a high degree of uncertainty due to the spatial variability of carbon that is stored in forest biomass. To improve confidence in emissions estimates, we need the spatial distribution of the amounts of carbon stored in the forest. In this study, we aim to quantify the sources of uncertainty, while testing and evaluating multi-modal models to quantify forest aboveground biomass. We initially build a multi-dimensional data layer approach to assess explanatory behavior of vegetation photosynthetic variables (e.g. LAI, NDVI), climate variables (e.g. temperature and precipitation) and topography (e.g. slope and elevation from SRTM) to vegetation height as estimated from the ICESat GLAS data. Our initial pilot project is for North America and the output biomass map is derived at a high spatial resolution of 30m. The vegetation photosynthetic variables are derived from the 30m Landsat atmospherically corrected surface reflectance product and land cover stratification is performed utilizing the 30-m NLCD land cover data. Climate variables corresponding to minimum/maximum temperature and mean annual precipitation are obtained from the Terrestrial Observation and Prediction System (TOPS) modeling framework. We use relational functions developed between measured forest heights from GLAS, vegetation photosynthesis and climate variables, to estimate heights for pixels where GLAS heights are not available. This will create a wall-to-wall continuous height map. Furthermore, we develop empirical models between the continuous height map and the available forest inventory aboveground biomass data from the FIA for different land cover types to create an aboveground biomass map. Uncertainty analysis will involve (a) comparing the continuous height map to FIA tree height data and LVIS obtained heights; (b) comparison of the generated biomass map with already available satellite derived biomass maps; (c) assessing the sensitivity of biomass estimates with respect to the empirical functions utilized in the multivariate models; (d) assessing variability in height distributions by incorporating high resolution species maps and land cover maps.
Estimates of carbon emissions from factors such as deforestation, degradation and afforestation p... more Estimates of carbon emissions from factors such as deforestation, degradation and afforestation pose a high degree of uncertainty due to the spatial variability of carbon that is stored in forest biomass. To improve confidence in emissions estimates, we need the spatial distribution of the amounts of carbon stored in the forest. In this study, we aim to quantify the sources of uncertainty, while testing and evaluating multi-modal models to quantify forest aboveground biomass. We initially build a multi-dimensional data layer approach to assess explanatory behavior of vegetation photosynthetic variables (e.g. LAI, NDVI), climate variables (e.g. temperature and precipitation) and topography (e.g. slope and elevation from SRTM) to vegetation height as estimated from the ICESat GLAS data. Our initial pilot project is for North America and the output biomass map is derived at a high spatial resolution of 30m. The vegetation photosynthetic variables are derived from the 30m Landsat atmospherically corrected surface reflectance product and land cover stratification is performed utilizing the 30-m NLCD land cover data. Climate variables corresponding to minimum/maximum temperature and mean annual precipitation are obtained from the Terrestrial Observation and Prediction System (TOPS) modeling framework. We use relational functions developed between measured forest heights from GLAS, vegetation photosynthesis and climate variables, to estimate heights for pixels where GLAS heights are not available. This will create a wall-to-wall continuous height map. Furthermore, we develop empirical models between the continuous height map and the available forest inventory aboveground biomass data from the FIA for different land cover types to create an aboveground biomass map. Uncertainty analysis will involve (a) comparing the continuous height map to FIA tree height data and LVIS obtained heights; (b) comparison of the generated biomass map with already available satellite derived biomass maps; (c) assessing the sensitivity of biomass estimates with respect to the empirical functions utilized in the multivariate models; (d) assessing variability in height distributions by incorporating high resolution species maps and land cover maps.
Land surface phenology is widely used as a diagnostic of ecosystem response to global change and ... more Land surface phenology is widely used as a diagnostic of ecosystem response to global change and influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, a robust framework in classifying the ground phenological events to compare with remotely sensed phenology is lacking. In this research, we develop a methodology to compare the latest Collection 5 500m MODIS phenology data (cardinal dates corresponding to greenup, maturity, senescence and dormancy) with all available ground observed data for North America for the period 2001-2006. Inter-comparison results show that the MODIS phenology product performs well in capturing the “earliest events” corresponding to each of the phenological phases (e.g. budburst, first leaf, first leaf color change) of plant growth in a community. Of all the ground phenological stages, the leaf greenup events are best correlated to the MODIS greenup onset. Establishing confidence in the MODIS greenup onset data through our inter-comparison exercise and exploiting the long-term nature of the data set (2001-present) led us to build a seasonal phenology forecast model within the Terrestrial Observation and Prediction System (TOPS) framework. The available MODIS greenup data serves as a training data to our climate based phenology forecast model. We parameterize the empirical model for each 500m pixel based on the relationship between local climate provided by TOPS and MODIS greenup. As a test case, we have predicted the greenup for 2010 spring by using the seasonal climate forecast data from NECP Climate Forecast System (CFS) to our forecast model. The phenology forecast portal is now available online, which provides a unique platform for community participation in monitoring local phenology forecast events and uploading observed phenological events through the web-based interface.
Land surface phenology is widely used as a diagnostic of ecosystem response to global change and ... more Land surface phenology is widely used as a diagnostic of ecosystem response to global change and influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, a robust framework in classifying the ground phenological events to compare with remotely sensed phenology is lacking. In this research, we develop a methodology to compare the latest Collection 5 500m MODIS phenology data (cardinal dates corresponding to greenup, maturity, senescence and dormancy) with all available ground observed data for North America for the period 2001-2006. Inter-comparison results show that the MODIS phenology product performs well in capturing the “earliest events” corresponding to each of the phenological phases (e.g. budburst, first leaf, first leaf color change) of plant growth in a community. Of all the ground phenological stages, the leaf greenup events are best correlated to the MODIS greenup onset. Establishing confidence in the MODIS greenup onset data through our inter-comparison exercise and exploiting the long-term nature of the data set (2001-present) led us to build a seasonal phenology forecast model within the Terrestrial Observation and Prediction System (TOPS) framework. The available MODIS greenup data serves as a training data to our climate based phenology forecast model. We parameterize the empirical model for each 500m pixel based on the relationship between local climate provided by TOPS and MODIS greenup. As a test case, we have predicted the greenup for 2010 spring by using the seasonal climate forecast data from NECP Climate Forecast System (CFS) to our forecast model. The phenology forecast portal is now available online, which provides a unique platform for community participation in monitoring local phenology forecast events and uploading observed phenological events through the web-based interface.
Urban places may be broadly defined as the settlements where most people live and work. Human bei... more Urban places may be broadly defined as the settlements where most people live and work. Human beings worldwide tend to cluster in spatially limited habitats occupying less than 5% of the world’s land area. Urbanization may be defined as those environment altering activities that create and maintain urban places. This includes the processes of construction, habitation, transportation, energy and water use, communication, industrialization, commercial and manufacturing services, plus civic activities associated with education and governance. The physical patterns of urban areas produce distinctive spatial and spectral signatures that are recorded by many types of remotely sensed data.
Urban places may be broadly defined as the settlements where most people live and work. Human bei... more Urban places may be broadly defined as the settlements where most people live and work. Human beings worldwide tend to cluster in spatially limited habitats occupying less than 5% of the world’s land area. Urbanization may be defined as those environment altering activities that create and maintain urban places. This includes the processes of construction, habitation, transportation, energy and water use, communication, industrialization, commercial and manufacturing services, plus civic activities associated with education and governance. The physical patterns of urban areas produce distinctive spatial and spectral signatures that are recorded by many types of remotely sensed data.
One of the major challenges we face on our planet is increasing agricultural production to meet t... more One of the major challenges we face on our planet is increasing agricultural production to meet the dietary requirements of an additional 2.5 billion people by the mid of the century while limiting cropland expansion and other damages to natural resources. This problem is even more so challenging given that nearly all the population growth will take place where the majority of the hungry live today and where ongoing and future climate changes are projected to most negatively impact agricultural production, the semi-arid tropics (SAT). The SAT contain 40% of the global irrigated and rainfed croplands in over 50 developing countries and a growing population of over a billion and half people, many of which live in absolute poverty and strongly depend on agriculture that is constrained by chronic water shortages. Rates of food grain production in many of the countries of the SAT have progressively increased since the mid 1960s aided by the Green Revolution and relatively favourable climatic conditions. However, aggregated agricultural production statistics indicate that the rate of food grain production has recently stalled or declined in several of the countries in this region, escalating the concerns over matters of food security, that is availability of food and one’s access to it, in a region where many people live in extreme poverty, depend on an agrarian economy and are expected to face increasingly worse climatic conditions in the near future. In this paper we analyze the agricultural deceleration and its drivers over the country of India, which faces the daunting challenge of needing a 50-100% increase in yields of major crops by the middle to the 21st century to feed its growing population. We analyze the long term (1982-2006) record of the Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA/AVHRR) together with climate, land use, and crop production statistics. We show that while there are no significant changes in long-term precipitation, there are geographically matching patterns of enhanced crop production and irrigation expansion with groundwater, initially, but both of which have levelled off in the past decade. In addition to increasing pressure on water resources, a decline in expansion of cropland area, a warming climate and increasing air pollution compound to challenging long term food security in India.
One of the major challenges we face on our planet is increasing agricultural production to meet t... more One of the major challenges we face on our planet is increasing agricultural production to meet the dietary requirements of an additional 2.5 billion people by the mid of the century while limiting cropland expansion and other damages to natural resources. This problem is even more so challenging given that nearly all the population growth will take place where the majority of the hungry live today and where ongoing and future climate changes are projected to most negatively impact agricultural production, the semi-arid tropics (SAT). The SAT contain 40% of the global irrigated and rainfed croplands in over 50 developing countries and a growing population of over a billion and half people, many of which live in absolute poverty and strongly depend on agriculture that is constrained by chronic water shortages. Rates of food grain production in many of the countries of the SAT have progressively increased since the mid 1960s aided by the Green Revolution and relatively favourable climatic conditions. However, aggregated agricultural production statistics indicate that the rate of food grain production has recently stalled or declined in several of the countries in this region, escalating the concerns over matters of food security, that is availability of food and one’s access to it, in a region where many people live in extreme poverty, depend on an agrarian economy and are expected to face increasingly worse climatic conditions in the near future. In this paper we analyze the agricultural deceleration and its drivers over the country of India, which faces the daunting challenge of needing a 50-100% increase in yields of major crops by the middle to the 21st century to feed its growing population. We analyze the long term (1982-2006) record of the Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA/AVHRR) together with climate, land use, and crop production statistics. We show that while there are no significant changes in long-term precipitation, there are geographically matching patterns of enhanced crop production and irrigation expansion with groundwater, initially, but both of which have levelled off in the past decade. In addition to increasing pressure on water resources, a decline in expansion of cropland area, a warming climate and increasing air pollution compound to challenging long term food security in India.
This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated ... more This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated by Biome-BGC, LPJ, and CASA in an ensemble model experiment conducted using the Terrestrial Observation and Prediction System. We developed the Hierarchical Framework for Diagnosing Ecosystem Models to separate the simulated biogeochemistry into a cascade of functional tiers and examine their characteristics sequentially. The analyses indicate that the simulated biomass is usually two to three times higher in Biome-BGC than LPJ or CASA. Such a discrepancy is mainly induced by differences in model parameters and algorithms that regulate the rates of biomass turnover. The mean residence time of biomass in Biome-BGC is estimated to be 40–80 years in temperate/moist climate regions, while it mostly varies between 5 and 30 years in CASA and LPJ. A large range of values is also found in the simulated soil carbon. The mean residence time of soil carbon in Biome-BGC and LPJ is ∼200 years in cold regions, which decreases rapidly with increases of temperature at a rate of ∼10 yr °C−1. Because long-term soil carbon pool is not simulated in CASA, its corresponding mean residence time is only about 10–20 years and less sensitive to temperature. Another key factor that influences the carbon balance of the simulated ecosystem is disturbance caused by wildfire, for which the algorithms vary among the models. Because fire emissions are balanced by net ecosystem production (NEP) at steady states, magnitudes, and spatial patterns of NEP vary significantly as well. Slight carbon imbalance may be left by the spin-up algorithm of the models, which adds uncertainty to the estimated carbon sources or sinks. Although these results are only drawn on the tested model versions, the developed methodology has potential for other model exercises.
This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated ... more This paper examines carbon stocks and their relative balance in terrestrial ecosystems simulated by Biome-BGC, LPJ, and CASA in an ensemble model experiment conducted using the Terrestrial Observation and Prediction System. We developed the Hierarchical Framework for Diagnosing Ecosystem Models to separate the simulated biogeochemistry into a cascade of functional tiers and examine their characteristics sequentially. The analyses indicate that the simulated biomass is usually two to three times higher in Biome-BGC than LPJ or CASA. Such a discrepancy is mainly induced by differences in model parameters and algorithms that regulate the rates of biomass turnover. The mean residence time of biomass in Biome-BGC is estimated to be 40–80 years in temperate/moist climate regions, while it mostly varies between 5 and 30 years in CASA and LPJ. A large range of values is also found in the simulated soil carbon. The mean residence time of soil carbon in Biome-BGC and LPJ is ∼200 years in cold regions, which decreases rapidly with increases of temperature at a rate of ∼10 yr °C−1. Because long-term soil carbon pool is not simulated in CASA, its corresponding mean residence time is only about 10–20 years and less sensitive to temperature. Another key factor that influences the carbon balance of the simulated ecosystem is disturbance caused by wildfire, for which the algorithms vary among the models. Because fire emissions are balanced by net ecosystem production (NEP) at steady states, magnitudes, and spatial patterns of NEP vary significantly as well. Slight carbon imbalance may be left by the spin-up algorithm of the models, which adds uncertainty to the estimated carbon sources or sinks. Although these results are only drawn on the tested model versions, the developed methodology has potential for other model exercises.
Estimates of carbon emissions from factors such as deforestation, degradation and afforestation p... more Estimates of carbon emissions from factors such as deforestation, degradation and afforestation pose a high degree of uncertainty due to the spatial variability of carbon that is stored in forest biomass. To improve confidence in emissions estimates, we need the spatial distribution of the amounts of carbon stored in the forest. In this study, we aim to quantify the sources of uncertainty, while testing and evaluating multi-modal models to quantify forest aboveground biomass. We initially build a multi-dimensional data layer approach to assess explanatory behavior of vegetation photosynthetic variables (e.g. LAI, NDVI), climate variables (e.g. temperature and precipitation) and topography (e.g. slope and elevation from SRTM) to vegetation height as estimated from the ICESat GLAS data. Our initial pilot project is for North America and the output biomass map is derived at a high spatial resolution of 30m. The vegetation photosynthetic variables are derived from the 30m Landsat atmospherically corrected surface reflectance product and land cover stratification is performed utilizing the 30-m NLCD land cover data. Climate variables corresponding to minimum/maximum temperature and mean annual precipitation are obtained from the Terrestrial Observation and Prediction System (TOPS) modeling framework. We use relational functions developed between measured forest heights from GLAS, vegetation photosynthesis and climate variables, to estimate heights for pixels where GLAS heights are not available. This will create a wall-to-wall continuous height map. Furthermore, we develop empirical models between the continuous height map and the available forest inventory aboveground biomass data from the FIA for different land cover types to create an aboveground biomass map. Uncertainty analysis will involve (a) comparing the continuous height map to FIA tree height data and LVIS obtained heights; (b) comparison of the generated biomass map with already available satellite derived biomass maps; (c) assessing the sensitivity of biomass estimates with respect to the empirical functions utilized in the multivariate models; (d) assessing variability in height distributions by incorporating high resolution species maps and land cover maps.
Estimates of carbon emissions from factors such as deforestation, degradation and afforestation p... more Estimates of carbon emissions from factors such as deforestation, degradation and afforestation pose a high degree of uncertainty due to the spatial variability of carbon that is stored in forest biomass. To improve confidence in emissions estimates, we need the spatial distribution of the amounts of carbon stored in the forest. In this study, we aim to quantify the sources of uncertainty, while testing and evaluating multi-modal models to quantify forest aboveground biomass. We initially build a multi-dimensional data layer approach to assess explanatory behavior of vegetation photosynthetic variables (e.g. LAI, NDVI), climate variables (e.g. temperature and precipitation) and topography (e.g. slope and elevation from SRTM) to vegetation height as estimated from the ICESat GLAS data. Our initial pilot project is for North America and the output biomass map is derived at a high spatial resolution of 30m. The vegetation photosynthetic variables are derived from the 30m Landsat atmospherically corrected surface reflectance product and land cover stratification is performed utilizing the 30-m NLCD land cover data. Climate variables corresponding to minimum/maximum temperature and mean annual precipitation are obtained from the Terrestrial Observation and Prediction System (TOPS) modeling framework. We use relational functions developed between measured forest heights from GLAS, vegetation photosynthesis and climate variables, to estimate heights for pixels where GLAS heights are not available. This will create a wall-to-wall continuous height map. Furthermore, we develop empirical models between the continuous height map and the available forest inventory aboveground biomass data from the FIA for different land cover types to create an aboveground biomass map. Uncertainty analysis will involve (a) comparing the continuous height map to FIA tree height data and LVIS obtained heights; (b) comparison of the generated biomass map with already available satellite derived biomass maps; (c) assessing the sensitivity of biomass estimates with respect to the empirical functions utilized in the multivariate models; (d) assessing variability in height distributions by incorporating high resolution species maps and land cover maps.
Land surface phenology is widely used as a diagnostic of ecosystem response to global change and ... more Land surface phenology is widely used as a diagnostic of ecosystem response to global change and influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, a robust framework in classifying the ground phenological events to compare with remotely sensed phenology is lacking. In this research, we develop a methodology to compare the latest Collection 5 500m MODIS phenology data (cardinal dates corresponding to greenup, maturity, senescence and dormancy) with all available ground observed data for North America for the period 2001-2006. Inter-comparison results show that the MODIS phenology product performs well in capturing the “earliest events” corresponding to each of the phenological phases (e.g. budburst, first leaf, first leaf color change) of plant growth in a community. Of all the ground phenological stages, the leaf greenup events are best correlated to the MODIS greenup onset. Establishing confidence in the MODIS greenup onset data through our inter-comparison exercise and exploiting the long-term nature of the data set (2001-present) led us to build a seasonal phenology forecast model within the Terrestrial Observation and Prediction System (TOPS) framework. The available MODIS greenup data serves as a training data to our climate based phenology forecast model. We parameterize the empirical model for each 500m pixel based on the relationship between local climate provided by TOPS and MODIS greenup. As a test case, we have predicted the greenup for 2010 spring by using the seasonal climate forecast data from NECP Climate Forecast System (CFS) to our forecast model. The phenology forecast portal is now available online, which provides a unique platform for community participation in monitoring local phenology forecast events and uploading observed phenological events through the web-based interface.
Land surface phenology is widely used as a diagnostic of ecosystem response to global change and ... more Land surface phenology is widely used as a diagnostic of ecosystem response to global change and influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, a robust framework in classifying the ground phenological events to compare with remotely sensed phenology is lacking. In this research, we develop a methodology to compare the latest Collection 5 500m MODIS phenology data (cardinal dates corresponding to greenup, maturity, senescence and dormancy) with all available ground observed data for North America for the period 2001-2006. Inter-comparison results show that the MODIS phenology product performs well in capturing the “earliest events” corresponding to each of the phenological phases (e.g. budburst, first leaf, first leaf color change) of plant growth in a community. Of all the ground phenological stages, the leaf greenup events are best correlated to the MODIS greenup onset. Establishing confidence in the MODIS greenup onset data through our inter-comparison exercise and exploiting the long-term nature of the data set (2001-present) led us to build a seasonal phenology forecast model within the Terrestrial Observation and Prediction System (TOPS) framework. The available MODIS greenup data serves as a training data to our climate based phenology forecast model. We parameterize the empirical model for each 500m pixel based on the relationship between local climate provided by TOPS and MODIS greenup. As a test case, we have predicted the greenup for 2010 spring by using the seasonal climate forecast data from NECP Climate Forecast System (CFS) to our forecast model. The phenology forecast portal is now available online, which provides a unique platform for community participation in monitoring local phenology forecast events and uploading observed phenological events through the web-based interface.
Urban places may be broadly defined as the settlements where most people live and work. Human bei... more Urban places may be broadly defined as the settlements where most people live and work. Human beings worldwide tend to cluster in spatially limited habitats occupying less than 5% of the world’s land area. Urbanization may be defined as those environment altering activities that create and maintain urban places. This includes the processes of construction, habitation, transportation, energy and water use, communication, industrialization, commercial and manufacturing services, plus civic activities associated with education and governance. The physical patterns of urban areas produce distinctive spatial and spectral signatures that are recorded by many types of remotely sensed data.
Urban places may be broadly defined as the settlements where most people live and work. Human bei... more Urban places may be broadly defined as the settlements where most people live and work. Human beings worldwide tend to cluster in spatially limited habitats occupying less than 5% of the world’s land area. Urbanization may be defined as those environment altering activities that create and maintain urban places. This includes the processes of construction, habitation, transportation, energy and water use, communication, industrialization, commercial and manufacturing services, plus civic activities associated with education and governance. The physical patterns of urban areas produce distinctive spatial and spectral signatures that are recorded by many types of remotely sensed data.
One of the major challenges we face on our planet is increasing agricultural production to meet t... more One of the major challenges we face on our planet is increasing agricultural production to meet the dietary requirements of an additional 2.5 billion people by the mid of the century while limiting cropland expansion and other damages to natural resources. This problem is even more so challenging given that nearly all the population growth will take place where the majority of the hungry live today and where ongoing and future climate changes are projected to most negatively impact agricultural production, the semi-arid tropics (SAT). The SAT contain 40% of the global irrigated and rainfed croplands in over 50 developing countries and a growing population of over a billion and half people, many of which live in absolute poverty and strongly depend on agriculture that is constrained by chronic water shortages. Rates of food grain production in many of the countries of the SAT have progressively increased since the mid 1960s aided by the Green Revolution and relatively favourable climatic conditions. However, aggregated agricultural production statistics indicate that the rate of food grain production has recently stalled or declined in several of the countries in this region, escalating the concerns over matters of food security, that is availability of food and one’s access to it, in a region where many people live in extreme poverty, depend on an agrarian economy and are expected to face increasingly worse climatic conditions in the near future. In this paper we analyze the agricultural deceleration and its drivers over the country of India, which faces the daunting challenge of needing a 50-100% increase in yields of major crops by the middle to the 21st century to feed its growing population. We analyze the long term (1982-2006) record of the Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA/AVHRR) together with climate, land use, and crop production statistics. We show that while there are no significant changes in long-term precipitation, there are geographically matching patterns of enhanced crop production and irrigation expansion with groundwater, initially, but both of which have levelled off in the past decade. In addition to increasing pressure on water resources, a decline in expansion of cropland area, a warming climate and increasing air pollution compound to challenging long term food security in India.
One of the major challenges we face on our planet is increasing agricultural production to meet t... more One of the major challenges we face on our planet is increasing agricultural production to meet the dietary requirements of an additional 2.5 billion people by the mid of the century while limiting cropland expansion and other damages to natural resources. This problem is even more so challenging given that nearly all the population growth will take place where the majority of the hungry live today and where ongoing and future climate changes are projected to most negatively impact agricultural production, the semi-arid tropics (SAT). The SAT contain 40% of the global irrigated and rainfed croplands in over 50 developing countries and a growing population of over a billion and half people, many of which live in absolute poverty and strongly depend on agriculture that is constrained by chronic water shortages. Rates of food grain production in many of the countries of the SAT have progressively increased since the mid 1960s aided by the Green Revolution and relatively favourable climatic conditions. However, aggregated agricultural production statistics indicate that the rate of food grain production has recently stalled or declined in several of the countries in this region, escalating the concerns over matters of food security, that is availability of food and one’s access to it, in a region where many people live in extreme poverty, depend on an agrarian economy and are expected to face increasingly worse climatic conditions in the near future. In this paper we analyze the agricultural deceleration and its drivers over the country of India, which faces the daunting challenge of needing a 50-100% increase in yields of major crops by the middle to the 21st century to feed its growing population. We analyze the long term (1982-2006) record of the Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA/AVHRR) together with climate, land use, and crop production statistics. We show that while there are no significant changes in long-term precipitation, there are geographically matching patterns of enhanced crop production and irrigation expansion with groundwater, initially, but both of which have levelled off in the past decade. In addition to increasing pressure on water resources, a decline in expansion of cropland area, a warming climate and increasing air pollution compound to challenging long term food security in India.
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Papers by Cristina Milesi