A simple assessment approach for winter wheat loss risk impacted by water stress

In this study, an empirical assessment approach for the risk of crop loss due to water stress was developed and used to evaluate the risk of winter wheat loss in China, the United States, Germany, France and the United Kingdom. We combined statistical and remote sensing data on crop yields with climate data and cropland distribution to model the effect of water stress from 1982 to 2011. The average value of winter wheat loss due to water stress for the three European countries was about ?931 kg/ha, which was higher than that in China (?570 kg/ha) and the United States (?367 kg/ha). Our study has important implications for the operational assessment of crop loss risk at a country or regional scale. Future studies should focus on using higher spatial resolution remote sensing data, combining actual evapotranspiration to estimate water stress, improving the method for downscaling of statistical crop yield data and establishing more sophisticated zoning methods.     

Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data

The Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composite data product (MOD12Q) was used to develop annual cropland and crop-specific map products (corn, soybeans, and wheat) for the Laurentian Great Lakes Basin (GLB). The crop area distributions and changes in crop rotations were characterized by comparing annual crop map products for 2005, 2006, and 2007. The total acreages for corn and soybeans were relatively balanced for calendar years 2005 (31,462 km² and 31,283 km², respectively) and 2006 (30,766 km² and 30,972 km², respectively). Conversely, corn acreage increased approximately 21% from 2006 to 2007, while soybean and wheat acreage decreased approximately 9% and 21%, respectively. Two-year crop rotational change analyses were conducted for the 2005–2006 and 2006–2007 time periods. The large increase in corn acreages for 2007 introduced crop rotation changes across the GLB. Compared to 2005–2006, crop rotation patterns for 2006–2007 resulted in increased corn–corn, soybean–corn, and wheat–corn rotations. The increased corn acreages could have potential negative impacts on nutrient loadings, pesticide exposures, and sediment-mediated habitat degradation. Increased in US corn acreages in 2007 were related to new biofuel mandates, while Canadian increases were attributed to higher world-wide corn prices. Additional study is needed to determine the potential impacts of increases in corn-based ethanol agricultural production on watershed ecosystems and receiving waters.     

Forecasting vegetation greenness with satellite and climate data

A new and unique vegetation greenness forecast (VGF) model was designed to predict future vegetation conditions to three months through the use of current and historical climate data and satellite imagery. The VGF model is implemented through a seasonality-adjusted autoregressive distributed-lag function, based on our finding that the normalized difference vegetation index is highly correlated with lagged precipitation and temperature. Accurate forecasts were obtained from the VGF model in Nebraska grassland and cropland. The regression R/sup 2/ values range from 0.97-0.80 for 2-12 week forecasts, with higher R/sup 2/ associated with a shorter prediction. An important application would be to produce real-time forecasts of greenness images.     

Global rain-fed,irrigated, and paddy croplands: A new high resolution map derived from remote sensing,crop inventories and climate data

Irrigation accounts for 70% of global water use by humans and 33–40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.     

Carbon fluxes in ecosystems of Yellowstone National Park predicted from remote sensing data and simulation modeling

Background

A simulation model based on remote sensing data for spatial vegetation properties has been used to estimate ecosystem carbon fluxes across Yellowstone National Park (YNP). The CASA (Carnegie Ames Stanford Approach) model was applied at a regional scale to estimate seasonal and annual carbon fluxes as net primary production (NPP) and soil respiration components. Predicted net ecosystem production (NEP) flux of CO₂ is estimated from the model for carbon sinks and sources over multi-year periods that varied in climate and (wildfire) disturbance histories. Monthly Enhanced Vegetation Index (EVI) image coverages from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (from 2000 to 2006) were direct inputs to the model. New map products have been added to CASA from airborne remote sensing of coarse woody debris (CWD) in areas burned by wildfires over the past two decades.

Results

Model results indicated that relatively cooler and wetter summer growing seasons were the most favorable for annual plant production and net ecosystem carbon gains in representative landscapes of YNP. When summed across vegetation class areas, the predominance of evergreen forest and shrubland (sagebrush) cover was evident, with these two classes together accounting for 88% of the total annual NPP flux of 2.5 Tg C yr^-1 (1 Tg = 10¹² g) for the entire Yellowstone study area from 2000-2006. Most vegetation classes were estimated as net ecosystem sinks of atmospheric CO₂ on annual basis, making the entire study area a moderate net sink of about +0.13 Tg C yr^-1. This average sink value for forested lands nonetheless masks the contribution of areas burned during the 1988 wildfires, which were estimated as net sources of CO₂ to the atmosphere, totaling to a NEP flux of -0.04 Tg C yr^-1 for the entire burned area. Several areas burned in the 1988 wildfires were estimated to be among the lowest in overall yearly NPP, namely the Hellroaring Fire, Mink Fire, and Falls Fire areas.

Conclusions

Rates of recovery for burned forest areas to pre-1988 biomass levels were estimated from a unique combination of remote sensing and CASA model predictions. Ecosystem production and carbon fluxes in the Greater Yellowstone Ecosystem (GYE) result from complex interactions between climate, forest age structure, and disturbance-recovery patterns of the landscape.     

Measuring land-use and land-cover change using the U.S. department of agriculture’s cropland data layer: Cautions and recommendations

Monitoring agricultural land is important for understanding and managing food production, environmental conservation efforts, and climate change. The United States Department of Agriculture's Cropland Data Layer (CDL), an annual satellite imagery-derived land cover map, has been increasingly used for this application since complete coverage of the conterminous United States became available in 2008. However, the CDL is designed and produced with the intent of mapping annual land cover rather than tracking changes over time, and as a result certain precautions are needed in multi-year change analyses to minimize error and misapplication. We highlight scenarios that require special considerations, suggest solutions to key challenges, and propose a set of recommended good practices and general guidelines for CDL-based land change estimation. We also characterize a problematic issue of crop area underestimation bias within the CDL that needs to be accounted for and corrected when calculating changes to crop and cropland areas. When used appropriately and in conjunction with related information, the CDL is a valuable and effective tool for detecting diverse trends in agriculture. By explicitly discussing the methods and techniques for post-classification measurement of land-cover and land-use change using the CDL, we aim to further stimulate the discourse and continued development of suitable methodologies. Recommendations generated here are intended specifically for the CDL but may be broadly applicable to additional remotely-sensed land cover datasets including the National Land Cover Database (NLCD), Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover products, and other regional, national, and global land cover classification maps.     

顾及地貌特征的矿区地表塌陷DEM的生成方法

地下煤炭资源开采导致矿区地表塌陷和矿区地形的变化,直接由开采沉陷下沉预计数据生成的DEM不能表现矿区地表塌陷后的地形状况。通过在开采沉陷下沉预计程序中增加高程修正计算功能,对矿区原始地貌高程进行下沉修正计算,生成矿区地表塌陷高程数据文件,由该数据文件可以生成顾及地貌特征的矿区地表塌陷DEM。该方法操作和实现简单,生成的DEM精度可靠,能够表现矿区地表塌陷后的地形状况,可以作为矿区地理信息系统的基础数据,对于指导开采沉陷的防治和治理具有重要意义。     

A causal analysis of error in viewsheds from USGS digital elevation models

Users of GIS are faced with the ongoing and difficult problem of estimating the validity of a GIS output given the uncertainty of the quality of digital mapped data. This research project examines the manner in which error from a digital elevation model (DEM) results in error in an estimate of a viewshed. The data set includes two DEMs for the same study area. A 10-metre DEM serves as the control model to test a United States Geological Survey (USGS) DEM. The amount and spatial pattern of the error in the test DEM is determined by comparing it to the control DEM. A viewshed analysis is then performed at a set of 61 sites. After determining the relative accuracy of the viewsheds thus estimated, a causal model of viewshed error is developed, which links the sources of the DEM error with the error in the viewshed. The most important factor in DEM error is terrain roughness. The error in the viewsheds is a result of the error being propagated from the DEM, with landscape characteristics of the viewpoinr playing a role due to their effect on the error in the DEM. The conceptual path model developed sets the stage for a quancitative approach that will attempt to predict viewshed error from the landscape characteristics without direct knowledge of the error in the DEM.     

MODIS植被指数的美国玉米单产遥感估测

针对中国开展的国外农作物产量遥感估测大多依靠中低分辨率耕地信息、省级(州级)或国家级作物产量统计数据的现状,本文以美国玉米为例,探讨利用多年中高分辨率作物分布信息、时序遥感植被指数和县级作物产量统计数据开展国外重点地区作物单产遥感估测技术研究,以期进一步提高中国对国外农作物产量监测精度和精细化水平。首先,利用美国农业部国家农业统计局(NASS/USDA)生产的作物分布数据(CDL)获得多个年份玉米空间分布图,并对相应年份250 m分辨率16天合成的MODIS-NDVI时序数据进行掩膜处理,统计获得每年各县域内玉米主要生育期NDVI均值;其次,以各州为估产区,以多年县级玉米统计单产和县域内玉米主要生育期NDVI均值为基础,建立各州玉米主要生育期NDVI与玉米单产间关系模型;然后,通过主要生育期玉米单产和玉米植被指数间拟合程度,筛选确定各州玉米最佳估产期和最佳估产模型。最终,利用最佳估产模型实现美国各州玉米单产估测和全国玉米单产推算。其中,建模数据覆盖时间为2007年—2010年,验证数据为2011年。结果表明,应用最佳估产模型的2011年美国各州玉米单产估测相对误差在-4.16%—4.92%,均方根误差在148.75—820.93 kg/ha,各州估测结果计算获得全国玉米单产的相对误差仅为2.12%,均方根误差为285.57 kg/ha。可见,本研究的作物单产遥感估测技术方法具有一定可行性,可准确估测全球重点地区作物单产信息。     

Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes

In this paper, we extend the applicability of a previously proposed class of dynamic space-time models by enabling them to accommodate large datasets. We focus on the common setting where space is viewed as continuous but time is taken to be discrete. Scalability is achieved by using a low-rank predictive process to reduce the dimensionality of the data and ease the computational burden of estimating the spatio-temporal process of interest. The proposed models are illustrated using weather station data collected over the northeastern United States between 2000 and 2005. Here our interest is to use readily available predictors, association among measurements at a given station, as well as dependence across space and time to improve prediction for incomplete station records and locations where station data does not exist.     

Forecasting sea level anomalies from TOPEX/Poseidon and Jason-1 satellite altimetry

This paper aims at the prediction of both global mean sea level anomalies (SLAs) and gridded SLA data in the east equatorial Pacific obtained from TOPEX/Poseidon and Jason-1 altimetric measurements. The first prediction technique (denoted as LS) is based on the extrapolation of a polynomial-harmonic deterministic least-squares model describing a linear trend, annual and semi-annual oscillations. The second prediction method (denoted as LS + AR) is a combination of the extrapolation of a polynomial-harmonic model with the autoregressive forecast of LS residuals. In the case of forecasting global mean SLA data, both techniques allow one to compute the predictions of comparable accuracy (root mean square error for 1-month in the future is of 0.5 cm). In the case of predicting gridded SLA data, the LS + AR prediction method gains significantly better prediction accuracy than the accuracy obtained by the LS technique during El Niño 1997/1998, La Niña 1998/1999 and during normal conditions.     

Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).     

GNSS 监测数据预处理在变形预测中的应用

GNSS监测技术被广泛应用于变形监测工作中,但GNSS监测数据中会有缺失值、噪声等误差的存在,对预测结果造成影响。引入改进的小波神经网络模型进行变形预测,并考虑该模型的自适应性和容错性,分别采用三次样条插值法、小波滤波法和拉依达准则对原始监测数据进行缺失值填补、去噪和粗差剔除等预处理。并利用实际监测数据进行短期预报分析,对比原始监测数据和预处理后的监测数据的预测结果,结果表明预处理后的监测数据的预测效果更好。     

Estimating crop net primary production using national inventory data and MODIS-derived parameters

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917 g C m⁻² yr⁻¹ and 409 g C m⁻² yr⁻¹, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.     

Modeling global Hammond landform regions from 250‐m elevation data

In 1964, E.H. Hammond proposed criteria for classifying and mapping physiographic regions of the United States. Hammond produced a map entitled “Classes of Land Surface Form in the Forty‐Eight States, USA”, which is regarded as a pioneering and rigorous treatment of regional physiography. Several researchers automated Hammond?s model in GIS. However, these were local or regional in application, and resulted in inadequate characterization of tablelands. We used a global 250 m DEM to produce a new characterization of global Hammond landform regions. The improved algorithm we developed for the regional landform modeling: (1) incorporated a profile parameter for the delineation of tablelands; (2) accommodated negative elevation data values; (3) allowed neighborhood analysis window (NAW) size to vary between parameters; (4) more accurately bounded plains regions; and (5) mapped landform regions as opposed to discrete landform features. The new global Hammond landform regions product builds on an existing global Hammond landform features product developed by the U.S. Geological Survey, which, while globally comprehensive, did not include tablelands, used a fixed NAW size, and essentially classified pixels rather than regions. Our algorithm also permits the disaggregation of “mixed” Hammond types (e.g. plains with high mountains) into their component parts.     

A method to generalize stream flowlines in small-scale maps by a variable flow-based pruning threshold

The aim of this paper is to explore and describe a method of automated generalization designed to produce a map which strikes a balance between cartographic and hydrologic representations. Following a discussion of scholarly literature on generalization, we describe a novel method for automated generalization of hydrographic stream data, using the National Hydrography Data Set (NHDPlus) as an example.

Traditional hydrography shows a fairly uniform density of stream flowlines over space. While this is pleasing to the eye, traditional methods tend to under-represent rivers in humid areas and over-represent them in arid areas. We address this problem through a method in automated generalization to produce a high-quality presentation of hydrographic data, suitable for display as a wall map or in an atlas. Streams are pruned based on a variable flow threshold, derived from the local mean annual precipitation by a regression equation.

After running the model using different parameters, we produce a more satisfactory portrayal of stream networks in the United States that communicates the flow of water through rivers and reflects the regional climate. Specific advantages in generalizing with variable flow threshold include (1) the method allows for fine gradations in output scale; (2) the output maps tend to minimize density variations in the raw data; (3) the subjective criteria are easily derived; and (4) the method can be performed rapidly on large data sets, as long as the stream data has been enriched with reliable flow rates.     

An integrated methodology for soil moisture analysis using multispectral data in Mongolia

Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.     

新陈代谢灰色模型在地铁变形监测中的应用

针对地铁开挖造成的地表变形预测问题,本文探讨了灰色模型的基本原理与优势,并以某地铁实测变形数据为依据,采用新陈代谢GM(1,1)模型进行建模预测,以ARMA预测模型、GM(1,1)模型分别进行对比分析。通过精度评定,获取可靠结论。实验结果表明,3种预计模型均可获取一定精度的预测值,新陈代谢GM(1,1)模型的预测值准确可靠,精度高于另外两种模型,为同类变形预计的实际工程项目提供了依据,具有参考价值。     

Preparing The National Map for the 3D Elevation Program – products,process and research

The 3D Elevation Program (3DEP) is a collaborative effort among government entities, academia, and the private sector to collect high-resolution 3-dimensional data over the United States. The United States Geological Survey (USGS) is making preparations for managing, processing, and delivering petabytes of 3DEP elevation products for the Nation. In addition to the existing 1/3, 1, and 2 arc-second seamless elevation data layers of The National Map, new 3DEP products include lidar point cloud data; a standard 1-meter DEM layer; additional source datasets; and, in Alaska, 5-meter digital elevation models. A new product generation system improves the construction and publication of the seamless elevation datasets, prepares the additional 3DEP products for distribution, and automates the data management functions required to accommodate the high-volume 3DEP data collection. Major changes in geospatial data acquisition, such as high resolution lidar data, volunteered geographic information, data processing using parallel and grid computer systems, and user needs for semantic access to geospatial data and products, are driving USGS research associated with the 3DEP. To address the research requirements, a set of inter-related projects including spatiotemporal data models, data integration, geospatial semantics and ontology, high performance computing, multi-scale representation, and hydrological modeling using lidar and other 3DEP data has been developed.     

中国地区SRTM3 DEM高程精度质量评价

为揭示我国SRTM3DEM数据高程精度质量,结合已开展过SRTM3DEM高程精度质量评价工作的局部地区的研究,考虑空间分布情况,选取新疆、辽宁、山东、浙江、海南5个地区的平原、丘陵、盆地、山地等地形区域作为典型研究区,并以1∶5万DEM为假定真值、以1∶25万DEM为参照,通过DEM面误差可视化分析、DEM面误差信息熵模型、中误差模型等方法对SRTM3DEM数据高程精度质量做了分析。计算结果表明我国SRTM3DEM数据高程精度质量受地形影响并存在一定的空间差异性,同时我国范围内SRTM3DEM数据高程精度质量整体上要高于1∶25万DEM。