地理国情普查中地表覆盖分类数据典型问题解析

由于地理国情普查是一个全新的、有别于其他测绘项目的新兴项目,在生产过程中难免会出现各种各样的问题。本文结合生产实际,对于地表覆盖分类数据生产过程中出现的一些特征明显、问题集中的错误进行归类解析。     

Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure

Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.     

Evaluating the relationship between biomass,percent groundcover and remote sensing indices across six winter cover crop fields in Maryland,United States

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r² = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r² = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.     

Reducing background effects in orchards through spectral vegetation index correction

Satellite remote sensing provides an alternative to time-consuming and labor intensive in situ measurements of biophysical variables in agricultural crops required for precision agriculture applications. In orchards, however, the spatial resolution causes mixtures of canopies and background (i.e. soil, grass and shadow), hampering the estimation of these biophysical variables. Furthermore, variable background mixtures obstruct meaningful comparisons between different orchard blocks, rows or within each row. Current correction methodologies use spectral differences between canopies and background, but struggle with a vegetated orchard floor. This background influence and the lack of a generic solution are addressed in this study.Firstly, the problem was demonstrated in a controlled environment for vegetation indices sensitive to chlorophyll content, water content and leaf area index. Afterwards, traditional background correction methods (i.e. soil-adjusted vegetation indices and signal unmixing) were compared to the proposed vegetation index correction. This correction was based on the mixing degree of each pixel (i.e. tree cover fraction) to rescale the vegetation indices accordingly and was applied to synthetic and WorldView-2 satellite imagery. Through the correction, the effect of background admixture for vegetation indices was reduced, and the estimation of biophysical variables was improved (ΔR² = 0.2–0.31).     

Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa

Currently there is a lack of knowledge on spatio-temporal patterns of land surface dynamics at medium spatial scale in southern Africa, even though this information is essential for better understanding of ecosystem response to climatic variability and human-induced land transformations. In this study, we analysed vegetation dynamics across a large area in southern Africa using the 14-years (2000–2013) of medium spatial resolution (250 m) MODIS-EVI time-series data. Specifically, we investigated temporal changes in the time series of key phenometrics including overall greenness, peak and timing of annual greenness over the monitoring period and study region. In order to specifically capture spatial and per pixel vegetation changes over time, we calculated trends in these phenometrics using a robust trend analysis method. The results showed that interannual vegetation dynamics followed precipitation patterns with clearly differentiated seasonality. The earliest peak greenness during 2000–2013 occurred at the end of January in the year 2000 and the latest peak greenness was observed at the mid of March in 2012. Specifically spatial patterns of long-term vegetation trends allowed mapping areas of (i) decrease or increase in overall greenness, (ii) decrease or increase of peak greenness, and (iii) shifts in timing of occurrence of peak greenness over the 14-year monitoring period. The observed vegetation decline in the study area was mainly attributed to human-induced factors. The obtained information is useful to guide selection of field sites for detailed vegetation studies and land rehabilitation interventions and serve as an input for a range of land surface models.     

Use of WorldView-2 time series to establish a wetland monitoring program for potential offsite impacts of mine site rehabilitation

The Ramsar-listed wetlands of the Magela Creek floodplain, situated in the World Heritage Kakadu National Park, in northern Australia are recognised for their biodiversity and cultural values. The floodplain is also a downstream receiving environment for Ranger uranium mine, which is entering closure and rehabilitation phases. Vegetation on the floodplain is spatially and temporally variable which is related to the hydrology of the region, primarily the extent and level of inundation and available soil moisture. Time-series mapping of the floodplain vegetation will provide a contemporary baseline of annual vegetation dynamics to assist with determining whether change is natural or a result of the potential impacts of mine closure activities such as increased suspended sediment moving downstream. The research described here used geographic object-based image analysis (GEOBIA) to classify the upper Magela Creek floodplain vegetation from WorldView-2 imagery captured over four years (2010–2013) and ancillary data including a canopy height model. A step-wise rule set was used to implement a decision tree classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010, May 2011, June 2012 and June 2013 with overall accuracies of over 80% for each map. Most of the error appears to be associated with confusion between vegetation classes that are spectrally similar such as the classes dominated by grasses. Object-based change detection was then applied to the maps to analyse change between dates. Results indicate that change between dates was detected for large areas of the floodplain. Most of the change is associated with the amount of surface water present, indicating that although imagery was captured at the same time of year, the imagery represents different stages of the seasonal cycle of the floodplain.     

Evaluating the Tree Population Density and Its Impacts in CLM-DGVM

Vegetation population dynamics play an essential role in shaping the structure and function of terrestrial ecosystems.However,large uncertainties remain in the parameterizations of population dynamics in current Dynamic Global Vegetation Models(DGVMs).In this study,the global distribution and probability density functions of tree population densities in the revised Community Land Model-Dynamic Global Vegetation Model(CLM-DGVM) were evaluated,and the impacts of population densities on ecosystem characteristics were investigated.The results showed that the model predicted unrealistically high population density with small individual size of tree PFTs(Plant Functional Types) in boreal forests,as well as peripheral areas of tropical and temperate forests.Such biases then led to the underestimation of forest carbon storage and incorrect carbon allocation among plant leaves,stems and root pools,and hence predicted shorter time scales for the building/recovering of mature forests.These results imply that further improvements in the parameterizations of population dynamics in the model are needed in order for the model to correctly represent the response of ecosystems to climate change.     

Earth System Model FGOALS-s2: Coupling a dynamic global vegetation and terrestrial carbon model with the physical climate system model

Earth System Models （ESMs） are fundamental tools for understanding climate-carbon feedback. An ESM version of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS） was recently developed within the IPCC AR5 Coupled Model Intercomparison Project Phase 5 （CMIP5） modeling framework, and we describe the development of this model through the coupling of a dynamic global vegetation and terrestrial carbon model with FGOALS-s2. The performance of the coupled model is evaluated as follows. The simulated global total terrestrial gross primary production （GPP） is 124.4 PgC yr-I and net pri- mary production （NPP） is 50.9 PgC yr-1. The entire terrestrial carbon pools contain about 2009.9 PgC, comprising 628.2 PgC and 1381.6 PgC in vegetation and soil pools, respectively. Spatially, in the tropics, the seasonal cycle of NPP and net ecosystem production （NEP） exhibits a dipole mode across the equator due to migration of the monsoon rainbelt, while the seasonal cycle is not so significant in Leaf Area Index （LAI）. In the subtropics, especially in the East Asian monsoon region, the seasonal cycle is obvious due to changes in temperature and precipitation from boreal winter to summer. Vegetation productivity in the northern mid-high latitudes is too low, possibly due to low soil moisture there. On the interannual timescale, the terrestrial ecosystem shows a strong response to ENSO. The model- simulated Nifio3.4 index and total terrestrial NEP are both characterized by a broad spectral peak in the range of 2-7 years. Further analysis indicates their correlation coefficient reaches -0.7 when NEP lags the Nifio3.4 index for about 1-2 months.     

An investigation of sand–dust storm events and land surface characteristics in China using NOAA NDVI data

Observations from 560 weather stations in China show that sand–dust storms occur most frequently in April in north China. The region consists of Sub-dry Mid Temperate, Dry Mid Temperate, Sub-dry South Temperate and Dry South Temperate Zones and much of the land surface is desert or semi-desert: it is relatively dry with minimal rainfall and a high annual mean temperature. In most regions of China, the annual mean frequency of sand–dust events decreased sharply between 1980 and 1997 and then increased from 1997 to 2000. Statistical analyses demonstrate that the frequency of sand–dust storms correlates highly with wind speed, which in turn is strongly related to land surface features; on the other hand, a significant correlation between storm events and other atmospheric quantities such as precipitation and temperature was not observed. Accordingly, land surface cover characteristics (vegetation, snowfall and soil texture) may play a significant role in determining the occurrence of sand–dust storms in China. Analysis of Normalized Difference Vegetation Index derived from National Oceanic and Atmospheric Administration and Empirical Orthogonal Function show that since 1995 surface vegetation cover in large areas of Northern China has significantly deteriorated. Moreover, a high correlation is shown to exist among the annual occurrence of sand–dust storms, surface vegetation cover and snowfall. This suggests that the deterioration of surface vegetation cover may strongly influence the occurrence of sand–dust storms in China. Soils with coarse and medium textures are found to be more associated with sand–dust storms than other soils.     

Investigations on the runoff generation at the profile and plot scales,Swiss Emmental

This article describes an investigation on runoff generation at different scales in the forested catchment of the Sperbelgraben in the Emmental region (Swiss Prealps) where studies in the field of forest hydrology have a history of 100 years. It focuses on the analysis of soil profiles and the subsequent sprinkling experiments above them (1 m²), as well as on surface runoff measurements on larger plots (50 to 110 m²). In the Sperbelgraben investigation area, two very distinct runoff reactions could be observed. On the one hand, very high production of saturation overland flow was registered on wet areas of gleyic soils, with runoff coefficients between 0·39 and 0·94 for profile irrigation. On the other hand, almost no surface runoff was measured on Cambisols, with the exception at some sites of a hydrophobic reaction detected at the beginning of storms after dry periods (coefficients for profile irrigation: 0·01–0·16). This pattern was observed during 1 m² soil plot irrigation and on surface runoff plots. Apart from a less distinctive signal of the water‐repellent litter layer on the larger surface runoff plots, the dominant hydrological processes at the two scales are the same. The determined runoff reaction at the two scales corresponds well with information from a forest site type map describing soil and vegetation characteristics and used as a substitute for a soil map in this study. Theoretical considerations describing forest influence on flood discharge are discussed and evaluated to be in good agreement with observations. These findings are a sound foundation for application in hydrological catchment modelling. Copyright © 2006 John Wiley & Sons, Ltd.