Xiwu Zhan

Detection of land cover changes using MODIS 250 m data

The Vegetative Cover Conversion (VCC) product is designed to serve as a global alarm for land cover change caused by anthropogenic activities and extreme natural events. MODIS 250 m surface reflectance data availability was limited both spatially and temporally in the first year after launch due to processing system constraints. To address this situation, the VCC algorithms were applied to available MODIS 250 m Level 1B radiance data to test the VCC change detection algorithms presented in this paper. Five data sets of MODIS Level 1B 250 m data were collected for the year 2000, representing: (1) Idaho–Montana wildfires; (2) the Cerro Grande prescribed fire in New Mexico; (3) flood in Cambodia; (4) Thailand–Laos flood retreat; and (5) deforestation in southern Brazil. Decision trees are developed for each of the VCC change detection methods for each of these six cases. These decision trees are to be used for updating the look-up tables required by the VCC production code. For these change detection cases, the VCC change detection methods worked reasonably well. In the Idaho–Montana wildfire case, a fire perimeter polygon data set compiled by the USDA Forest Service was used to validate the output of the VCC change detection methods. Although the VCC output identified only 32% of the burned pixels within the ground observed Idaho–Montana fire perimeter polygons, the detection accuracy of the VCC output did reach 99% when the VCC product is considered as an alarm system identifying the occurrence of the change in an area. For other cases, the detection accuracy in per-pixel terms of the VCC output ranges from 55% to 90% against reference change bitmaps that were created by image interpretation. Look-up tables created with AVHRR and Landsat Thematic Mapper data require modifications for the MODIS data due to differences in radiometric response between MODIS and the heritage instruments. The applications presented in this paper also evaluate the relative performance of each of the five change detection methods used as VCC algorithms. Conclusions reached in this paper will be used for future refinement of the VCC product.

An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications

AbstractComparison of multiple hydrologic indicators, derived from independent data sources and modeling approaches, may improve confidence in signals of emerging drought, particularly during periods of rapid onset. This paper compares the evaporative stress index (ESI)—a diagnostic fast-response indicator describing evapotranspiration (ET) deficits derived within a thermal remote sensing energy balance framework—with prognostic estimates of soil moisture (SM), ET, and runoff anomalies generated with the North American Land Data Assimilation System (NLDAS). Widely used empirical indices based on thermal remote sensing [vegetation health index (VHI)] and precipitation percentiles [standardized precipitation index (SPI)] were also included to assess relative performance. Spatial and temporal correlations computed between indices over the contiguous United States were compared with historical drought classifications recorded in the U.S. Drought Monitor (USDM). Based on correlation results, improved forms for...

Combining optical and microwave remote sensing for mapping energy fluxes in a semiarid watershed

Abstract A dual-source model treating the energy balance of the soil/substrate and vegetation that was developed to use radiometric surface temperature observations is revised to use remotely sensed near-surface moisture from a passive microwave sensor for estimating the soil surface energy balance. With remotely sensed images of near-surface soil moisture, land cover classification, and leaf area index, the model is applied over a semiarid area in the Walnut Gulch Watershed in southern Arizona. The spatial and temporal variation of the Bowen ratio (i.e., the ratio of the turbulent fluxes, sensible, and latent heat) “maps” generated by the model were similar to the changes in near-surface moisture fields caused by recent precipitation events in the study area. The estimated fluxes at the time of the microwave observations (i.e., “instantaneous” estimates) and those simulated over the daytime period are compared with the ground observations within the watershed. Differences between predicted and observed “instantaneous” fluxes were usually comparable to the measurement uncertainties, namely, 5% for net radiation and 20–30% for soil, sensible and latent heat fluxes, except when there was large temporal and spatial variations in solar radiation across the study area. However, by running the model over the daytime period, this variability in solar radiation proved to have a minor effect on computed daytime totals. In fact, differences with observed heat fluxes were significantly less (i.e., around 15%) than when comparing “instantaneous” values. Model predictions of the total soil heat flux over the daytime period were generally higher than measured. An empirical model was developed to reduce this bias, but it is not known how generally applicable it will be. Model sensitivity to typical uncertainties in remotely sensed leaf area index (LAI) and near-surface (0–5 cm) water content, W, was quantified. The variation in flux predictions caused by errors in prescribing leaf area index and W was less than 30%. More tests with this model over different landscapes are necessary to evaluate its potential for predicting regional fluxes. In particular, microwave and radiometric surface temperature observations are needed under drought conditions for evaluating if the model formulation of vegetation transpiration can properly adjust to this extreme and very important environmental condition.Published by Elsevier Science Inc., 1998

Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model

AbstractMany studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged sur...

Land cover change detection with change vector in the red and near-infrared reflectance space

An enhanced land cover change indicator product is produced using the two 250-m spatial resolution bands of the moderate resolution spectroradiometer (MODIS) of the NASA Earth Observing System. The rationale for creating the 250-m resolution land cover change product is that a very high proportion of land cover changes occur at the finest MODIS spatial resolutions. Multiple change detection algorithms are employed for the product generated because different algorithms detect different types of change. Among these algorithms there are two using the change vector in the red and near-infrared reflectance space. An early example of using change vector analysis for change detection is in Malila (1980). A more recent example is Johnson and Kasischke (1998). In these examples the general concepts of multispectral change vector analysis were described and applied to detect vegetation changes for the cases in specific locations and seasons. In this paper, the change vector in the red and near-infrared (NIR) reflectance space is analyzed for different types of relevant land cover changes. The algorithms using the change vector characteristics obtained with NOAAs Advanced Very High Resolution Radiometer (AVHRR) data to detect the changes are then described. Validations of these two algorithms with the simulated MODIS data from Landsat Thematic Mapper (TM) images of two different areas are presented.

Red and infrared space partitioning for detecting land cover change

One of the algorithms included in producing the change detection product for the Moderate Resolution Imaging Spectroradiometer (MODIS) of NASAs Earth Observing System (EOS) employs directly partitioning the red/infrared spectral space in order to detect human-induced land cover change. Monthly look-up tables for tall woody vegetation, short herbaceous/woody vegetation, and bare ground have been created to detect spectral migration from one cover type to another. Using training data from the 8 km Pathfinder data set and the University of Marylands 1 km global classification, a classification tree algorithm was used to partition the monthly look-up tables for all 12 months across four regions of the globe. The globe was split into northern tropical (0-23.5 degrees north), northern extra tropical (>23.5 degrees north) southern tropical (0-23.5 degrees south) and southern extra tropical (>23.5 degrees south) regions. The classification tree was used to find core areas approaching 100% accuracy for each of the 5 classes. Spectral boundaries between classes were revealed in the tree structure by poor classification accuracies, and were labelled as mixed areas. In this manner the problem of overlaying two full classifications will be avoided as mixed pixels will be labelled as such, and only pixels migrating from one core area to another will be labelled as change. Compensation for phenological changes is built into the algorithm as the look-up tables are made for each month in each region. For example, in the northern extra tropical region winter look-up tables have a much smaller percentage of core areas present in the red-infrared space compared to summer look-up tables, due to seasonal brown-off and snow cover. Results from two test areas reveal the utility of this method, particularly in reducing errors of commission.

A simple assessment of the expansion of American urban areas from 1970s to 1990s using the North American Landscape characterization data set

The triplicates of the North American Landscape Characterization (NALC) data set are used to assess the expansion of 10 large urban areas in the United States in two time periods since 1970s. For each of the ten cities, an image window covering the city is selected from the corresponding triplicate. For each site, a decision tree classifier is applied to classify the urban, suburban developed/residential areas from the other land use (the undeveloped/non-residential areas) for the three dates, and to detect the areas changed from vegetated/non-residential areas to developed/residential land surface types in the two time periods (from date 1 to date 2 and from date 2 to date 3). The classification maps are then examined and adjusted for accuracy using visual inspection of the images and the available ancillary maps. The adjusted maps are then used to calculate the percentage increases of the urban and suburban areas. The calculated percentages indicate that cities in western United States were experiencing faster expansion than cities in the eastern US. The increase of residential areas in the time period from 1973 to 1986 ranges from 93% for Las Vegas, NV to only 5% for Washington, DC. In the time period from 1986 to 1991, the increase spreads from more than 37% for Las Vegas, NV to only 3% for Washington, DC again. When percentage increases of the developed/residential areas are plotted against population of the city, significant linear relationships are found for most of the 10 cities.

Application of vegetation continuous fields data in atmosphere-biosphere interaction models

Global atmosphere-biosphere interaction models and biogeochemical models currently use land-cover classifications that describe one single cover type in each grid cell. However, much of the land surface is made up of a heterogeneous mixture of trees, shrubs, grasses, crops, litter, rocks and bare soil. This heterogeneity exists over a large range of scales from a small patch of a few square meters to a 4 by 5 degree grid cell used in general circulation models of the atmosphere. To better describe this heterogeneity of land cover, a vegetation continuous fields (VCF) data set is being generated with satellite remote sensing data. The authors explore how the new land cover data set can be utilized to improve the estimates of the land surface biophysical parameters in two major examples of the global atmosphere-biosphere interaction models: the Simple Biosphere Model (SiB2) and the Biosphere-Atmosphere Transfer Scheme (BATS).

NOaa Soil Moisture Operational Product System (SMOPS) and its validations

Global soil moisture is one of the critical land surface initial conditions for numerical weather, climate, and hydrological predictions. Since it is not practical to provide global maps using ground measurements, land surface soil moisture remote sensing has been a hot research topic in the last several decades. As a result, a number of soil moisture products have been produced from different satellite sensors with different spatial and temporal coverage and quality. To make effective use of all available soil moisture products, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration (NOAA) to produce a one-stop shop for all operational soil moisture products from different satellite sensors. To increase the spatial and temporal coverage of soil moisture product, SMOPS also provides a data layer that merges soil moisture retrievals from multiple satellites in addition to the individual soil moisture retrievals from each of the available satellites. This paper gives an overall introduction of SMOPS and its validations using ground measurements.

Future Opportunities and Challenges in Remote Sensing of Drought

15.1 INTRODUCTION Snow cover is an important earth surface characteristic because it influences partitioning of the surface radiation, energy, and hydrologic budgets. Snow is also an important source of moisture for agricultural crops and water supply in many higher latitude or mountainous areas. For instance, snowmelt provides approximately