Jiaguo Qi

Planar domain indices: a method for measuring a quality of a single component in two-component pixels

A method is presented that reduces the difficulty of measuring a particular quality of one component in a multi-component element. The planar domain index design requires two measurements: that of a signal sensitive to the desired quality of the target component and another signal sensitive to the components weight or relative proportion to the whole. The quality signal and component weight signal form the two dimensions of a plane, and the maximum and minimum possible values for each signal define the boundaries of a domain within this plane. The position of a coordinate pair within the domain can then be correlated to the quality being measured, independent of the components proportion to the whole. Examples given involve mixed vegetation and soil targets, with vegetation indices used to measure the component weight. Quality signals of the example applications include canopy minus air temperature as a measure of evapotranspiration, a normalized difference of near infrared and far red wavelengths as a measure of chlorophyll content, and differential synthetic aperture radar (SAR) for measuring near-surface soil moisture.

Developing land use/land cover parameterization for climate–land modelling in East Africa

Regional climate modeling studies now have numerous choices in selecting land use/land cover (LULC) products to provide land surface parameter information. The various LULC products were developed with different objectives, methods and data sources. Not all new LULC products have land classes that match the land class types defined in climate models. More importantly, when used in regional climate models, simulation results can vary significantly depending on the LULC products. Thus, developing appropriate LULC parameterization for climate models becomes critical depending on objectives and efforts. The objective of this paper is to develop the most accurate LULC scheme possible for East Africa for implementation in the Regional Atmospheric Modeling System (RAMS). A crosswalk procedure, based on assessments of various LULC products, was performed connecting land class types in RAMS and the newly created LULC scheme. No simulations are discussed here; rather, we present an outline of the procedures that were carried out to take advantage of the strengths of currently available LULC products, Africover and Global Land Cover 2000, for the purpose of conducting regional climate simulations.

Biophysical attributes estimation from satellite images in arid regions

To efficiently manage limited rangeland resources, land managers and rangeland users require accurate and timely geospatial data products concerning the health, productivity and biomass of the rangeland area. Traditional approaches to deriving these products are limited in and and semi-arid regions due to several reasons. First, in and and semi-arid regions, vegetation is often very sparse and therefore signals from vegetation are often much smaller than those from soil backgrounds. Second, vegetation in and and semiarid regions is often in senescent form and therefore is unable to be related to traditional spectral vegetation indices that were specifically designed to be sensitive to green materials. In this study, we developed new techniques that circumvent these limitations by exploring the use of the shortwave infrared spectral bands of the Landsat images. These spectral bands are very sensitive to water content of pixel elements. The inclusion of SWIR spectral bands in spectral vegetation indices results in sensitive indices to both green and senescent vegetation. The products derived from this study include fractional green grass cover, fractional senescent grass cover, biomass or forage, and canopy height in and grass dominated rangelands in the Southwest from Landsat7 ETM+ imagery. Because the algorithms used are simple, they can be used operatically to produce these geospatial products to assist rangeland managers in making optimal management decisions.

An assessment of africover and GLC2000 using general agreement and videography techniques

Remote sensing science has grown to provide a range of land use land cover datasets. These datasets range in creation methodologies, objectives, and validation levels. This study evaluates Africover and Global Land Cover for the year 2000 (GLC) land use land covers for East Africa as land use land cover input for climate and land modeling research. Africover was produced from multi-temporal Landsat data under a relatively new classification scheme primarily using visual interpretation methods. GLC utilized the VEGETATION instrument onboard the Satellite Pour l’Observation de la Terre (SPOT) 4 using a similar classification scheme. A two-tiered assessment approach examining general class agreement and airborne videography techniques were performed. Differences between the products show moderate general agreement with a wide range of class level agreement. The video data was useful in developing both qualitative and quantitative assessment measures. Agricultural land use dynamics were particularly problematic causing much of the misclassification. The general objective was to assess and evaluate the land covers and their potential applications. Introduction Land use land cover change is a fundamental component of natural resource and global change research. These data represent a range of biophysical parameters and socioeconomic indicators thusly detailed and accurate land use land cover (LULC) information are vital to scientists, managers, and decision makers. The availability and quality of regional to continental scale remotely sensed LULC products have increased greatly in the past decade. At the same time remote sensing science and computational ability advances have made the incorporation of more advanced land cover products and statistical techniques possible in LULC mapping and monitoring. The capabilities and level of detail for regional to global modeling studies will benefit immensely from newer LULC data. When created by different agencies or groups, LULC datasets can often differ in temporal coverage, spatial resolutions, objectives, classification methodologies, and validation levels. This makes direct comparisons challenging and can limit applications. A range of assessment techniques provide approaches to deal with evaluation concerns. In this study assessments are needed to examine dataset validity and utility, illustrate strengths and weaknesses, and determine the overall quality and accuracy of the information. Numerous studies and methods have been implemented for assessing LULC products. These include both qualitative and quantitative evaluations. Two general methods of assessment, all with variations, are abundant throughout the literature. The first method is becoming more common and is identified as appropriate to evaluate larger scale LULC data. This method is a comparison of the levels of agreement/disagreement between general land categories of two generated LULC products. The second method is to examine a remotely sensed product against ground truthed or reference data, often finer resolution. Aerial video data capture, or videography, for land cover and vegetation condition assessment has proven to be one such useful method. Advances in digital technology and computer integration have increased the applicability of airborne video as a remote sensing tool. There are distinct logistical advantages to utilizing airborne video data in isolated and environmentally sensitive regions where there is limited preexisting aerial photography and poor infrastructure and where ground accessibility is difficult and expensive [1]. Study Site The study site is located in East Africa. For this assessment a two tiered study site was developed. The first level of assessment includes the countries of Burundi, Kenya, Rwanda, Uganda, and Tanzania. The second level of the assessment includes two aerial flight paths located in central and southern Kenya. Within the study sites, a wide range of LULC are present. Biophysical vegetation cover ranges from expansive savannas to dense forest to riparian wetlands. Land uses include a variety of human activities from intense agriculture to open cattle ranching to conservation. Data Africover is a project administered by the United Nations Food and Agricultural Organization (UN FAO) that was approved in 1994. Africover was initiated in response to requests to develop information on natural resources required at national and regional levels. The purposes range from disaster and early warning systems to agriculture and food security to biodiversity and global change research [2]. Africover methodology included combining digital automatic classification with traditional visual interpretation at 1:200:000 scale off Landsat satellite imagery. For smaller countries and specific areas 1:100,000 scale was used. A 21class regional aggregate for East Africa was evaluated for the study region. 5005 0-7803-9050-4/05/

Biophysical evaluation of five land covers for land-climate interaction modeling in East Africa

20.00 ©2005 IEEE. 5005 Africover needs assisted in generating the Land Cover Classification System (LCCS) developed by the UN FAO. The LCCS is a hierarchical, priori system that in theory can be applied to any region of the globe for comparison purposes [3]. The categorical assignment includes broad sweeping LULC classes followed by a series of descriptive characteristics. Determining parameters include vegetation or non-vegetated surfaces, terrestrial or aquatic, cultivated and managed, natural and semi-natural, life-form, cover layer, feature height, spatial distribution, leaf type and phenology. Global Land Cover 2000 is available from the Joint Research Centre’s Global Vegetation Monitoring Unit. The overall objective in creating GLC was to provide a harmonized land cover database for the globe as part of the Millennium Ecosystem Assessment. The database was designed to serve users from science programs, policy makers, environmental convention secretariats, international and non-governmental organizations and development-aid projects [4]. More than 30 partner institutions participated in the project utilizing 14 months of pre-processed VEGETATION sensor data via the System Pour l’observation de la Terre (SPOT) 4 satellite. The data used was collected between 11/1/1999 – 12/31/2000. The classification methodology was based off the UN FAO LCCS for compatibility purposes. A continental Africa version was evaluated here. Methods For the general agreement analysis each land cover class was aggregated into one of nine general land cover classes. Using the LCCS scheme, aggregating to broad classes for comparison purposes is systematic. However, the classifications were performed by different multi-teamed groups at different scales possibly resulting in different interpretations. The videography was recorded onboard an aircraft flown at a mean altitude of 1000 meters above sea level in order to capture land cover at fine scale. A Global Positioning System recorded location information during the flight directly to the footage. Video tape data was transferred digitally at a rate of 30-frames per second via an IEEE 1394 FireWire cable. The capture resolution was at 720x480 in accordance with National Television System Committee standards. Approximately five hours was recorded over the 900 kilometers traveled. The field of view (FOV) was calculated in order to geolink the video data to the LULC data. The GPS points were re-sampled to a representative level for accurately depicting the airplane’s flight path over the landscape. The GPS points were treated as network nodes and snapped together to form a polyline flight path. Two flight line FOV shapefiles were created and merged together as an end result. The land covers were clipped using the flight line FOV shapefile. Area estimates were generated to identify classes captured and assist in sampling design. The FOV area was calculated to cover approximately 12.6 hectares per frame. The number of possible FOVs, or sampling units in this case, was calculated by dividing the area of each class by the FOV area. For Africover, this resulted in a range of possible samples from 11 for irrigated agriculture to 772 for tree and shrub savanna. The number of possible FOVs for GLC ranged from 8 for sandy desert and dunes to 844 for open grassland with sparse shrubs. The georegistered digital video was then scanned through finding a stratified random sampling scheme of each GPS point to determine whether that point was correctly or incorrectly classified. The interpretation process included three individuals judging a sample point together. A sampling point, or area in a frame in which that point was located, was determined whether the classification was correct or incorrect. Results Using the LCCS framework both GLC and Africover classes were aggregated into broad land categories to examine general agreement/disagreement. All classes were grouped into the broad level classes of forest, woodland/shrubland, grassland, agriculture, barren, water, and urban. The overall agreement between GLC and Africover at general levels using the LCCS framework is approximately 41% calculated from the confusion table (Table 1). Class agreement was calculated for each land category. Measuring Africover against GLC, class agreement ranged between 0.3% 90%. Class agreements levels for forest, woodland/shrubland, grassland, agriculture, barren, water, and urban were 22%, 22%, 58%, 37%, 0.32%, 90%, and 11% respectively. Measuring GLC against Africover for forest, woodland/shrubland, grassland, agriculture, barren, water, urban class agreement produced 21%, 16%, 46%, 54%, 13%, 92%, and 63% respectively. Using the video footage to build an error matrix for Africover, for the flightlines sampled, an overall accuracy of 55% (213/391) was calculated. No correct sample points were identified for open to closed grassland. Aquatic agriculture which had a small regional set of samples, was identified as mostly correct. Closed trees also had relatively high accuracy.

Improved selective logging detection with Landsat images in tropical regions

A statistical measure, Q, was developed to evaluate five land cover products using monthly MODIS Leaf Area Index (LAI) product. This method spatially aggregates within-class LAI variation over a year period to a single statistic (mean Q) for the whole study area. The smaller mean Q value for a land cover product indicates the more consistent vegetation structural characteristic within class and the more appropriate land cover classification. The five land covers assessed in this paper include GLC2000, MODIS IGBP, Africover, OGE and LEAF. The evaluation was conducted at three different spatial scales corresponding to 30×30, 50×50 and 100×100 km quadrats. We found that GLC2000 is ranked best compared to the other four land cover products for every quadrat size. For the 30×30 km quadrat size GLC2000 is significantly better than LEAF cover which is currently used in the Regional Atmospheric Modeling System (RAMS). The statistic ranks MODIS IGBP better than Africover, which is better than OGE.

Water quality assessment using hyperspectral remote sensing

Logging is a major form of forest degradation in the tropical regions like Brazilian Amazon. It alters the tropical habitat environments and results in release of carbons as well. The traditional way of logging is through forest clearing, which converts forest to other land uses such as agriculture or rangeland. Recently a new form of forest degradation is selective logging, removing only those good quality tree species. This form of deforestation does not result in land use conversion, but degradation. Logging by means of clear-cutting can be easily detected and monitored from satellite images such as those from Landsat sensors. However, detection and monitoring selective logging is difficult with satellite images because the process only removes a small number of trees per area, resulting in subtle disturbances but substantial removal of biomass. Therefore, traditional classification technique is unable to detect and monitor this type of disturbances effectively. In order to detect selective logging, and to better understand carbon sequestrations, a continuous field ought to be used that can quantify the degree of disturbances due to selective logging, instead of using binary classification techniques. In this paper, we used signal-unmixing techniques in a spectral vegetation index domain as a continuous field measure of forest density, with which selective logging is mapped and quantified. The spectral index used is the MSAVI further modified to enhance its sensitivity to subtle forest degradations in the tropical environments in Brazilian Amazon as well as in Southeast Asia.

Water quality model parameters inversion based on improved stochastic optimization

High spectral resolution remote sensing imagery has been successfully used to estimate spatial and temporal variation of optical water quality parameters such as chlorophyll-a, transparency, and suspended solids, primarily for marine and coastal waters. Although physicochemical properties of sea water and inland water are slightly different, they are similar enough for hyperspectral remote sensing to become an alternative tool for inland lakes monitoring. A field campaign was conducted in the spring and summer 2004 in Michigan, USA. A total of 191 water samples were collected at multiple depths from 46 lakes and were analyzed for Chlorophyll-a, suspended solids, total nitrogen, total phosphorus, Secchi disk transparency, light conditions, and temperature profiles. Geographic locations of the sample sites were recorded, and over 5,000 lake reflectance measurements were collected using a field spectroradiometer. The hyperspectral data were used to specify the regions of wavelengths that best respond to changes of water quality parameters. Principal components analysis (PCA) and regression technique were used to examine the relationship between water quality parameters and spectral reflectance. The quantitative relationship between remotely sensed variables and water quality indicators can be potentially used to extrapolate point-based water quality measurements to large spatial extents for an improved water quality assessment.

Land cover impacts on regional climate simulations

As inherent optical properties (IOPs) are directly related to the constituents in the water, the condition of water quality can be reflected by fundamental IOPs absorption and scattering coefficients. And these values can be derived by analytically inverting the remote sensing spectral reflectance. In this paper, the relations between the remote sensing reflectance and water quality information are established, and the model parameters of water quality are obtained by stochastic optimization. Based on Threshold Accepting algorithm, a method with the improved searching strategy and new optimization criteria is proposed to find optimal parameters for the inversion model. The experiments conducted on the simulated data and real data, which indicate that through the division of optimization parameters and the use of different search methods, the accuracy of inversion and operational efficiency can be improved.

Hyperspectral Remote Sensing in Global Change Studies

Land cover products are being generated with increased spatial resolutions for all regions of the earth. These products provide new opportunities to more accurately depict land surface conditions as input to soil-vegetation-atmosphere transfer (SVAT) schemes in climate models. The impact resulting from using different land cover representations needs to be addressed in order to gauge land cover performance and choice. A regional climate model was used to compare land surface temperatures among three land cover products. Large differences among the products were evident from simulations conducted over a 5 month interval in East Africa.