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Zhiliang Zhu

Publications and source records attributed to Zhiliang Zhu.

65 records · Page 4Linked to original sources
USGS2006

Perspectives on LANDFIRE Prototype Project accuracy assessment

The purpose of this chapter is to provide a general overview of the many aspects of accuracy assessment pertinent to the Landscape Fire and Resource Management Planning Tools Prototype Project (LANDFIRE Prototype Project). The LANDFIRE Prototype formed a large and complex research and development project with many broad-scale data sets and products developed throughout its various stages. The scope of the project was defined as mapping and modeling vegetation, wildland fuel, and fire regime characteristics (Rollins and others, Ch. 2). Because of the breadth of the investigation, it is important to base our expectations for accuracy on a clear understanding of the intricacies, interdependencies, and scope of mapping and modeling LANDFIRE products. Our goals in this chapter are to: 1) provide relevant background information regarding accuracies and what was realistically achievable in the LANDFIRE Prototype, 2) provide background regarding our strategies for LANDFIRE National, 3) describe our actual LANDFIRE Prototype accuracy results in broad terms, and 4) provide recommendations for the national implementation of LANDFIRE. This chapter is not intended to provide an exhaustive list and description of all of the various accuracy-related issues and conclusions resulting from the LANDFIRE Prototype (for specific details, the reader will be referred to the appropriate chapters). Rather, this chapter is intended to be broad in scope and to place the many accuracy components within the context of the LANDFIRE Prototype and LANDFIRE National projects. Please note that Lunetta and Lyon (2004) provide an in-depth discussion of the current state of accuracy assessment within the science community.

General Technical Report↗
USGS2006

Executive summary

Geospatial data describing wildland fuel and current as well as historical vegetation conditions are essential for planning, implementing, and monitoring projects supported by the National Fire Plan and the Healthy Forests Restoration Act. Scientifically credible, consistent, and standardized spatial data allow fire and land managers to accurately identify the amount and locations of lands or communities with hazardous fuel build-up or extreme departure from historical conditions. These data also facilitate the prioritization of ecosystem restoration and hazardous fuel reduction treatments to protect ecosystems, property, and people. Moreover, these data may be used during specific wildland fire incidents to maximize firefighter safety, pre-position resources, and evaluate fire behavior under a variety of weather conditions.

General Technical Report↗
USGS2006

An overview of the LANDFIRE Prototype Project

This chapter describes the background and design of the Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype Project, which was a sub-regional, proof-of-concept effort designed to develop methods and applications for providing the high-resolution data (30-m pixel) needed to support wildland fire management and to implement the National Fire Plan and Healthy Forests Restoration Act. In addition, this chapter provides synopses of the many interrelated procedures necessary for development of the 24 LANDFIRE Prototype products (see table 1 and appendix 2-A). Throughout this chapter, direction is provided for where, in this report and elsewhere, additional detailed information is available.

General Technical Report↗
USGS2005

Integrating habitat status, human population pressure, and protection status into biodiversity conservation priority setting

Priority setting is an essential component of biodiversity conservation. Existing methods to identify priority areas for conservation have focused almost entirely on biological factors. We suggest a new relative ranking method for identifying priority conservation areas that integrates both biological and social aspects. It is based on the following criteria: the habitat's status, human population pressure, human efforts to protect habitat, and number of endemic plant and vertebrate species. We used this method to rank 25 hotspots, 17 megadiverse countries, and the hotspots within each megadiverse country. We used consistent, comprehensive, georeferenced, and multiband data sets and analytical remote sensing and geographic information system tools to quantify habitat status, human population pressure, and protection status. The ranking suggests that the Philippines, Atlantic Forest, Mediterranean Basin, Caribbean Islands, Caucasus, and Indo-Burma are the hottest hotspots and that China, the Philippines, and India are the hottest megadiverse countries. The great variation in terms of habitat, protected areas, and population pressure among the hotspots, the megadiverse countries, and the hotspots within the same country suggests the need for hotspot- and country-specific conservation policies.

Conservation Biology↗
USGS2003

Deriving annual integrated NDVI greenness at 30 m spatial resolution

Temporal greenness matrics have been found useful for characterizing vegetation phenology, and have been used to discriminate vegetation cover types and to estimate key vegetation attributes including percent cover and green biomass. So far, however, such matrics have been calculated only from coarse resolution satellite data. Intermediate spatial resolution satellites like Landsat cannot provide the temporal resolutions needed for directly calculating such greenness matrics. In this study, we developed a method to indirectly derive annual integrated NDVI at 30 m spatial resolution using 250 m MODIS data and 30 m Landsat ETM+ imagery. Results showed that more than 90% of the variance of the annual integrated NDVI calculated using one full year’s MODIS data could be explained using as few as 3 appropriately selected observations, demonstrating the feasibility of indirectly estimating the annual integrated NDVI at intermediate spatial resolutions, as normally only limited number of useful observations would be available within the life cycle of a typical project at such spatial resolutions. The developed method was applied to two ETM+ paths/rows, for each of which 3 ETM+ images were acquired in roughly spring, summer and fall/winter seasons around the year 2000. Of the total variance of the MODIS annual integrated NDVI, 81% was explained by the three ETM+ images for one path/row and 74% for the other.

Conference Paper↗
USGS2003

Predictive modeling of forest cover type and tree canopy height in the central Rocky Mountains of Utah

Maps of forest cover type and canopy height are needed for LANDFIRE, a multi-scale fire risk assessment project designed to generate intermediate-resolution data of vegetation and fire fuel characteristics for the U.S. Here we describe an evaluation study in the central Rockies of Utah, comparing tree-based methods, multivariate adaptive regression splines (MARS), and a hybrid method for mapping forest cover and canopy height on the basis of more than 2,000 forest inventory ground plots in the seven million ha mapping zone. The two forest attributes were modeled as functions of a variety of predictor variables, including: Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images acquired at three different seasons; Tasseled-cap brightness, greenness, and wetness; a forest type group map; and topographic variables derived from Digital Elevation Models (DEMs); and other ancillary variables. The hybrid modeling approach showed a marked increase in overall and within forest cover type accuracies, outperforming the tree-based and MARS approaches. Little difference was seen in global performance measures of forest canopy height models, but patterns in residual plots resulting from different modeling approaches raise questions about utility of height predictions in different applications.

Idaho, Utah, Wyoming↗
USGS2003

Exploration of satellite-measured vegetation seasonality for Landfire land cover

The purpose of this study is to explore the use of satellite data and other sources of spatial data for large area classification in the western United States to support research on potential fire hazards. Extensive field information was made available to this project from two sources: Forest Inventory and Assessment (FIA) and Utah State University. Seasonal spectral patterns of reflectance generated for select vegetation communities indicated that substantial spectral changes occurred through the growing season for most land cover types. In many cases, pronounced spectral differences characterized different types of vegetation, indicating a high probability that classification will accurately separate these particular types of land cover. However, spectral similarities between other types of land cover, such as Douglas fir and white fir, indicate potential classification challenges. Results from this study also show that decision tree analysis is highly effective for assessing quality of input field data and for generating large area land cover classification data sets. It was found that a 5-7% improvement in classification results could be achieved simply by not using those field plots that appeared to be sub-optimal for classification purposes based on image interpretation.

Conference Paper↗
USGS2002

Application of decision-tree techniques to forest group and basal area mapping using satellite imagery and forest inventory data

Accurate, current, and cost-effective fire fuel data are required by management and fire science communities for use in reducing wildland fire hazards over large areas. In this paper we present results of applying decision-tree techniques to mapping vegetation parameters (such as vegetation types and canopy structure classification) required for fire fuel characterization. Specifically, we present preliminary results of mapping forest types and average basal area by different forest types at 30-meter resolution. Input data into the decision tree model included Landsat-7 ETM+ spring, summer and fall greenness, brightness and wetness of the tasseled cap transformation, topographic data layers such as slope and elevation, and forest variables measured on inventory plots in the Mid-Atlantic region. Using decision-tree models, eight forest types were successfully identified in training cases and mapped for the entire mapping area. Forest basal area per unit area (conifer and deciduous) was estimated as well using regression tree models. Cross-validation conducted for both forest types and basal area showed that discrete forest type estimation error was 35% and continuous basal area relative errors were between 58 and 72%. Accuracy was higher in homogeneous forested lands and lower in areas with fragmented forest cover. The study demonstrated that decision tree and regression tree methods are efficient for large-area vegetation mapping if sufficient large-amount of reference data are available.

Conference Paper↗
USGS2002

Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping

FIA plot data were used to assist in classifying forest land cover from Landsat imagery and relevant ancillary data in two regions of the U.S.: one around the Chesapeake Bay area and the other around Utah. The overall accuracies for the forest/nonforest classification were over 90 percent and about 80 percent, respectively, in the two regions. The accuracies for deciduous/evergreen/mixed and forest type group classifications were around 80 percent and 65 percent, respectively, and were consistent in the two regions. These results suggest that use of FIA plot data together with satellite imagery and relevant ancillary data may substantially improve the efficiency, accuracy, and consistency of large area forest land cover mapping.

Conterminous United States↗
USGS1999

An analysis of IGBP global land-cover characterization process

The international Geosphere Biosphere Programme (IGBP) has called for the development of improved global land-cover data for use in increasingly sophisticated global environmental models. To meet this need, the staff of the U.S. Geological Survey and the University of Nebraska-Lincoln developed and applied a global land-cover characterization methodology using 1992-1993 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) and other spatial data. The methodology, based on unsupervised classification with extensive postclassification refinement, yielded a multi-layer database consisting of eight land-cover data sets, descriptive attributes, and source data. An independent IGBP accuracy assessment reports a global accuracy of 73.5 percent, and continental results vary from 63 percent to 83 percent. Although data quality, methodology, interpreter performance, and logistics affected the results, significant problems were associated with the relationship between AVHRR data and fine-scale, spectrally similar land-cover patterns in complex natural or disturbed landscapes.

Photogrammetric Engineering and Remote Sensing↗