Land Use Database Research Articles

Analysis and simulation of the spatiotemporal evolution pattern of tourism lands at the Natural World Heritage Site Jiuzhaigou, China

This study researched the Natural World Heritage Site of Jiuzhaigou, China, which has a vulnerable ecological environment and booming tourism industry. Using satellite imaging with field calibration, we established the site's land-use databases for 2005 and 2015, analysed tourism land-use transformations, determined the driving mechanism behind land-use, and simulated the spatial patterns of tourism lands for 2025 and 2035. The results revealed that, during 2005–2015, the heritage site underwent dramatic land-use/land-cover change from tourism development. The tourism functions have been becoming more similar. Moreover, the distribution of tourism lands was determined by elevation and slope, as well as distances to transportation lands, watersheds, and existing lands. Between 2025 and 2035, the evolution of tourism lands is projected to gradually slow down, while the tourism functions of each village would still be dominated by accommodation and catering.

Analysis of large-scale flood inundation area using optimal topographic factors

Recently, the spatiotemporal patterns of flood disasters have become more complex and unpredictable due to climate change. Flood hazard map including information on flood risk level has been widely used as an unstructured measure against flooding damages. In order to product a high-precision flood hazard map by combination of hydrologic and hydraulic modeling, huge digital information such as topography, geology, climate, landuse and various database related to social economic are required. However, in some areas, especially in developing countries, flood hazard mapping is difficult or impossible and its accuracy is insufficient because such data is lacking or inaccessible. Therefore, this study suggests a method to delineate large scale flood-prone area based on topographic factors produced by linear binary classifier and ROC (Receiver Operation Characteristics) using globally-available geographic data such as ASTER or SRTM. We applied the proposed methodology to five different countries: North Korea Bangladesh, Indonesia, Thailand and Myanmar. The results show that model performances on flood area detection ranges from 38% (Bangladesh) to 78% (Thailand). The flood-prone area detection based on the topographical factors has a great advantage in order to easily distinguish the large-scale inundation-potent area using only digital elevation model (DEM) for ungauged watersheds.

Green space definition affects associations of green space with overweight and physical activity

IntroductionIn epidemiological studies, exposure to green space is inconsistently associated with being overweight and physical activity, possibly because studies differ widely in their definition of green space exposure, inclusion of important confounders, study population and data analysis. ObjectivesWe evaluated whether the association of green space with being overweight and physical activity depended upon definition of greenspace. MethodsWe conducted a cross-sectional study using data from a Dutch national health survey of 387,195 adults. Distance to the nearest park entrance and surrounding green space, based on the Normalized Difference Vegetation Index (NDVI) or a detailed Dutch land-use database (TOP10NL), was calculated for each residential address. We used logistic regression analyses to study the association of green space exposure with being overweight and being moderately or vigorously physically active outdoors at least 150min/week (self-reported). To study the shape of the association, we specified natural splines and quintiles. ResultsThe distance to the nearest park entrance was not associated with being overweight or outdoor physical activity. Associations of surrounding green space with being overweight or outdoor physical activity were highly non-linear. For NDVI surrounding greenness, we observed significantly decreased odds of being overweight [300m buffer, odds ratio (OR) = 0.88; 95% CI: 0.86, 0.91] and increased odds for outdoor physical activity [300m buffer, OR = 1.14; 95% CI: 1.10, 1.17] in the highest quintile compared to the lowest quintile. For TOP10NL surrounding green space, associations were mostly non-significant. Associations were generally stronger for subjects living in less urban areas and for the smaller buffers. ConclusionAssociations of green space with being overweight and outdoor physical activity differed considerably between different green space definitions. Associations were strongest for NDVI surrounding greenness.

Effects of Explicit Urban-Canopy Representation on Local Circulations Above a Tropical Mega-City

The Advanced Regional Prediction System (ARPS) is coupled with the tropical town energy budget (tTEB) scheme to analyze the effects of the urban canopy circulation over the metropolitan area of Sao Paulo and its interactions with the sea breeze and mountain-valley circulation in the eastern state of Sao Paulo, Brazil. Two experiments are carried out for the typical sea-breeze event occurring on 22 August 2014 under weak synoptic forcing and clear-sky conditions: (a) a control run with the default semi-desert surface parametrization and; (b) a tTEB run for the urban canopy of Sao Paulo. A realistic land-use database over the south-eastern domain of Brazil is used in the downscaling simulation to a horizontal grid resolution of 3 km. Our results indicate that ARPS effectively simulates features of the nighttime and early morning land-breeze circulation, which is affected by the surrounding hills and the nocturnal heat island of Sao Paulo. By early afternoon, the south-eastern sea-breeze circulation moves inland perpendicular to the upslope of the Serra do Mar scarp, which generates a line of moisture convergence and updrafts further inland. Later, the convergence line reaches Sao Paulo and interacts with the circulation arising from the urban heat island (UHI), which increases the moisture convergence and strength of updrafts. The surface energy balance indicates that the UHI is caused by large sensible heat storage within the urban canopy during the day, which is later released in the afternoon and at night. The simulations are verified with available radiosonde and surface weather station data, land-surface-temperature estimates from the moderate resolution imaging spectroradiometer, as well as the National Center for Atmospheric Research reanalysis databases. The three-dimensional geometry of the urban canyons within the tTEB scheme consistently improves the thermodynamically-induced circulation over Sao Paulo.

Open land-use map: a regional land-use mapping strategy for incorporating OpenStreetMap with earth observations

A land-use map at the regional scale is a heavy computation task yet is critical to most landowners, researchers, and decision-makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating land classification maps at the regional scale: the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time. Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens. As one of the most well-known VGI initiatives, OpenStreetMap (OSM), contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns. Whereas, most large-scale mapping approaches – including regional and national scales – confuse “land cover” and “land-use”, or build up the land-use database based on modeled land cover data-sets, in this study, we clearly distinguished and differentiated land-use from land cover. By focusing on our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery. Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work. Furthermore, our novel approach in regional scale land-use mapping produced robust results in our study area: the overall internal accuracy of the classifier was 95.2% and the external accuracy of the classifier was measured at 74.8%.

Land use and land cover change and its driving forces in Maqu County, China in the past 25 years.

Maqu County is located in the northeast Qinghai-Tibetan Plateau, and it is the main watershed for the Yellow River. The ecosystem there is extremely vulnerable and sensitive to climate change and human activities, which have caused significant deterioration of the eco-environment in this region. In order to restore the ecological environment, a government project to restore the grazing areas to grassland was implemented in Maqu County in early 2004. This study evaluates the effects of that restoration project on land use and land cover change (LUCC), and explores the driving forces of LUCC in Maqu County. In the study we used Landsat images obtained in 1989, 2004, 2009, and 2014 to establish databases of land use and land cover. Then we derived LUCC information by overlaying these layers using GIS software. Finally, we analyzed the main forces responsible for LUCC. The results showed that forests, high-coverage grasslands, and marshes experienced the most significant decreases during 1989-2004, by 882.8 ha, 35, 250.4 ha, and 2, 753.4 ha, respectively. However, moderate-and low-coverage grasslands and sand lands showed the opposite trend, increasing by 12, 529.7 ha, 25, 491.0 ha, and 577.5 ha, respectively. LUCC in 2004-2009 showed that ecological degradation slowed compared with 1989-2004. During 2009-2014, high-and moderate-coverage grasslands increased obviously, but low-coverage grasslands, marshes, unused lands, sand lands, and water areas showed the opposite trend. These results suggested that the degradation of the eco-environment was obvious before 2009, showing a decrease in the forests, grasslands, and water areas, and an increase in unused lands. The ecological degradation was reversed after 2009, as was mainly evidenced by increases in high-and moderate-coverage grasslands, and the shrinkage rate of marshes decreased obviously. These results showed that the project of restoring grazing lands to grassland had a positive effect on the LUCC. Other major factors that influence the LUCC include increasing temperature, variation in the seasonal frozen soil environment, seasonal overgrazing, and pest and rodent damage.

The Effects of Urban Sprawl on the Spatial Evolution of Rural Settlements: A Case Study in Changchun, China

Detailed analysis of continuous time-series data from regions undergoing rapid urbanization can accurately reveal spatial variations on short time scales. This study used the city of Changchun in Jilin Province, China, as a case study to analyze total and annual changes in area—especially decreases in rural settlement area—as well as regional differences in these changes and driving forces of rural settlement evolution. Quantitative analytical techniques include a dynamic percentage of rural settlements, the distribution index of rural settlements, the regression correlation analysis, and other spatial analysis methods. Data were derived from a variety of sources, including land-use databases and social and economic statistics. The results show that the area of rural settlements decreased between 2009 and 2014, with the urban construction land expansion and decreases in cultivated lands. Rural settlements also became increasingly fragmented after 2009. Most of the rural settlements were located close to the urban construction land, and changes in rural settlement area were more pronounced with decreasing distance to the closest urban construction land, illustrating the effect of urban sprawl on rural settlement changes. The analysis also shows that the decreasing area of rural settlements between 2009 and 2014 is directly caused by urban sprawl. Regional development strategies and urban planning indirectly contribute to changes in the scale and spatial distribution of rural settlements by guiding urban development. The geographical environment and strict cultivated-land-protection policies also indirectly restrict changes in rural settlements by determining the restrictive area of urban expansion. No significant changes were found in the influence of population change on changing areas of rural settlements. In conclusion, the interaction of strategy for social-economic development, natural geography environments, and human demand jointly caused changes in rural settlements.

Sensitivity Study on High-Resolution Numerical Modeling of Static Topographic Data

Both research-grade and operational numerical weather prediction models perform simulations with horizontal grid spacings as fine as 1 km, and their multi-scale terrain data have become increasingly important for high-resolution model forecasting. This study focused on the influence of multi-scale surface databases of topographical height and land use on the modeling of atmospheric circulation in a megacity. The default data were the global 30S United States Geographic Survey terrain data set and Moderate Resolution Imaging Spectroradiometer land-use data. The capacity for topographical expression under the combined scale effect was evaluated against observational data. The experiments showed that surface input data using finer resolutions for the Weather Research and Forecasting model with 1-km resolution gave better topographical expression and meteorological reproduction in a megacity and agreed with observational data in the fields of temperature and relative humidity, but precipitation values were not sensitive to the surface input data when verified against a suite of observational data including, but not limited to, ground-based instruments. The results indicated that the use of high-resolution databases improved the local atmospheric circulation in a megacity and that a fine-scale model was sensitive to the resolution of the surface input data whereas a coarse-scale model was less sensitive to it.

Evaluation of improved land use and canopy representation in BEIS v3.61 with biogenic VOC measurements in California

Abstract. Biogenic volatile organic compounds (BVOC) participate in reactions that can lead to secondarily formed ozone and particulate matter (PM) impacting air quality and climate. BVOC emissions are important inputs to chemical transport models applied on local to global scales but considerable uncertainty remains in the representation of canopy parameterizations and emission algorithms from different vegetation species. The Biogenic Emission Inventory System (BEIS) has been used to support both scientific and regulatory model assessments for ozone and PM. Here we describe a new version of BEIS which includes updated input vegetation data and canopy model formulation for estimating leaf temperature and vegetation data on estimated BVOC. The Biogenic Emission Landuse Database (BELD) was revised to incorporate land use data from the Moderate Resolution Imaging Spectroradiometer (MODIS) land product and 2006 National Land Cover Database (NLCD) land coverage. Vegetation species data are based on the US Forest Service (USFS) Forest Inventory and Analysis (FIA) version 5.1 for 2002–2013 and US Department of Agriculture (USDA) 2007 census of agriculture data. This update results in generally higher BVOC emissions throughout California compared with the previous version of BEIS. Baseline and updated BVOC emission estimates are used in Community Multiscale Air Quality (CMAQ) Model simulations with 4 km grid resolution and evaluated with measurements of isoprene and monoterpenes taken during multiple field campaigns in northern California. The updated canopy model coupled with improved land use and vegetation representation resulted in better agreement between CMAQ isoprene and monoterpene estimates compared with these observations.

Demand and Supply of Crude Palm Oil for Biodiesel Production towards Food and Energy Security

Energy and food security are two key national policies and agenda of Thailand. The Department of Alternative Energy Development and Efficiency (DEDE), Ministry of Energy has modified a 15-year (2008-2021) Alternative Energy Development Plan (AEDP). According to the modified AEDP, the share of renewable and alternative energy consumption should increase to 25% by 2021. The latest policy aims to promote the biodiesel consumption and production by 5.97 million liters/day within 2021, up from its previous plan of 4.50 million liters/day while nowadays production capacity is 1.62 million liters/day. The current and anticipated production of biodiesel require raw materials, notably crude palm oil (CPO) from oil palm plantations. This paper presents a feasibility study, with respect to the AEDP plan, for biodiesel production by using domestic fresh fruit oil palm as a raw material. Demand and supply are analyzed based on statistical modeling and forecasting techniques, as well as GIS spatial analysis. The results show that the domestic CPO consumption for cooking palm oil will be 1.02 million tons/year, while biodiesel production will be 1.67 million tons/year in 2021. This corresponds to an oil palm plantation area of 11,152 km2 in 2021. Based on GIS spatial analysis along with land-use database and other relevant databases, it was found that the suitable area for palm oil plantation in Thailand is 14,639 km2, which is sufficient for domestic demand and consumption. The suitable area consists of 9,664 km2 for quite suitable and 4,975 km2 for medium suitable. However, the government should provide the guidelines, along with pro-active and supportive policies for worst case scenarios, e.g. drought and flood leading to the insufficient raw material, in order to reduce the negative impacts of biodiesel production for domestic demand and consumption.

Hierarchical Coding Vectors for Scene Level Land-Use Classification

Land-use classification from remote sensing images has become an important but challenging task. This paper proposes Hierarchical Coding Vectors (HCV), a novel representation based on hierarchically coding structures, for scene level land-use classification. We stack multiple Bag of Visual Words (BOVW) coding layers and one Fisher coding layer to develop the hierarchical feature learning structure. In BOVW coding layers, we extract local descriptors from a geographical image with densely sampled interest points, and encode them using soft assignment (SA). The Fisher coding layer encodes those semi-local features with Fisher vectors (FV) and aggregates them to develop a final global representation. The graphical semantic information is refined by feeding the output of one layer into the next computation layer. HCV describes the geographical images through a high-level representation of richer semantic information by using a hierarchical coding structure. The experimental results on the 21-Class Land Use (LU) and RSSCN7 image databases indicate the effectiveness of the proposed HCV. Combined with the standard FV, our method (FV + HCV) achieves superior performance compared to the state-of-the-art methods on the two databases, obtaining the average classification accuracy of 91.5% on the LU database and 86.4% on the RSSCN7 database.

Finding history: the locational geography of Ashokan inscriptions in the Indian subcontinent

Abstract

A Review of the Health Benefits of Greenness.

Researchers are increasingly exploring how neighborhood greenness, or vegetation, may affect health behaviors and outcomes. Greenness may influence health by promoting physical activity and social contact; decreasing stress; and mitigating air pollution, noise, and heat exposure. Greenness is generally measured using satellite-based vegetation indices or land-use databases linked to participants' addresses. In this review, we found fairly strong evidence for a positive association between greenness and physical activity, and a less consistent negative association between greenness and body weight. Research suggests greenness is protective against adverse mental health outcomes, cardiovascular disease, and mortality, though most studies were limited by cross-sectional or ecological design. There is consistent evidence that greenness exposure during pregnancy is positively associated with birth weight, though findings for other birth outcomes are less conclusive. Future research should follow subjects prospectively, differentiate between greenness quantity and quality, and identify mediators and effect modifiers of greenness-health associations.

An assessment of enhanced biogenic emissions influence on ozone formation in central Alberta, Canada

Emissions from biogenic sources are the primary source of isoprene and monoterpene that contribute to the formation of ozone and secondary organic aerosol (SOA). In this study, we used a regional air quality model to examine the sensitivity of simulated ozone to biogenic emission estimates. We created a new regional database of plant functional type (PFT) and land use and land cover (LULC) based on two satellite data products, the Alberta Vegetation Inventory (AVI) and Earth Observation for Sustainable Development of forests (EOSD).The Model for Emissions for Gases and Aerosols from Nature (MEGAN) biogenic emissions model was used with the new PFT data as well as the default Moderate Resolution Imaging Spectroradiometer (MODIS) PFT to generate biogenic emissions in Alberta. The comparison revealed that the new LULC increased isoprene (ISOP) emissions by 30–40 %, decreased monoterpene emissions by 7–8 %, and increased NO emissions by 65–90 % associated with the new regional plant functional type fractional (PFTf) database. This study further investigated the impact of using Alberta-specific biogenic emission on ambient ozone concentration through the Community Multi-scale Air Quality (CMAQ) modeling system against the default MEGAN emissions. In addition, the result from using the new LULC on the predicted fourth highest daily maximum 8-h ozone concentration were examined, which showed a substantial reduction (6 ppb) in ozone in the urbanized capital region area when compared to the default the LULC with less ozone changes in the rural areas. This is likely due to ozone formation in the urban areas being more sensitive to changes in volatile organic compound (VOC) emissions than the rural areas. As CMAQ tended to overestimate the observed ozone peaks in the urban areas, the use of the enhanced LULC resulted in improved model performance compared to the default LULC database. This study results will be primarily used to understand impact of biogenic emission into air on ambient air quality and secondary to provide strategic advice on air quality management plan and initiatives regarding air issues (e.g., ozone) in the region. Results also can be implied to the relationship between climate change-induced increases in biogenic VOC emissions and ozone concentrations.

Influence of land-use misrepresentation on the accuracy of WRF wind estimates: Evaluation of GLCC and CORINE land-use maps in southern Spain

In this work, we evaluate the influence of land-use representation accuracy on the reliability of wind speed and direction estimates derived from the Weather Research and Forecasting (WRF) model. To this end, the 100-m spatial resolution Coordination of Information on the Environment (CORINE) land-use dataset was implemented as static geographic data in WRF. Next, a set of one-year long simulations at 1-km spatial resolution was conducted using both the CORINE and Global Land Cover Characterization (GLCC) land-use datasets, the latter the default in WRF. The simulations were conducted for three locations in southern Spain, and were characterized by variable land-use composition and topography. At these locations, wind speed and direction estimates were compared against observations at different measurement elevations. Results showed that the selection of land-use database has a major influence on wind estimate bias. The effect on the wind direction distribution is also significant, whereas that on the standard deviation is much weaker. CORINE provided a more reliable land-use representation than GLCC. Nevertheless, as a consequence of the interpolation procedure used for land use in the domain setup, this representation did not necessarily translate to a superior roughness length, thereby affecting wind speed and direction estimates. This was particularly so for areas of high spatial variability in land-use categories. In such areas, the misrepresentation of land use may result in large wind speed estimation errors.

Modelling the water budget of Ireland - evapotranspiration and soil moisture

The water budget is one of the most important exchange cycles within the earth-atmosphere system. When applied to a surface it accounts for the partition of precipitation into evapotranspiration. runoff and exchanges in soil-water storage. While there has been work published on some of these components, few have attempted to evaluate these within the context of the water budget. In this paper a simple model is applied to a 5x5km grid superimposed on the land area of Ireland. The model uses measured monthly values of meteorological variables (precipitation, temperature and sunshine hours) and databases of landuse and elevation to calculate each of the water budget terms. The results for the thirty year period 1961 -1 WO are presented and seem to conform to the known climate of the period.

Effects of management and landscape composition on the diversity and structure of tree species assemblages in coffee agroforests

Understanding the processes that influence tree species composition in agricultural landscapes is essential for conservation of tropical biodiversity outside of protected areas. We analyzed the effects of landscape composition (amount of surrounding forest cover) and farm management (conventional vs. organic) on the diversity and structure of woody plant species assemblages in Costa Rican coffee agroforestry systems. We utilized information from a GIS land-use database, surveys of 1-ha plots located in 14 coffee farms and 4 forest fragments, and farmer interviews on management practices. The coffee farms harbored over 100 tree species, including 19% of the native tree species found in the surrounding forests. The majority of tree species on the farms were native (82%) to the study area and originated from natural regeneration (73%). Among the tree species that regenerated naturally, 71% were dispersed by animals. On the other hand more than half of the individuals were non-natives (55%) and originated from planting, which resulted in low species similarity between farms and forests and a low density for most native species on the farms. Forest cover within a 1000m radius around the farms varied between 4 and 38%. Increasing forest cover around the farms had a significant, positive effect on species richness; especially on tree species dispersed by animals, and on species similarity between farms and forests. This suggests that the connection to natural forests increases seed dispersal into adjacent farms. The number of regenerated species was higher on the organic farms, but tree species richness was not affected by management type. Although species assemblages on the coffee farms are strongly determined by natural regeneration, the number of individuals contributed by these processes is low. Tree species conservation in agricultural landscapes would greatly benefit from protecting remnant forests, from facilitating natural regeneration processes and promoting native trees on farms, with particular attention to rare species.

Influence of high-resolution surface databases on the modeling of local atmospheric circulation systems

Abstract. Large-eddy simulations are performed using the Advanced Regional Prediction System (ARPS) code at horizontal grid resolutions as fine as 300 m to assess the influence of detailed and updated surface databases on the modeling of local atmospheric circulation systems of urban areas with complex terrain. Applications to air pollution and wind energy are sought. These databases are comprised of 3 arc-sec topographic data from the Shuttle Radar Topography Mission, 10 arc-sec vegetation-type data from the European Space Agency (ESA) GlobCover project, and 30 arc-sec leaf area index and fraction of absorbed photosynthetically active radiation data from the ESA GlobCarbon project. Simulations are carried out for the metropolitan area of Rio de Janeiro using six one-way nested-grid domains that allow the choice of distinct parametric models and vertical resolutions associated to each grid. ARPS is initialized using the Global Forecasting System with 0.5°-resolution data from the National Center of Environmental Prediction, which is also used every 3 h as lateral boundary condition. Topographic shading is turned on and two soil layers are used to compute the soil temperature and moisture budgets in all runs. Results for two simulated runs covering three periods of time are compared to surface and upper-air observational data to explore the dependence of the simulations on initial and boundary conditions, grid resolution, topographic and land-use databases. Our comparisons show overall good agreement between simulated and observational data, mainly for the potential temperature and the wind speed fields, and clearly indicate that the use of high-resolution databases improves significantly our ability to predict the local atmospheric circulation.

Parcel-Based Change Detection in Land-Use Maps by Adopting the Holistic Feature

Updating the existing land-use maps with remote sensing imagery has become a common method to produce the latest land-use database. An important step is to extract the changed information. In this paper, we propose a novel method to extract the changed parcels in land-use maps by using the holistic feature called “Spatial Envelope,” which encodes each parcel without segmenting it into homogeneous objects or small regions. The holistic feature is based on the energy spectrum of the windowed Fourier transform (WFT) of each land-use parcel, which is ideal for scene categorization. Unlike the pixel-based change detection using the difference image (DI) leading to speckled results or object-based method which requires a complicated process to segment the land-use parcel into homogeneous land-cover objects, our parcel-based change detection treats each land-use parcel as an entirety by calculating the holistic feature for the former and latter parcels. Then, the distance between the corresponding former and latter parcels is compared against a threshold to select the changed parcels. Experiments have demonstrated that our procedure can extract the changed parcels with the overall accuracy of more than 92%. The performance of our procedure is reliable not only on the high-resolution (HR) images of the same sensor, but also on the images acquired by different sensors with the same or approximate spatial resolution. Comparative experiments have also proved that the holistic feature is better than conventional spectral and textural features in parcel-based change detection.

Land-use regionalization based on landscape pattern indices using rough set theory and catastrophe progression method

This study presents the rough set theory and catastrophe progression method to regionalize the land-use change and to analyze the land ecological process. It uses the land-use database of Yixing City of Jiangsu Province, an urbanized and industrialized city in Yangtze River Delta of China, as an exemplification. The study area is divided into six kinds of land-use types according to the national standard of land-use classification. It includes arable land, garden, woodland, urban–rural construction land, water, and unused land. The six kinds of land-use types are formed into their corresponding landscape types in the scale of 1:10,000 by the aid of ArcGIS9.3 software of ESRI. In ArcGIS9.3, the landscape pattern indices are calculated by using Fragstats (raster version 3.3) software. Based on these landscape pattern indices, an integrated indicator system of landscape regionalization of land use in Yixing was established, and land-use regionalization models are set up using the catastrophe theory. Rough set theory is introduced to avoid the subjectivity in the indicator’s importance in catastrophe models. The hidden rule among the raw data is acquired by knowledge reduction of the data mining in the rough set theory. In the process, indicators needed to be arranged according to the computed importance of an attribute without considering the determination of weight function. This greatly avoids the subjectivity in the process of weight factor determination. The zoning of land use based on landscape indices finally is made by the multi-indicator integrated catastrophe progression method. According to these indices, Yixing is divided into four grading land-use zones when the rough set and catastrophe progression methods are combined. The zones include high-, medium-, low-, and weak-intensity zones, indicating that land use primarily varies the landscape pattern. With the increase of water and forest area proportion, the human disturbance to land system wanes; patch fragmentation reduces; patch shape complexity enhances; and landscape diversity decreases. Moreover, it can mostly avoid the subjective evaluation in artificially determining factor weights by using rough set theory. It makes the zoning results more objective and exact.