Meng-Lung Lin

Application of fuzzy models for the monitoring of ecologically sensitive ecosystems in a dynamic semi‐arid landscape from satellite imagery

Purpose – The purpose of this paper is to better understand landscape dynamics in arid and semi‐arid environments. Land degradation has recently become an important issue for land management in western China. The oasis ecosystem is especially sensitive to environmental disturbances, such as abnormal/extreme precipitation events, variations in the water supply from the upper watersheds, fluctuations in temperature, etc. Satellite remote sensing of terrestrial ecosystems can provide us with the temporal dynamics and spatial distributions of green cover over large areas of landscape. Seasonal green cover data are especially important in assessing landscape health (e.g. desertification, rate of urban sprawl, natural disturbances) in arid and semi‐arid regions. In this study, green cover data are derived from vegetation indices retrieved from moderate resolution imaging spectroradiometer (MODIS) sensors onboard the satellite Terra.Design/methodology/approach – Satellite images recorded during the period from A...

Fuzzy model‐based assessment and monitoring of desertification using MODIS satellite imagery

Purpose – The growing rate of desertification in Northwestern China and Mongolia that is occurring as a result of the conflict between economic development and natural conservation has been demonstrated in many studies. There have, for example, been some large studies using variations in bi‐weekly normalized difference vegetation index (NDVI) satellite images as a parameter for evaluating the vegetation dynamics in these areas. The purpose of this paper is to identify multi‐temporal variation in vegetated and non‐vegetated areas in remotely sensed satellite images to assess the status of desertification in East Asia.Design/methodology/approach – Spatial data derived from these satellite images are applied to evaluate vegetation dynamics on a regional level, to identify the areas most vulnerable to desertification.Findings – Analytical results indicate that the desert areas in East Asia are primarily distributed over Southern Mongolia, Central and Western Inner Mongolia, and Western China (the Taklimakan D...

Mapping paddy rice agriculture in a highly fragmented area using a geographic information system object-based post classification process

Most paddy rice fields in Asia are comprised of small parcels of land, and the weather conditions during the growing season are usually cloudy. This study develops a geographic information system (GIS) object-based post classification (GOBPC) that combines low-cost remotely sensed and GIS data to precisely map paddy rice fields in the intensively cultivated but fragmented growing areas which are characteristic of Asia. FORMOSAT-2 multispectral images have an 8-meter resolution and a one-day recurrence, making them ideal for mapping such areas. Multitemporal images are examined to distinguish the different growth characteristics between paddy rice and other types of ground cover. The pixel-based hybrid classification technique is used with both the unsupervised and supervised approach to distinguish the paddy rice fields from their surroundings. In addition to the pixel-based approach, we also use GOBPC to deal with over-fragmented parcels of land and to reduce the incidence of misclassification caused by speckle or mixed pixels (mixels) in the images. A comparison is made with the pixel-based technique. The Kappa index of agreement obtained with the GOBPC reaches 0.095 to 0.291, and there is a statistically significant improvement in the user and producer accuracy for all the classes ( z > 1.96 ) with McNemar’s test in the four study areas. The proposed GOBPC approach is shown to be useful in highly fragmented rice growing areas and may have the potential for other agricultural applications.

Assessment and monitoring of desertification using satellite imagery of MODIS in East Asia

The desertification in Northwestern China and Mongolia shows the result of conflicts between economic development and natural conservation. Many researches have proven the desert areas are growing in these regions. The variations of bi-weekly NDVI satellite images are used as one of the parameters to evaluate the vegetation dynamics over large scale studies. In this study, remotely sensed satellite images are conducted to provide multi-temporal vegetated and non-vegetated areas in order to assess the status of desertification in East Asia. Spatial data derived from these satellite images are applied to evaluate vegetation dynamics at regional scale to find out the hot spot areas vulnerable to desertification. The results show that the desert areas are mainly distributed over southern Mongolia, central and western Inner-Mongolia, western China (the Taklimakan desert). The desert areas were expanded from 2000 to 2002, were shrunk in 2003, and were expanded from 2003 to 2005 again. The hot spot areas of desertification are mainly distributed over southeastern Mongolia and eastern Inner-Mongolia. The results will help administrators to refine the planning processes in defining the boundaries of protected areas and will facilitate to take decision of the priority areas for conservation of desertification.

Using MODIS-based vegetation and moisture indices for oasis landscape monitoring in an arid environment

Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) were compared for an oasis ecosystem in an arid environment. Both indices were computed from 500m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data (8-day composite from 193 to 200) for the period from 2001 to 2008. NDMI were positively correlated with NDVI. R2 values ranging between 0.80 to 0.92. The results indicated that the vegetation dynamics have highly correlated with land surface moisture in an arid environment.

Changing landscapes: monitoring ecologically sensitive ecosystems in a dynamic semi-arid landscape using satellite imagery: a case study in Ejin Oasis, western China

Land degradation has become an important issue in western China recently. Oasis ecosystem is sensitive to environmental disturbances, such as abnormal /extreme events of precipitations, water supply from upper watersheds, fluctuations of temperatures, etc. Satellite remote sensing of terrestrial ecosystems provides temporal dynamics and spatial distributions of landscape green covers over large areas. Seasonal green cover data are normally important in assessing landscape health (ex. desertification, rate of urban sprawl, natural disturbances) in arid and semi-arid regions. In this study, green cover data is derived from vegetation indices retrieved from MODIS sensors onboard Terra. The satellite images during the period April 2000 to December 2005 are analyzed to quantify the spatial distribution and temporal changes of Ejin Oasis. The results will help improving monitoring techniques to evaluate land degradation and to estimate the newest tendency of landscape green cover dynamics in the Ejin Oasis.

Monitoring Drought Dynamics in the Ejin Oasis using Drought Indices from MODIS Data

The Ejin Oasis in arid environments suffers with frequent drought due to insufficient water resources and abnormally high summer-temperature. Detailed analysis of remote sensing data has been carried out for the years 2001-2006. Vegetative drought indices like Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) and Vegetation Health Index (VHI) have been derived using Normalized Difference Vegetation Index (NDVI) values obtained from TERRA/Moderate Resolution Imaging Spectoradiometer (MODIS). Moisture Condition Index (MCI) has been developed to assess hydrological droughts using Normalized Difference Moisture Index (NDMI) values obtained from MODIS/TERRA. Detailed analyses of spatial and temporal drought dynamics for seasons have been compared through drought index maps generated in geographical information systems environments during the 2001 to 2006 period. Analysis and interpretation of these maps will show the spatial patterns of drought with vegetative drought indices and hydrological drought indices. Furthermore, results will help us to identify the high drought risk areas and the short term trend of drought in the Ejin Oasis.

Mapping and recovering cloud-contaminated area in multispectral satellite imagery with visible and near-infrared bands

Cloud cover severely influences the accuracy of land use/cover mapping and biomass estimation with optical satellite imagery. This study integrated automated threshold selection algorithm (ATSA) and region growing to delineate unrecoverable thick cloud. Concerning hazy areas, Fourier analysis was used to generate haze filter to reduce haze interference and recover ground information. The result of thick cloud delineation shows the overall accuracy and kappa statistics are 94.75% and 0.883 separately. For the haze-off result, haze filter improves land cover classification and increases the overall accuracy and kappa statistics by about 4%. With NDVI results, the root-mean-square (RMS) between hazy and clear image is 0.21 while RMS between haze-off and clear image is 0.15. This study demonstrated that cloud processing only using Green, Red, NIR bands without cloud-free reference areas or imagery is sufficient for thick cloud delineation and can achieve some improvement in haze removal.

Integrating remote sensing, spatial analysis and certainty factor model for waste dumping risk assessment

Waste dumping is one of the main pollution causing land deterioration and resource depletion. To help manage the environmental protection resource, this study proposed the risk assessment integrating remote sensing and geographic information system (GIS) to predict and map potential waste dumping area. Factors relative to waste dumping were generated with spatial analysis, which quantifies the spatial correlation between waste dumping areas and other land use. The suspected waste dumping mapping with FORMOSAT-2 imagery and hybrid classification was used as the other factor. All factors were then combined with certainty factor (CF) model to generate waste dumping risk map. Validating the result with the 45 waste dumping cases, CF model predicts 75.56% of the waste dumping cases in the very high potential area. The result demonstrated that integrating remote sensing, spatial analysis and CF model for waste dumping risk assessment could be reliable. Future applications include helping inspectors to concentrate the patrol areas, which could save manpower significantly.

Spatial filtering analysis for quick drought assessment using MODIS images to detect drought affected areas

This paper proposed to apply spatial computing methods to identify drought affected areas using satellite images. Satellite derived indices, such as Normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), standardized vegetation index (SVI) and standardized moisture index (SMI), were used to evaluate drought condition. The maps of SVI and SMI showed the areas affected by the drought. The results demonstrated the proposed method is effective and provide useful spatial information.