Land-cover changes in an urban lake watershed in a mega-city, Central China

Urbanization can exert a profound influence on land covers and landscape characteristics. In this study, we characterize the impact of urbanization on land cover and lacustrine landscape and their consequences in a large urban lake watershed, Donghu Lake watershed (the largest urban lake in China), Central China, by using Landsat TM satellite images of three periods of 1987, 1993 and 1999 and ground-based information. We grouped the land covers into six categories: water body, vegetable land, forested land, shrub-grass land, open area and urban land, and calculated patch-related landscape indices to analyze the effects of urbanization on landscape features. We overlaid the land cover maps of the three periods to track the land cover change processes. The results indicated that urban land continuously expanded from 9.1% of the total watershed area in 1987, to 19.4% in 1993, and to 29.6% in 1999. The vegetable land increased from 7.0% in 1987, 11.9% in 1993, to 13.9% in 1999 to sustain the demands of vegetable for increased urban population. Concurrently, continuous reduction of other land cover types occurred between 1987 and 1999: water body decreased from 30.4% to 23.8%, and forested land from 33.6% to 24.3%. We found that the expansion of urban land has at least in part caused a decrease in relatively wild habitats, such as urban forest and lake water area. These alterations had resulted in significant negative environmental consequences, including decline of lakes, deterioration of water and air quality, and loss of biodiversity.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

Land-cover classification of China: integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.

The characteristics of land cover and macroscopical ecology changes in the source region of three rivers in Qinghai-Tibet plateau during last 30 years

We obtained four phases of land cover spatial data sets by interpreting MSS images of middle and late 1970s and three phases of TM images of late 1980s, 2004 and 2008 based on field investigation in Three Rivers' Source Region. We analyzed the temporal and spatial characteristics of land cover and macro ecological changes in Three Rivers' Source Region in Qinghai-Tibet plateau since middle and late 1970s. Indicated by land cover condition index change rate and land cover change index, land cover and macroscopical ecological condition degenerated (7090 period Zc -0.63, LCCI -0.58)-obviously degenerated (9004 period, Zc -0.94, LCCI -1.76)-slightly meliorated (0408 period, Zc 0.06, LCCI 0.33). This course was jointly driven by climate change, grassland stocking pressure and implement of ecological construction project.

Palynological perspectives on vegetation survey: a critical step for model-based reconstruction of Quaternary land cover

1. Quantitative reconstruction of past vegetation distribution and abundance from sedimentary pollen records provides an important baseline for understanding long term ecosystem dynamics and for the calibration of earth system process models such as regional-scale climate models, widely used to predict future environmental change. Most current approaches assume that the amount of pollen produced by each vegetation type, usually expressed as a relative pollen productivity term, is constant in space and time.
2. Estimates of relative pollen productivity can be extracted from extended R-value analysis (Parsons and Prentice, 1981) using comparisons between pollen assemblages deposited into sedimentary contexts, such as moss polsters, and measurements of the present day vegetation cover around the sampled location. Vegetation survey method has been shown to have a profound effect on estimates of model parameters (Bunting and Hjelle, 2010), therefore a standard method is an essential pre-requisite for testing some of the key assumptions of pollen-based reconstruction of past vegetation; such as the assumption that relative pollen productivity is effectively constant in space and time within a region or biome.
3. This paper systematically reviews the assumptions and methodology underlying current models of pollen dispersal and deposition, and thereby identifies the key characteristics of an effective vegetation survey method for estimating relative pollen productivity in a range of landscape contexts.
4. It then presents the methodology used in a current research project, developed during a practitioner workshop. The method selected is pragmatic, designed to be replicable by different research groups, usable in a wide range of habitats, and requiring minimum effort to collect adequate data for model calibration rather than representing some ideal or required approach. Using this common methodology will allow project members to collect multiple measurements of relative pollen productivity for major plant taxa from several northern European locations in order to test the assumption of uniformity of these values within the climatic range of the main taxa recorded in pollen records from the region.

Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

Larval habitat associations with human land uses, roads, rivers, and land cover for Anopheles albimanus, A. pseudopunctipennis, and A. punctimacula (Diptera: Culicidae) in coastal and highland Ecuador

Larval habitat for three highland Anopheles species: Anopheles albimanus Wiedemann, Anopheles pseudopunctipennis Theobald, and Anopheles punctimacula Dyar and Knab was related to human land uses, rivers, roads, and remotely sensed land cover classifications in the western Ecuadorian Andes. Of the five commonly observed human land uses, cattle pasture (n = 30) provided potentially suitable habitat for A. punctimacula and A. albimanus in less than 14% of sites, and was related in a principal components analysis (PCA) to the presence of macrophyte vegetation, greater surface area, clarity, and algae cover. Empty lots (n = 30) were related in the PCA to incident sunlight and provided potential habitat for A. pseudopunctipennis and A. albimanus in less than 14% of sites. The other land uses surveyed (banana, sugarcane, and mixed tree plantations; n = 28, 21, 25, respectively) provided very little standing water that could potentially be used for larval habitat. River edges and eddies (n = 41) were associated with greater clarity, depth, temperature, and algae cover, which provide potentially suitable habitat for A. albimanus in 58% of sites and A. pseudopunctipennis in 29% of sites. Road-associated water bodies (n = 38) provided potential habitat for A. punctimacula in 44% of sites and A. albimanus in 26% of sites surveyed. Species collection localities were compared to land cover classifications using Geographic Information Systems software. All three mosquito species were associated more often with the category “closed/open broadleaved evergreen and/or semi-deciduous forests” than expected (P ≤ 0.01 in all cases), given such a habitat’s abundance. This study provides evidence that specific human land uses create habitat for potential malaria vectors in highland regions of the Andes.

Stochastic simulation to improve land-cover estimates derived from coarse spatial resolution satellite imagery = La simulation stochastique pour améliorer les estimations de la couverture des sols à partir d'images satellitales à résolution spatiale grossière

Thèse diffusée initialement dans le cadre d'un projet pilote des Presses de l'Université de Montréal/Centre d'édition numérique UdeM (1997-2008) avec l'autorisation de l'auteur.