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1.
Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m?2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.  相似文献   

2.
ABSTRACT

Monitoring of destructive invasive weeds such as those from the genus Striga requires accurate, near real-time predictions and integrated assessment techniques to enable better surveillance and consistent assessment initiatives. Thus, in this study, we predicted the potential ecological niche of Striga (Striga asiatica) weed in Zimbabwe, to identify and understand its propagation and map potentially vulnerable cropping areas. Vegetation phenology from remote sensing, bioclimatic and other environmental variables (i.e. cropping system, edaphic, land surface temperature, and terrain) were used as predictors. Six machine learning modeling techniques and the ensemble model were evaluated on their suitability to predict current and future Striga weed distributional patterns. The mentioned predictors (n = 40) were integrated into six models with “presence-only” training and evaluation data, collected in Zimbabwe over the period between the 12th and 28th of March 2018. The area under the curve (AUC) and true skill statistic (TSS) were used to measure the performance of the Striga modeling framework. The results showed that the ensemble model had the strongest Striga occurrence predictive power (AUC = 0.98; TSS = 0.93) when compared to the other modeling algorithms. Temperature seasonality (Bio4), the maximum temperature of the warmest month (Bio5) and precipitation seasonality (Bio15) were determined to be the most dominant bioclimatic variables influencing Striga occurrence. “Start of the season” and “season minimum value” of the “Enhanced Vegetation Index base value” were the most relevant remote sensing-based variables. Based on projected climate change scenarios, the study showed that up to 2050, the suitable area for Striga propagation will increase by ~ 0.73% in Zimbabwe. The present work demonstrated the importance of integrating multi-source data in predicting possible crop production restraints due to weed propagation. The results can enhance national preparedness and management strategies, specifically, if the current and future risk areas can be identified for early intervention and containment  相似文献   

3.
Abstract

An atlas of Zimbabwe and the Southern African Development Community was designed and produced for use by American diplomats in Zimbabwe. Two copies of the bound atlas are used by the Embassy of the United States of America (U.S. Embassy) and the Mission of the U.S. Agency for International Development (USAID) in Harare, Zimbabwe, to orient visitors and discuss matters of diplomacy and development in Zimbabwe and the Southern African Development Community. The atlas contains maps derived from satellite images showing features of the physical geography of Southern Africa and Zimbabwe and plastic overlays showing rivers and lakes and manmade features, such as major roads, railroads, and cities. The atlas is an important tool that American diplomats can use to orient participants in discussions of the environment and to develop agreements for management of the environment in Zimbabwe and Southern Africa.  相似文献   

4.
Geographically weighted regression (GWR) is an important local method to explore spatial non‐stationarity in data relationships. It has been repeatedly used to examine spatially varying relationships between epidemic diseases and predictors. Malaria, a serious parasitic disease around the world, shows spatial clustering in areas at risk. In this article, we used GWR to explore the local determinants of malaria incidences over a 7‐year period in northern China, a typical mid‐latitude, high‐risk malaria area. Normalized difference vegetation index (NDVI), land surface temperature (LST), temperature difference, elevation, water density index (WDI) and gross domestic product (GDP) were selected as predictors. Results showed that both positively and negatively local effects on malaria incidences appeared for all predictors except for WDI and GDP. The GWR model calibrations successfully depicted spatial variations in the effect sizes and levels of parameters, and also showed substantially improvements in terms of goodness of fits in contrast to the corresponding non‐spatial ordinary least squares (OLS) model fits. For example, the diagnostic information of the OLS fit for the 7‐year average case is R2 = 0.243 and AICc = 837.99, while significant improvement has been made by the GWR calibration with R2 = 0.800 and AICc = 618.54.  相似文献   

5.
Abstract

This study examines the potentials of remotely sensed data, GIS and some machine learning classifiers and ensemble techniques in the investigation of the non-linear relationship between malaria occurrences and socio-physical conditions in the Dak Nong province of Viet Nam. Accuracy assessment was determined with Receiver Operating Characteristic (ROC) curve and pair t-test. The results showed that the area under ROC of Random Subspace ensemble model performed better than the other models based on statistical indicators. Comparing pair t-test with Area Under Curve values showed a slight difference of about 1%. Therefore ensemble techniques had significantly improved the performance of the base classifier. However, the performances might vary according to geographic locations. It is concluded that the machine learning classifiers combined with remotely sensed data and GIS is promising for malaria vulnerability mapping, and the derived maps can be used as a fundamental basis for programmes on spatial disease control.  相似文献   

6.
Abstract

Three spatial resolutions of airborne remote sensing imagery (60 cm, 1 m, and 2 m) collected over multi‐layer aspen, pine, spruce, and mixedwood forest stands in Alberta on July 18th, 1998 were tested for their ability to provide a statistical stand discrimination based on spatial co‐occurrence texture analysis. As spatial resolution increased, classification accuracies increased. The highest classification accuracy of 86.7% was obtained using the highest image spatial resolution data (60 cm), with spatial co‐occurrence texture and spectral signatures combined, and a thirteen‐class multi‐layer stand stratification. The texture of the highest spatial resolution imagery (60 cm pixel resolution) was interpreted to contain information on the crown architecture of individual trees. In larger windows, the texture was interpreted to contain information on stand structure. Texture of lower spatial resolution imagery (1 m and 2 m pixel resolution) could not detect individual tree crown architecture and was determined to be related primarily to stand structure characteristics. The use of texture channels improved the per‐plot classification accuracies by 15.7%, compared to the use of the spectral data alone.  相似文献   

7.
Abstract

The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances.  相似文献   

8.
ABSTRACT

Recently the cultivation of opium poppy in Afghanistan reached unprecedented levels. It is agreed that the complex and coupled interactions of social, economic and environmental drivers are crucial for understanding the spatial and temporal dynamics of opium poppy cultivation in Afghanistan. In this context, we present an integrated risk concept, which considers environmental and socio-economic drivers of opium poppy cultivation. A set of spatially explicit indicators for the environmental suitability and socio-economic vulnerability was established and populated from a variety of databases. Subsequently, novel methods of modelling homogeneous and spatially explicit regions of opium poppy cultivation suitability, socio-economic vulnerability and risk are developed and applied. The risk assessment results demonstrate the complex nature of the illicit crops production in Afghanistan and prompt a more profound examination of the drivers of opium poppy cultivation in a spatial context. The study also confirms what has already been widely discussed in literature: that reasons for cultivation are spatially diverse and often distinct, meaning that any formulation of generalized explanations cannot be drawn without ignoring a more complex reality. Thus, an integrative spatial view of risk, which integrates the social dimension as well as environmental parameters, is required to better identify context-specific intervention measures.  相似文献   

9.
This paper generates an extrapolation suitability index (ESI) to guide scaling-out of improved maize varieties and inorganic fertilizers. The best-bet technology packages were selected based on yield gap data from trial sites in Tanzania. A modified extrapolation detection algorithm was used to generate maps on two types of dissimilarities between environmental conditions at the reference sites and the outlying projection domain. The two dissimilarity maps were intersected to generate ESI. Accounting for correlation structure among covariates improved estimate of risk of extrapolating technologies. The covariate that highly limited the suitability of specific technology package in each pixel was identified. The impact based spatial targeting index (IBSTI) identified zones that should be prioritized to maximize the potential impacts of scaling-out technology packages. The proposed indices will guide extension agencies in targeting technology packages to suitable environments with high potential impact to increase probability of adoption and reduce risk of failure.  相似文献   

10.
Abstract

The southern part of the Caspian Sea shoreline in Iran with a length of 813 km has different topographic conditions. Owing to sea fluctuation, these zones have various dimensions in different times. During the last years, the Caspian Sea experienced enormous destructive rises. The historical information and tidal gauge measurements showed different ranges of sea rise from ?30 m to ?22 m from the mean sea level. On the other hand, the probable flooding zone is related to slope gradient of coasts. To help the determination of the probable flooding area owing to sea level rises, the coastal zones can be modelled using geographic information system (GIS) environment as vulnerability risk rates. These rates would be useful for making decisions in coastal management programs. This study examined different scenarios of sea rise to determine hazard-flooding rates in the coastal cities of the Mazandaran province and classified them based on vulnerability risk rates. The 1:2000 scale topographic maps of the coastal zones were prepared to extract topographic information and construct the coastal digital elevation model. With the presumption of half-metre sea rise scenarios, the digital elevation models classified eight scenarios from ?26 to ?22 m. The flooding areas in each scenario computed for 11 cities respectively. The vulnerability risk rate in each rise scenario was computed by dividing the flooded area of each scenario to city area. The results showed that in the first four scenarios, from ?26 to ?24 m, the Behshahr, Joibar, Neka and Babolsar cites would be more vulnerable than other cites. Moreover, for the second four scenarios from ?24 to ?22 m sea level rise scenario, only the coastal area of Chalous city would be vulnerable. It was also observed that the coastal region of Behshahr would be critical in total scenarios. Further studies would be necessary to complete this assessment by considering social-economic and land use information to estimate the exact hazardous and vulnerable zones.  相似文献   

11.
Abstract

The analysis of remote sensing (RS) images, which is often accomplished using unsupervised image classification techniques, requires an effective method to determine an appropriate number of classification clusters. This paper proposes a preliminary analytical method to evaluate the input parameters for unsupervised RS image classification. Our approach involves first analysing the colour spaces of RS images based on the human visual perception theory. This enables the initial number of clusters and their corresponding centres to be automatically established based on the interaction of different forces in our supposed force field. The proposed approach can automatically determine the appropriate initial number of clusters and their corresponding centres for unsupervised image classification. A comparison of the experimental results with those of existing methods showed that the proposed method can considerably facilitate unsupervised image classification for acquiring accurate results efficiently and effectively without any prior knowledge.  相似文献   

12.
The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various time-based groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R2  = 0.881, p < 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population.  相似文献   

13.
Abstract

Riparian vegetation has a fundamental influence on the biological, chemical and physical nature of rivers. The quantification of riparian landcover is now recognised as being essential to the holistic study of the ecosystem characteristics of rivers. Medium resolution satellite imagery is now commonly used as an efficient and cost effective method for mapping vegetation cover; however such data often lack the resolution to provide accurate information about vegetation cover within riparian corridors. To assess this, we measure the accuracy of SPOT multispectral satellite imagery for classification of riparian vegetation along the Taieri River in New Zealand. In this paper, we discuss different sampling strategies for the classification of riparian zones. We conclude that SPOT multispectral imagery requires considerable interpretative analysis before being adequate to produce sufficiently detailed maps of riparian vegetation required for use in stream ecological research.  相似文献   

14.
疟疾是一种具有复杂地理空间分布特征的地方性疾病,研究疟疾的空间格局,掌握疟疾发病的时空演化规律,为疟疾的防治提供科学决策具有十分重要的科学意义。本文以湖南省105个市、县级行政区1983～1992年疟疾发病的历史资料为例,运用空间自相关分析方法对湖南省疟疾发病的空间格局和时空演化规律进行分析、探测和识别。研究结果表明：湖南省各市县疟疾发病地域差异明显,整体呈现＂南高北低＂的空间格局,且主要集中在湘南和湘中经济相对落后的地区。在这期间,湖南省的疟疾发病经历了一个从＂南低北高＂到＂南高北低＂的时空演化过程。  相似文献   

15.
Abstract

Upper Lake is the lifeline of Bhopal City, India for drinking and other water needs. In recent years, environmentalists have expressed their serious concern on deteriorating water quality of this lake. Conventional field sampling methods for monitoring lake water quality lack spatial information about the pollution in the lake. It is desirable to have spatial information about the lake for better management and control. In the present paper the remote sensing data from IRS-1C LISS III have been integrated into a GIS environment to analyse and create a pollution zone map of the Upper Lake.

Spectral reflectance analysis was carried out to find the suitability of wavelengths for determining chlorophyll‐a concentration (chl‐a), suspended solid concentration (SSC) and secchi depth (SD). Empirical models relating spectral reflectance and chl‐a, SSC and SD were developed using least square regression analysis. These models were found valid on unused samples. Chl‐a, SSC and SD distribution maps were generated using proposed models and were incorporated as datalayers in the GIS for further analysis of pollution zones. The spatial information of pollution offered by the pollution zone map could delineate regions of lake having high pollution load. The methodology employed in this work can be used for regular monitoring of the pollution in surface water bodies and serve the data needs for better management of the water quality.  相似文献   

16.
Abstract

This study presents two approaches of textural classification on a synthetic aperture radar (SAR) ERS‐2 image, with the aim of the location of the flows of lava and of the serviced zones around the volcanic site of the Mountain Cameroon. The first approach is a method of progressive supervised classification, where a single class is extracted at the same moment, by using 7 parameters of texture stemming from the run length method. Three classes of texture were extracted by this method and the images from the three classes were merged by the red green blue coloured composition to produce the final map. For purposes of comparison, a not supervised classification was applied to the same image. The applied not supervised classification uses parameters of texture stemming from the co‐occurrence matrix method, and it lays on the detection of the peaks of a histogram. The results obtained by the two methods are coherent, and the validation of the results was made by observations during a recent mission on the site of study.  相似文献   

17.
Abstract

Most projects which involve planning of rural areas and towns in developing countries require maps, but many of these countries do not have sufficient large-scale mapping suitable for this purpose, or maps which are up-to-date. With environmental-friendly projects becoming highly desirable, up-to-date and accurate maps are vital. As most large scale mapping projects require technical assistance, this paper discusses aid agreements with donor countries. These opportunities could be of benefit to private entrepreneurs. This paper also examines the project management approach for introducing mapping projects to developing countries. It makes special reference to a large mapping project in Zimbabwe in which the author was personally involved.  相似文献   

18.
Abstract

Statistical tools were used to evaluate the relationships between observed fire effects and characteristics identifiable in pre‐fire multispectral and terrain data. Random points were placed within field delimited polygons representing areas of high and low canopy mortality. Each point was then used to extract Landsat TM based pre‐fire spectral characteristics and DEM derived terrain characteristics. The values for these random points were subjected to a multivariate discriminant analysis to ascertain whether specific spectral bands, indices, terrain characteristics, or specific combinations of these, could be effectively associated with the observed fire effects. Data values for high and low mortality points were found to be significantly different for all the pre‐fire data sets. The normalized difference vegetation index (NDVI) and tasseled cap greenness values provided the highest magnitude of direct differentiation between high and low mortality points. Discriminant analysis revealed that NDVI had the highest correspondence to degree of future canopy mortality, while the combined effect of the pre‐fire spectral response provided a prediction of observed fire effects with 87% accuracy, and the addition of terrain data improved accuracy to 90%.  相似文献   

19.
Abstract

In this research, multicriteria decision analysis with pairwise comparison weighting method was utilized to determine the suitable locations for vineyard plantation in Sarkoy region of Turkey. Soil maps, meteorological measurements, slope, aspect and elevation maps were used as input to conduct spatial analysis. Different methods were compared and pairwise comparison method was identified as the most appropriate method of weighting for this spatial analysis. Current vineyard areas were determined using Worldview-2 imagery and their spatial distribution compared with the resulting suitability map to determine the current suitability. Comparisons showed current vineyards were mostly established in locations where suitability map expresses low capability. Further inspection unveiled that, these low capability lands are closer to the transportation networks and city/county centres that tend to be in sea level elevations as opposed to vine grapes thriving in higher altitudes. Results also enabled providing suggestions on alternative sites for new vineyard plantation.  相似文献   

20.
High spatial resolution satellite data contribute to improving land cover/land use (LCLU) classification in agriculture. A classification procedure based on Quickbird satellite image data was developed to map LCLU of diversified agriculture at sub-communal and communal level (7 km2). Segmentation performance of the panchromatic band in combination with high pass filters (HPF) was tested first. Accuracy of field boundary delineation was evaluated by an object-based segmentation, a per-field and a manual classification, along with a quantitative accuracy assessment. Sub-communal classification revealed an overall accuracy of 84% with a κ coefficient of 0.77 for the per-field vector segmentation compared to an overall accuracy of 56–60% and a κ coefficient of 0.37–0.42 for object-based approaches. Per-field vector segmentation was thus superior and used for LCLU classification at communal level. Overall accuracy scored 83% and the κ coefficient 0.7. In diversified agriculture, per-field vector segmentation and classification achieved higher classification results.  相似文献   

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