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1.
ABSTRACT White mold of soybeans is one of the most important fungal diseases that affect soybean production in South Dakota. However, there is a lack of information on the spatial characteristics of the disease and relationship with soybean yield. This relationship can be explored with the Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 and a fusion of Landsat 8 and the Moderate Resolution Imaging Spectroradiometer (MODIS) images. This study investigated the patterns of yield in two soybean fields infected with white mold between 2016 and 2017, and estimated yield loss caused by white mold. Results show evidence of clustering in the spatial distribution of yield (Moran’s I = 0.38; p < 0.05 in 2016 and Moran’s I = 0.45; p < 0.05 in 2017) that can be explained by the spatial distribution of white mold in the observed fields. Yield loss caused by white mold was estimated at 36% in 2016 and 56% in 2017 for the worse disease pixels, with the most accurate period for estimating this loss on 21 August and 8 September for 2016 field and 2017 field, respectively. This study shows the potential of free remotely sensed satellite data in estimating yield loss caused by white mold. 相似文献
2.
Zhiqiang Gao Wei Gao Ni-Bin Chang 《International Journal of Applied Earth Observation and Geoinformation》2011
This paper presents a new drought assessment method by spatially and temporally integrating temperature vegetation dryness index (TVDI) with regional water stress index (RWSI) based on a synergistic approach. With the aid of LANDSAT TM/ETM data, we were able to retrieve the land-use and land-cover (LULC), vegetation indices (VIs), and land surface temperature (LST), leading to the derivation of three types of modified TVDI, including TVDI_SAVI, TVDI_ANDVI and TVDI_MSAVI, for drought assessment in a fast growing coastal area, Northern China. The categorical classification of four drought impact levels associated with the RWSI values enables us to refine the spatiotemporal relationship between the LST and the VIs. Holistic drought impact assessment between 1987 and 2000 was carried out by linking RWSI with TVDIs group wise. Research findings indicate that: (1) LST and VIs were negatively correlated in most cases of low, medium, and high vegetation cover except the case of high density vegetation cover in 2000 due to the effect of urban heat island (UHI) effect; (2) the shortage of water in 1987 was more salient than that that in 2000 based on all indices of TVDI and RWSI; and (3) TVDIs are more suitable for monitoring mild drought, normal and wet conditions when RWSI is smaller than 0.752; but they are not suitable for monitoring moderate and severe drought conditions. 相似文献
3.
Gohar Ghazaryan Olena Dubovyk Valerie Graw Nataliia Kussul Jürgen Schellberg 《地理信息系统科学与遥感》2020,57(5):704-718
ABSTRACT Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought. 相似文献
4.
Spectral analysis technique has been utilized to identify the Bauxite mineral occurrences in Panchpatmali, Orissa, India. Spectral processing of Landsat ETM+ data has been carried out by converting the digital data from quantized and calibrated values to reflectance values. Minimum noise fraction transformation is used to determine the inherent dimensionality of reflected Landsat ETM+ data, to segregate noise in the data, and to reduce the computational requirements for subsequent processing and interactively to locate pure pixels within the data-set, projecting n-dimensional scatterplots. Spectral processing results are displayed in the form of images corresponding to each group of pixels (endmembers). Mixed tune matched filtering method has been applied on Landsat ETM+ images which gave three score (abundance) images for three different classes (endmembers) such as Bauxite, vegetation and soil. Further, mineralized zones are identified using image fusion of ERS-2 SAR and Landsat ETM+ data using intensity-hue-saturation technique. 相似文献
5.
为提高农业干旱监测效果和精度,在对传统干旱监测模型对比分析基础上,本文提出将温度植被干旱指数(TVDI)和植被供水指数(VSWI)加权联合构建温度供水干旱指数(TSWDI)的研究思路。以京津冀2006—2012年5月份数据作为实验统计数据,以京津冀2006—2016年3—5月份春旱监测为例进行了模型实验。实验结果证实,TSWDI指数相对其他两个指数与10、20和50 cm深处的土壤水分相关性更高,能够更精准地反映农业干旱状况。TSWDI计算结果显示,京津冀干旱分布具有如下特征:从时间角度看,2006—2016年整体干旱状况逐渐缓解,特别是自2010年至今,研究区域干旱程度逐步减轻;从空间角度看,京津冀区域整体干旱面积逐步减少。 相似文献
6.
Monitoring agricultural drought effectively and timely is important to support drought management and food security. Effective drought monitoring requires a suite of drought indices to capture the evolution process of drought. Thermal infrared signals respond rapidly to vegetation water stress, thus being regarded useful for drought monitoring at the early stage. Several temperature-based drought indices have been developed considering the role of land surface temperature (LST) in surface energy and water balance. Here, we compared the recently proposed Temperature Rise Index (TRI) with several agricultural drought indices that also use thermal infrared observations, including Temperature Condition Index (TCI), Vegetation Health Index (VHI) and satellite-derived evapotranspiration ratio anomaly (ΔfRET) for a better understanding of these thermal infrared drought indices. To do so, we developed a new method for calculating TRI directly from the top-of-atmosphere brightness temperatures in the two split-window channels (centered around ∼11 and 12 μm) rather than from LST. TRI calculated using the Himawari-8 brightness temperatures (TRI_BT) and LST retrievals (TRI_LST), along with the other LST-based indices, were calculated for the growing season (July–October) of 2015−2019 over the Australian wheatbelt. An evaluation was conducted by spatiotemporally comparing the indices with the drought indices used by the Australian Bureau of Meteorology in the official drought reports: the Precipitation Condition Index (PCI) and the Soil Moisture Condition Index (SMCI). All the LST-based drought indices captured the wet conditions in 2016 and dry conditions in 2019 clearly. Ranking of Pearson correlations of the LST-based indices with regards to PCI and SMCI produced very similar results. TRI_BT and TRI_LST showed the best agreement with PCI and SMCI (r > 0.4). TCI and VHI presented lower consistency with PCI and SMCI compared with TRI_BT and TRI_LST. ΔfRET had weaker correlations than the other LST-based indices in this case study, possibly because of outliers affecting the scaling procedure. The capability of drought early warning for TRI was demonstrated by comparing with the monthly time series of the greenness index Vegetation Condition Index (VCI) in a case study of 2018 considering the relatively slow response of the greenness index to drought. TRI_BT and TRI_LST had a lead of one month in showing the changing dryness conditions compared with VCI. In addition, the LST-based indices were correlated with annual wheat yield. Compared to wheat yields, all LST-based indices had a peak correlation in September. TRI_BT and TRI_LST had strong peak and average correlations with wheat yield (r ≥ 0.8). We conclude that TRI has promise for agricultural drought early warning, and TRI_BT appears to be a good candidate for efficient operational drought early warning given the readily accessible inputs and simple calculation approach. 相似文献
7.
研究增强型植被指数基于Landsat-8数据反演土壤水分的可行性及适用性,分析研究区土壤水分总体分布,提高该地区应对干旱灾害的能力。基于温度植被干旱指数方法,以淮河流域上游地区作为研究区,基于2017年2月的Landsat-8影像,分别计算了地表温度、归一化植被指数、增强型植被指数,基于TVDI构建了两种土壤水分反演模型。研究比较了:1) EVI在TM数据中的应用特点;2)研究区土壤含水率的空间分布特征;3)两种模型反演结果的差异。结果表明:1)基于TM数据计算的EVI总体明显低于NDVI,但不同时间段的结果并不总是低于NDVI;2)基于EVI的模型结果精度低于基于NDVI模型结果。3)两种模型结果与植被覆盖度、地表温度的关系均为负相关,其中,基于EVI的模型结果与地表温度的负相关程度极高,即基于EVI的模型结果受植被影响较小,受温度影响程度高。 相似文献
8.
Land cover changes associated with urbanisation modify microclimate, leading to urban heat islands, whereby cities are warmer than the surrounding countryside. Understanding the factors causing this phenomenon could help urban areas adapt to climate change and improve living conditions of inhabitants. In this study, land surface temperatures (LST) of Aarhus, a city in the high latitudes, are estimated from the reflectance of a thermal band (TIRS1; Band 10; 10.60–11.19 μm) of Landsat 8 on five dates in the summer months (one in 2015, and four in 2018). Spectral indices, modelled on the normalised difference vegetation index (NDVI), using all combinations of the first seven bands of Landsat 8 are calculated and their relationships with LST, analysed. Land cover characteristics, in terms of the percentages of tree cover, building cover and overall vegetation cover are estimated from airborne LiDAR data, building footprints and 4-band aerial imagery, respectively. The correlations between LST, the spectral indices and land cover are estimated.The difference in mean temperature between the rural and urban parts of Aarhus is up to 3.96 °C, while the difference between the warmer and colder zones (based on the mean and SD of LST) is up to 13.26 °C. The spectral index using the near infrared band (NIR; Band 5; 0.85-0.88 μm) and a short-wave infrared band (SWIR2; Band 7; 2.11–2.29 μm) has the strongest correlations (r: 0.62 to 0.89) with LST for the whole study area. This index is the inverse of normalised burn ratio (NBR), which has been used for mapping burnt areas. Spectral indices using different combinations of the infrared bands have stronger correlations with LST than the more widely used vegetation indices such as NDVI. The percentage of tree cover has a higher negative correlation (Pearson’s r: -0.68 to -0.75) with LST than overall vegetation cover (r: -0.45 to -0.63). Tree cover and building cover (r: 0.53 to 0.71) together explain up to 68 % of the variation in LST. Modification of tree and building cover may therefore have the potential to regulate urban LST. 相似文献
9.
Landsat 8热红外数据定标参数的变化及其对地表温度反演的影响 总被引：1，自引：0，他引：1
Landsat系列卫星上的TIRS热红外传感器数据已被大量应用,针对TIRS数据的地表温度反演也相继开发出一些算法,并有一些研究对TIRS数据的定标及其地表温度反演算法的精度进行了对比。本文主要就TIRS热红外传感器定标参数的变化,结合这些定标参数变化的时间点对有关地表温度反演算法的适用性和有效性进行分析,特别是对劈窗算法是否适合当前的TIRS数据进行了讨论,以使用户能够对Landsat 8 TIRS热红外数据的正确使用有进一步的认识。总的看来,由于视域外杂散光的影响,TIRS数据的定标精度仍达不到设计目标,TIRS第11波段的不确定性仍成倍大于TIRS 10波段。因此,在Landsat团队未彻底解决这一问题之前,同时用TIRS第10、第11这两个差距较大的波段构成的劈窗算法来反演地表温度,其精度存在较大的不确定性,US6-S团队仍在致力于改进第11波段的精度,改进后的波段可以用劈窗策法。目前应以TIRS第10单波段的方式来反演地表温度为宜。 相似文献
10.
Joanna Zawadzka Ron Corstanje Jim Harris Ian Truckell 《International Journal of Digital Earth》2020,13(8):899-914
ABSTRACT We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30?m) resolution down to 2–4?m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83?K prior to and 0.76–1.21?K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales. 相似文献
11.
Qihao Weng Mohammad Karimi Firozjaei Amir Sedighi Seyed Kazem Alavipanah 《地理信息系统科学与遥感》2019,56(4):576-604
The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness. 相似文献
12.
作为驱动地表与大气之间能量交换的关键物理量,地表温度在众多领域中都发挥着重要作用,包括气候变化、环境监测、蒸散发估算以及地热异常勘探等。Landsat热红外数据因其时间连续性和高空间分辨率等特点被广泛应用于地表温度反演中。本文详细地介绍了Landsat热红外传感器及其可用的数据与产品的现状,梳理了2001年—2020年20年间基于Landsat热红外数据的地表温度遥感反演与应用的相关文献发表及互引情况,系统地综述了基于Landsat热红外数据的地表温度反演算法,包括基于辐射传输方程的算法、单窗算法、普适性单通道算法、实用单通道算法和分裂窗算法等。在此基础上,进一步介绍了每种算法的参数化方案,包括地表比辐射率和大气参数的估算方法。最后针对Landsat热红外数据地表温度遥感反演提出了未来可能的发展趋势与研究方向。 相似文献
13.
MODIS光谱指数在中国西南干旱监测中的应用 总被引：2，自引：0，他引：2
利用标准化降雨指数SPI比较了基于MODIS反射率数据提取的8种光谱指数(NDVI、NDWI、VARI、EVI、NDIIB6、NDIIB7、D1640和SR)对中国西南四省市(四川、重庆、云南和贵州)2000年—2012年典型干旱事件的反映。研究结果表明:(1)SPI指数能直接反映监测站点附近的干旱情况,其中3个月和6个月尺度的SPI(SPI3和SPI6)对2006年和2009年—2010年该区的特大干旱事件的监测效果较好;(2)除了D1640外,其余7种光谱指数的距平值与3个月尺度的SPI3都具有显著的相关性,其中NDIIB7、NDIIB6和VARI与SPI3的相关性较高(R0.3),在一定程度上可以表征中国西南地区的干旱状况;(3)MODIS NDIIB7距平值与3个月尺度的SPI3相关性最高(R=0.35),本文以其为例,再现了云贵高原2009年—2010年特大持续干旱事件的时空演变过程。 相似文献
14.
《International Journal of Digital Earth》2013,6(3):239-257
Abstract Iraq has suffered severely from drought in recent years and the year 2008 was the driest, particularly in the Iraqi Kurdistan region. This study incorporated Geoinformation technology into mapping the drought that severely affected the Kurdistan region in the years 2007–2008. Geoinformation technology provides support in the theories, methods and techniques for building, and development of Digital Earth aspect. Five vegetation, soil, water, and land surface temperature (LST) indices were applied to two Landsat 7 ETM+ imageries of June 2007 and June 2008, to assess the drought impacts in Erbil governorate Kurdistan during the study period. The indices that were employed in this study were Normalized Difference Vegetation Index, Bare Soil Index, Normalized Differential Water Index, Tasseled Cap Transformation Wetness, and LST. The results revealed a significant decrease in the vegetative cover (56.7%) and a decline in soil/vegetation wetness (29.9%) of the total study area. Likewise, there was a significant reduction in the water bodies surface area in the region such as Dokan Lake, which lost 32.5% of its surface area in comparison with the previous year, 2007. The study results showed that the soil moisture content was the most effective actor on the vegetative cover, LST, and drought status in the study area. 相似文献
15.
Spatio‐temporal prediction and forecasting of land surface temperature (LST) are relevant. However, several factors limit their usage, such as missing pixels, line drops, and cloud cover in satellite images. Being measured close to the Earth's surface, LST is mainly influenced by the land use/land cover (LULC) distribution of the terrain. This article presents a spatio‐temporal interpolation method which semantically models LULC information for the analysis of LST. The proposed spatio‐temporal semantic kriging (ST‐SemK) approach is presented in two variants: non‐separable ST‐SemK (ST‐SemKNSep) and separable ST‐SemK (ST‐SemKSep). Empirical studies have been carried out with derived Landsat 7 ETM+ satellite images of LST for two spatial regions: Kolkata, India and Dallas, Texas, U.S. It has been observed that semantically enhanced spatio‐temporal modeling by ST‐SemK yields more accurate prediction results than spatio‐temporal ordinary kriging and other existing methods. 相似文献
16.
Ikechukwu Nnamdi Maduako Raphael Ike Ndukwu Caleb Ifeanyichukwu Onyedika Igbokwe 《Journal of the Indian Society of Remote Sensing》2017,45(4):631-642
Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value < 0.05), while in iTVDI versus TWI (R = 0.00, P value > 0.05) and TVDI versus TWI (R = ?0.01, P value > 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R = ?0.52, P value < 0.05) and iTVDI (R = ?0.63, P value < 0.05) but not with TWI (R = ?0.10, P value > 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale. 相似文献
17.
For three agricultural crop types, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), we estimated biophysical parameters including fresh and dry biomass, leaf area index (LAI), and vegetation water content, for which we found the equivalent water thickness (EWT), fuel moisture content per fresh weight (FMCFW), and fuel moisture content per dry weight (FMCDW). We performed these estimations using data from the newly launched Landsat 8 Operational Land Imager (OLI) sensor, as well as its predecessor the Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Progress in the design of the new sensor (i.e., Landsat 8), including narrower near-infrared (NIR) wavebands, higher signal-to-noise ratio (SNR), and greater radiometric resolution highlights the necessity to investigate the biophysical parameters of agricultural crops, especially compared to data from its predecessor. This study aims to evaluate vegetation indices (VIs) derived from the Landsat 8 OLI and the Landsat 7 ETM+. Both the Landsat 8 OLI and Landsat 7 ETM+ VIs agreed well with in-situ data measurements. However, the Landsat 8 OLI-derived VIs were generally more consistent with in situ data than the Landsat 7 ETM+ VIs. We also note that the Landsat 8 OLI is better able to capture the small variability of the VIs because of its higher SNR and wider radiometric range; in addition, the saturation phenomenon occurred earlier for the Landsat 7 ETM+ than for the Landsat 8 OLI. This indicates that the new sensor is better able to estimate the biophysical parameters of crops. 相似文献
18.
本文以新疆焉耆盆地为研究区,首先利用实测数据和Landsat 8 OLI遥感数据获取土壤调查植被指数（MSAVI）和地表温度（Ts）,构建Ts-MSAVI特征空间,拟合特征空间的干湿方程;然后利用该特征空间计算温度植被干旱指数（TVDIm）,反演9-11月的土壤湿度,探讨土壤湿度时空分布特征。试验结果表明：①遥感影像反演的TVDI与实地考察的土壤湿度显著相关（a=0.05）;不同土层中,TVDIm与10~20 cm土层湿度相关性最高（R=0.588）;②焉耆盆地湿度总体以半干旱为主（0.60.8）;土壤湿度空间分布上,焉耆盆地南侧为干旱区,西部和北部地区偏干旱,中部为湿润区域,对于该地区滨湖湿地和博斯腾湖附近小湖土壤湿度最高,博斯腾湖南部的沙地区土壤湿度最低,Ts与土壤湿度呈负相关;③10月湿地的TVDIm值最低,9月沙地的TVDIm值最高。TVDI模型应用于焉耆盆地取得较好的结果,可用于正确地估算土壤湿度,研究结果可为焉耆盆地生态环境和水资源提供重要的参数。 相似文献
19.
结合像元分解和STARFM模型的遥感数据融合 总被引：4，自引：2，他引：2
高空间、时间分辨率遥感数据在监测地表快速变化方面具有重要的作用。然而,对于特定传感器获取的遥感影像在空间分辨率和时间分辨率上存在不可调和的矛盾,遥感数据时空融合技术是解决这一矛盾的有效方法。本文利用像元分解降尺方法(Downscaling mixed pixel)和STARFM模型(Spatial and Temporal Adaptive Reflectance Fusion Model)相结合的CDSTARFM算法(Combination of Downscaling Mixed Pixel Algorithm and Spatial and Temporal Adaptive Reflectance Fusion Model)进行遥感数据融合。首先,利用像元分解降尺度方法对参与融合的MODIS数据进行分解降尺度处理;其次,利用分解降尺度的MODIS数据替代STARFM模型中直接重采样的MODIS数据进行数据融合;最后以Landsat 8和MODIS遥感影像数据对该方法进行了实验。结果表明:(1)CDSTARFM算法比STARFM和像元分解降尺度算法具有更高的融合精度;(2)CDSTARFM能够在较小的窗口下获得更高的融合精度,在相同的窗口下其融合精度也高于STARFM;(3)CDSTARFM融合的影像更接近真实影像,消除了像元分解降尺度影像中的"图斑"和STARFM模型融合影像中的"MODIS像元边界"。 相似文献
20.
Salomon Cesar Nguemhe Fils Mumbfu Ernestine Mimba Jean Guy Dzana Joachim Etouna Patrick Valerie Mounoumeck Mustapha Hakdaoui 《Journal of the Indian Society of Remote Sensing》2018,46(1):131-143
Douala, the most important metropolis of Cameroon, is a sub-Saharan wet coastal environment of which the anarchic urbanization is a socio-economic and environmental problem, significantly influencing the local climate. In this study, three Landsat images from 1986 (TM), 2007 (ETM+) and 2016 (LDCM), were utilized to investigate the effect of this urbanization on the increasing land surface temperature (LST) between these dates. Thus, the urban indices (UI), determined from the Landsat Visible and NIR channels were used to identify impervious areas (Urban Fabric and bare soil) of urban area. It has been shown from the UI images that, impervious areas have been increased from 1986 to 2016. The LST images derived have a continual expansion of zones and points of heat throughout these dates. The correlation analysis of LST and UI, at the pixel-scale, indicated the positive relationship between these parameters, which could show a real impact of urbanization on the increasing temperature in the area. These correlations are fairly low in 1986 (maximum R-square value is about 0.35) and in 2007 (maximum R-square value is about 0.44. In 2016, a high positive correlation (maximum R-square value is about 0.77) confirm that, the impervious areas strengthen the temperature and the Urban Heat Island effect in Douala urban zone. Overall, the earth observation images and the geographic information system techniques were effective approaches for aiming at environment monitoring and analyzing urban growth patterns and evaluating their impacts on urban climates. 相似文献