An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

基于随机森林模型的云南元阳梯田地形因子分析

为探究地形因子对梯田空间分布的影响,以云南元阳梯田坝达流域为研究区,运用GeoEye-1遥感卫星数据(2010年,1 m分辨率)和数字高程模型(20 m分辨率),提取梯田空间分布信息以及海拔高程、地形坡度、地形坡向、地面曲率、高程变异系数、地形起伏度、地面粗糙度、地表切割度、到水系距离9个地形因子。采用随机森林(Random Forest)方法建模,结合ROC曲线和AUC值对模型进行精度评价,根据随机置换残差均方减少量和因子作用曲线,探讨地形因子对梯田分布的影响规律。研究结果表明:该流域内梯田总面积1 158395 hm2,模型AUC值为0.947,海拔高度因子的随机置换残差均方减少量为38814,到水系距离因子为199.77,地面坡向因子为80.26,三者占总值的73.45%。由此可见,元阳梯田的空间分布主要受海拔高度、到水系距离、地面坡向3类地形因子的影响,其因子曲线表明梯田分布与地形因子间呈非线性关系。     

珠江流域降水集中度时空变化特征及成因分析

基于珠江流域内43个常规气象站点1960~2012年的逐日降水资料,计算了流域内各站点长期降水集中度(LCI)和逐年降水集中度(ACI);采用Mann-Kendall趋势检验法和Sen’s坡度检验法检测ACI时间上的变化趋势;同时采用反距离权重插值法(IDW)对LCI的区域特征和ACI的变化趋势进行空间插值以分析降水集中度的时空分布规律;采用随机森林(RF)算法对降水集中度的影响因子进行重要度分析。结果表明:(1)珠江流域逐年降水集中度ACI的年际变化不明显,东南部呈上升趋势,西北部呈下降趋势;(2)珠江流域西北部长期降水集中度LCI值偏小,即降水分布较平均;东南部长期降水集中度LCI值偏大,即降水较集中,表明该地区降水极值情况发生的几率相对较高,该空间分布趋势可能是受距离海洋的远近及海拔的影响;(3)众多气候影响因子中,东亚夏季季风(EASMI)对珠江流域的降水集中度影响最明显。     

基于SPOT6遥感影像的滩涂湿地入侵种互花米草植株高度的反演研究

本文以SPOT6 高空间分辨率遥感影像为数据源,通过植被覆盖度和地上生物量两个参数进行滩涂湿地入侵种互花米草植株高度的估算研究。结果表明,三沙湾滩涂湿地互花米草植株高度平均值为2.04 m,以1~2 m和2~3 m植株为主要分布高度,分布面积分别为6.83 km2和10.31 km2,占研究区互花米草总面积的33.83%和51.06%,小于1 m和大于3 m的互花米草仅占9.26%和5.84%。估算值与真实值之间的均方根误差为0.204,绝对误差为0.04~0.37 m。该方法是对高空间分辨率光学影像应用研究的重要尝试,其反演方法具有较好的可行性,可较为准确的获取滩涂湿地植株高度信息。     

Possibilities of land use change analysis in a mountainous rural area: a methodological approach

Decreasing population density is a current trend in the European Union, and causes a lower environmental impact on the landscape. However, besides the desirable effect on the regeneration processes of semi-natural forest ecosystems, the lack of traditional management techniques can also lead to detrimental ecological processes. In this study we investigated the land use pattern changes in a micro-region (in North-Eastern Hungary) between 1952 and 2005, based on vectorised land use data from archive aerial photos. We also evaluated the methodology of comparisons using GIS methods, fuzzy sets and landscape metrics. We found that both GIS methods and statistical analysis of landscape metrics resulted in more or less the same findings. Differences were not as relevant as was expected considering the general tendencies of the past 60 years in Hungary. The change in the annual rate of forest recovery was 0.12%; settlements extended their area by an annual rate of 3.04%, while grasslands and arable lands had a net loss in their area within the studied period (0.60% and 0.89%, respectively). The kappa index showed a smaller similarity (~60%) between these dates but the fuzzy kappa and the aggregation index, taking into account both spatial and thematic errors, gave a more reliable result (~70–80% similarity). Landscape metrics on patch and class level ensured the possibility of a detailed analysis. We arrived at a similar outcome but were able to verify all the calculations through statistical tests. With this approach we were able to reveal significant (p < 0.05) changes; however, effect sizes did not show large magnitudes. Comparing the methods of revealing landscape change, the approach of landscape metrics was the most effective approach, as it was independent of spatial errors and ensuring a multiple way of interpretation.     

关岭-贞丰石漠化治理示范区植被覆盖变化及其对气候因子的响应

为揭示喀斯特石漠化治理示范区植被覆盖变化以及气候因子对植被覆盖变化的影响,利用2006—2015年Landsat 30 m/16 d分辨率影像数据,采用最大合成法、NDVI差值指数和相关、偏相关分析法,系统分析示范区归一化植被指数的时空变化特征及其与气候因子的关系。结果表明:(1)2006—2015年最大NDVI平均值为0.39,NDVI较高覆盖区域在示范区南北边界,而较低覆区域以花江南岸为主;(2)2006年以来示范区极低(-1.210)、低(-0.669)和中等(-0.729)植被覆盖度呈减少趋势,高(1.359)和极高(1.247)植被覆盖度增加,整体上呈显著增加趋势;(3)本月NDVI与本月、上月、上上月降雨量和气温的相关性均通过显著水平0.05检验,且本月NDVI与本月降雨量相关性高于本月气温(RNDVI降雨 =0.782),本月NDVI与上月气温相关性高于上月降雨量(RNDVI气温 =0.771);(4)在月尺度上,示范区植被生长对降雨量无滞后期,而对气温存在1个月的滞后期。      

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.     

Assessing soil salinity using WorldView-2 multispectral images in Timpaki,Crete, Greece

Salinization is one of the major soil degradation threats occurring worldwide. This study evaluates the feasibility of operational surface soil salinity mapping based on state-of-the-art Earth Observation (EO) products captured by sensors on-board WorldView-2 (WV2) and Landsat 8 satellites. The proposed methods are tested in Timpaki, south-central Crete,Greece, where brackish water irrigation puts soil health at risk of soil salinization. In all cases, EO products are calibrated against soil samples collected from bare soil locations. Results indicate a moderate correlation of observed EC_e values with the investigated remote sensing parameters. Regarding sensitivity to saline soil, the yellow band displays higher values. Comparison between methods used in the literature shows that those developed specifically for soil salinity, and especially index S5, perform better. The proposed ‘detection index’ and 3D PCA transformation methodology perform reasonably well in detecting areas with high EC_e values and provide a simple and effective operational alternative for saline topsoil detection and mapping.     

Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.