Discrimination of mangrove forests and characterization of adjoining land cover classes using temporal C-band Synthetic Aperture Radar data: A case study of Sundarbans

C-band dual polarization (HH, HV) Synthetic Aperture Radar (SAR) data from Radarsat-2 were used to discriminate and characterize mangrove forests of the Sundarbans. Multi-temporal data acquired during winter and rainy seasons were analysed for the segregation of mangrove forest area. A decision rule based classification involving combination of three-date HH (range −11 to −2 dB) with single-date cross-polarization ratio (2–8) was applied on the datasets for discriminating mangrove forests from other land cover classes. Application of textural measures (entropy and angular second moment) in the aforesaid decision rule based classification produced three broad homogeneous mangrove classes. The area covered by the most homogeneous class increased from January to March and decreased from July to September, and correlated well to the change in the phenological status of the mangroves. Extent of homogeneous areas was more in the eastern region of the Sundarbans than that of the central and western side. Thus, the study revealed that textural measures combined with multi-temporal HH backscatter and single-date cross-polarization ratio in a decision rule classification could be satisfactorily used for characterization of the mangrove forests.     

农作物长势综合监测——以印度为例

提出农作物长势综合监测方法,利用卫星遥感得到的NDVI时间序列数据,综合采用实时监测、过程监测和时间序列聚类监测方法,明确不同方法适用的监测尺度及监测目的,对不同范围农作物长势进行监测。改进了Crop Watch全球农情遥感速报系统运行化作物长势监测方法,克服了原有作物长势监测中实时监测方法无法反映相同区域苗情在整个生长过程中的连续变化情况的缺点。实现对相同区域作物长势连续变化的定量描述,可对作物长势进行更准确的判断。利用官方发布的作物单产变幅数据,对单产变幅较大的12个作物主产省区作物长势监测结果的准确性进行判断,结果表明:6个邦的实时监测和聚类监测方法所得结果一致,都符合作物单产变化的实际状况;4个邦的聚类监测方法所得结果对作物长势监测更为准确,更符合该区域作物单产的实际变化;1个邦实时监测结果对作物长势监测比聚类监测方法更为准确;只有1个邦采用两种方法对作物长势的监测存在误差,聚类监测方法在对农作物生长过程的连续监测及空间分布的定量化表述方面,比实时监测更为准确。3种方法可以综合使用,实现业务化运行的农作物长势监测。     

Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data

Beijing has experienced rapid urbanization and associated urban heat island effects and air pollution. In this study, a contribution index was proposed to explore the effect of urbanization on land surface temperature (LST) using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived data with high temporal resolution. The analysis indicated that different zones and landscapes make diurnally and seasonally different contributions to the regional thermal environment. The differences in contributions by the three main functional zones resulted from differences in their landscape compositions. The roles of landscapes in this process varied diurnally and seasonally. Urban land was the most important contributor to increases in regional LSTs. The contributions of cropland and forest varied distinctly between daytime and nighttime owing to differences in their thermal inertias. Vegetation had a notable cooling effect as the normalized vegetation difference index (NDVI) increased during summer. However, when the NDVI reached a certain value, the nighttime LST shifted markedly in other seasons. The results suggest that urban design based on vegetation partitions would be effective for regulating the thermal environment.     

A fixed full-matrix method for determining ice sheet height change from satellite altimeter: an ENVISAT case study in East Antarctica with backscatter analysis

A new method, called the fixed full-matrix method (FFM), is used to compute height changes at crossovers of satellite altimeter ground tracks. Using the ENVISAT data in East Antarctica, FFM results in crossovers of altimeter heights that are 1.9 and 79 times more than those from the fixed half method (FHM) and the one-row method (ORM). The mean standard error of height changes is about 14 cm from ORM, which is reduced to 7 cm by FHM and to 3 cm by FFM. Unlike FHM, FFM leads to uniform errors in the first-half and second-half height-change time series. FFM has the advantage in improving the accuracy of the change of height and backscattered power over ORM and FHM. Assisted by the ICESat-derived height changes, we determine the optimal threshold correlation coefficient (TCC) for a best correction for the backscatter effect on ENVISAT height changes. The TCC value of 0.92 yields an optimal result for FFM. With this value, FFM yields ENVISAT-derived height change rates in East Antarctica mostly falling between \(-3\) and 3 cm/year, and matching the ICESat result to 0.94 cm/year. The ENVISAT result will provide a constraint on the current mass balance result along the Chinese expedition route CHINARE.     

应用遥感时序数据研究植被变化与气候因子的关系——以环渤海地区为例

在全球气候变化背景下研究陆地植被的时空变化规律,探讨气候因素的驱动作用,对于预测未来气候变化对生态系统的可能影响、制定合适的生态环境保护策略具有重要意义。利用2000—2009年MODIS归一化差值植被指数(normalized difference vegetation index,NDVI)时间序列数据和地面气象站点的气温、降水量数据,从遥感角度分析环渤海地区植被的时空变化,并研究变化与气温、降水的相关关系,探讨区域植被年内和年际变化的驱动因素。结果表明,2000—2009年环渤海地区植被覆盖总体呈增加的趋势,但存在一定的空间异质性,局部有减少的倾向;区域植被的生长受温度和降水的双重驱动,对降水和温度的响应存在明显的滞后,滞后期大约为1个合成期;年际的变化主要受降水和人类活动的影响,降水增加可使区域NDVI提高;不同的人类活动会导致NDVI向相反的方向发展。     

Monitoring of urban subsidence with SAR interferometric point target analysis: A case study in Suzhou,China

Ground subsidence, mainly caused by over exploitation of groundwater and other underground resources, such as oil, gas and coal, occurs in many cities in China. The annual direct loss associated with subsidence across the country is estimated to exceed 100 million US dollar. Interferometric SAR (InSAR) is a powerful tool to map ground deformation at an unprecedented level of spatial detail. It has been widely used to investigate the deformation resulting from earthquakes, volcanoes and subsidence. Repeat-pass InSAR, however, may fail due to impacts of spatial decorrelation, temporal decorrelation and heterogeneous refractivity of atmosphere. In urban areas, a large amount of natural stable radar reflectors exists, such as buildings and engineering structures, at which radar signals can remain coherent during a long time interval. Interferometric point target analysis (IPTA) technique, also known as persistent scatterers (PS) InSAR is based on these reflectors. It overcomes the shortfalls in conventional InSAR. This paper presents a procedure for urban subsidence monitoring with IPTA. Calculation of linear deformation rate and height residual, and the non-linear deformation estimate, respectively, are discussed in detail. Especially, the former is highlighted by a novel and easily implemented 2-dimensional spatial search algorithm. Practically useful solutions that can significantly improve the robustness of IPTA, are recommended. Finally, the proposed procedure is applied to mapping the ground subsidence in Suzhou city, Jiangsu province, China. Thirty-four ERS-1/2 SAR scenes are analyzed, and the deformation information over 38,881 point targets between 1992 and 2000 are generated. The IPTA-derived deformation estimates correspond well with leveling measurements, demonstrating the potential of the proposed subsidence monitoring procedure based on IPTA technique. Two shortcomings of the IPTA-based procedure, e.g., the requirement of large number of SAR images and assumed linear plus non-linear deformation model, are discussed as the topics of further research.     

The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia

Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.     

Semi-automatic delineation of badlands using contrast in vegetation activity: a case study in the lower Chambal valley,India

Population growth worldwide leads to an increasing pressure on the land. Recent studies reported that many areas covered by badlands are decreasing because parts of badlands are being levelled and converted into arable land. It is important to monitor these changes for environmental planning. This paper proposes a remote-sensing-based detection method which allows mapping of badland dynamics based on seasonal vegetation changes in the lower Chambal valley, India. Supervised classification was applied on three Landsat (Thematic Mapper) images, from 3 different seasons; January (winter), April (summer) and October (post-monsoon). Different band selection methods were applied to get the best classification. Validation was done by ground referencing and a GeoEye-1 satellite image. The image from January performed best with overall accuracy of 87% and 0.69 of kappa. This method opens the possibilities of using semi-automatic classification for the Chambal badlands which is so far mapped with manual interpretations only.     

基于3S技术的城市辐射效应景观分析——以蓬莱市为例

本文在3S技术的支持下采用缓冲区分析,从景观和斑块两个层次对蓬莱市城市辐射效应与景观结构关系进行了研究。结果表明:在景观层次上,随着城市辐射距离的增加,景观破碎化降低。在斑块层次上,蓬莱市的城市辐射效应对果园和水浇地的影响较大,最大距离是25km;对其他斑块类型影响不大。这表明山东半岛城市化进程加剧了景观的破碎化,而且这一过程主要是通过影响果园和水浇地斑块来实现的。     

An improved dark object method to retrieve 500 m-resolution AOT (Aerosol Optical Thickness) image from MODIS data: A case study in the Pearl River Delta area,China

This paper presents an improved Dark Dense Vegetation (DDV) method for retrieving 500 m-resolution aerosol optical depth (AOT) based on MOD04-C005 arithmetic with the Moderate Resolution Imaging Spectroradiometer (MODIS) from the National Aeronautics and Space Administration (NASA). The improvements include change of the movement pattern of retrieval window, selection of a more suitable aerosol type, and storage of the look-up table. The method is then applied to obtain the AOT over the Pearl River Delta region (PRD). By comparing the results with the co-temporal ground sunphotometer observations in 2010, the correlation coefficient is found to be 0.794 with RMSE 0.139 and their variations remain consistent. Contrasts between model values in 2008 and MODIS AOT products in the same date also reveal a high accuracy of the improved DDV method. We also performed sensitivity tests to analyze the impacts of several parameters on apparent reflectance at different bands, and the results show that apparent reflectance is much more sensitive to surface reflectance and AOT than to elevation.     

Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains,California

Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling. Airborne Laser Scanning (ALS) can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains. This study proposed an ALS-based framework to quantify tree growth and competition. Bi-temporal ALS data were used to quantify tree growth in height (ΔH), crown area (ΔA), crown volume (ΔV), and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests. We analyzed the correlations between tree growth attributes and controlling factors (i.e. tree sizes, competition, forest structure, and topographic parameters) at multiple levels. At the individual tree level, ΔH had no consistent correlations with controlling factors, ΔA and ΔV were positively related to original tree sizes (R?>?0.3) and negatively related to competition indices (R?R|?>?0.7), ΔV was positively related to original tree sizes (|R|?>?0.8). Multivariate regression models were simulated at individual tree level for ΔH, ΔA, and ΔV with the R² ranged from 0.1 to 0.43. The ALS-based tree height estimation and growth analysis results were consistent with field measurements.     

Tracking long-term floodplain wetland changes: A case study in the China side of the Amur River Basin

Floodplain wetlands in the China side of the Amur River Basin (CARB) undergone consistent decreases because of both natural and anthropogenic drivers. Monitoring floodplain wetlands dynamics and conversions over long-time periods is thus fundamental to sustainable management and protection. Due to complexity and heterogeneity of floodplain environments, however, it is difficult to map wetlands accurately over a large area as the CARB. To address this issue, we developed a novel and robust classification approach integrating image compositing algorithm, objected-based image analysis, and hierarchical random forest classification, named COHRF, to delineate floodplain wetlands and surrounding land covers. Based on the COHRF classification approach, 4622 Landsat images were applied to produce a 30-m resolution dataset characterizing dynamics and conversions of floodplain wetlands in the CARB during 1990–2018. Results show that (1) all floodplain land cover maps in 1990, 2000, 2010, and 2018 had high mapping accuracies (ranging from 90 %±0.001–97%±0.005), suggesting that COHRF is a robust classification approach; (2) CARB experienced an approximately 25 % net loss of floodplain wetlands with an area declined from 8867 km² to 6630 km² during 1990–2018; (3) the lost floodplain wetlands were mostly converted into croplands, while, there were 111 km² and 256 km² of wetlands rehabilitated from croplands during periods of 2000–2010 and 2010–2018, respectively. To our knowledge, this study is the first attempt that focus on delineating floodplain wetlands at a large-scale and produce the first 30-m spatial resolution dataset demonstrating long-term dynamics of floodplain wetlands in the CARB. The COHRF classification approach could be used to classify other ecosystems readily and robustly. The resultant dataset will contribute to sustainable use and conservation of wetlands in the Amur River Basin and provide essential information for related researches.     

An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain: A case study in Changbai mountains,China

Light Detection And Ranging (LiDAR) has a unique capability for estimating forest canopy height, which has a direct relationship with, and can provide better understanding of the aboveground forest carbon storage. The full waveform data of the large-footprint LiDAR Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat), combined with field measurements of forest canopy height, were employed to achieve improved estimates of forest canopy height over sloping terrain in the Changbai mountains region, China. With analyzing ground-truth experiments, the study proposed an improved model over Lefsky's model to predict maximum canopy height using the logarithmic transformation of waveform extent and elevation change as independent variables. While Lefsky's model explained 8–89% of maximum canopy height variation in the study area, the improved model explained 56–92% of variation within the 0–30° terrain slope category. The results reveal that the improved model can reduce the mixed effects caused by both sloping terrain and rough land surface, and make a significant improvement for accurately estimating maximum canopy height over sloping terrain.     

A novel approach to estimate canopy height using ICESat/GLAS data: A case study in the New Forest National Park,UK

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints.Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies.     

A method for groundwater prospect zonation in data poor areas using remote sensing and GIS: a case study in Kalikavu Panchayath of Malappuram district,Kerala, India

Abstract

The present study was an attempt to delineate potential groundwater zones in Kalikavu Panchayat of Malappuram district, Kerala, India. The geo-spatial database on geomorphology, landuse, geology, slope and drainage network was generated in a geographic information system (GIS) environment from satellite data, Survey of India topographic sheets and field observations. To understand the movement and occurrence of groundwater, the geology, geomorphology, structural set-up and recharging conditions have to be well understood. In the present study, the potential recharge areas are delineated in terms of geology, geomorphology, land use, slope, drainage pattern, etc. Various thematic data generated were integrated using a heuristic method in the GIS domain to generate maps showing potential groundwater zones. The composite output map scores were reclassified into different zones using a decision rule. The final output map shows different zones of groundwater prospect, viz., very good (15.57% of the area), good (43.74%), moderate (28.38%) and poor (12.31%). Geomorphic units such as valley plains, valley fills and alluvial terraces were identified as good to excellent prospect zones, while the gently sloping lateritic uplands were identified as good to moderate zones. Steeply sloping hilly terrains underlain by hard rocks were identified as poor groundwater prospect zones.     

Analysis of differences in vegetation phenology cycle of abandoned farmland,using harmonic analysis of time-series vegetation indices data: the case of Gwangyang City,South Korea

ABSTRACT

Globally, countries have experienced substantial increases in farmland abandonment. Although vegetation phenology is a key factor for the classification of land use, understanding of the phenological change of abandoned farmland is lacking. Using harmonic analysis of NDVI and NDWI extracted from Landsat imagery, this study investigates the distinctive phenological characteristics of abandoned farmland, which contrasts with that of three other agricultural types (paddy, agricultural field, orchard) in the study site of Gwangyang City in Jeollanam Province, South Korea. The results suggest that abandoned farmland has higher overall greenness coverage and overall water content in vegetation than the other uses. In terms of both indices, abandoned farmlands changed with relatively less fluctuation than those of other uses, suggesting the existence of constant and unmanaged vegetation from ecological succession, which differs from crop fields that undergo cultivation procedures. The significant harmonic components differed among agricultural types and vegetation indices. In paddy, NDVI was explained with multiple, higher-order harmonic components, while in other types only first-order components met the 5% statistical significance level. With NDWI, land types were more clearly discernible, because of the different cultivation procedures involving water: wet-field method (paddy), dryland farming (orchard, agricultural field), and no cultivation (abandoned farmland). The analysis confirms that harmonic analysis could be useful in discerning abandoned farmland among areas of active agricultural use and shows that the statistical significance of harmonic terms can be employed as indicators of different agricultural types. The observed pattern of the geographic distribution of abandoned farmland has policy implications for the promotion of sustainable reuse of marginal farmland.     

Development of an image based integrated method for determining and mapping aerosol optical thickness (AOT) over urban areas using the darkest pixel atmospheric correction method,RT equation and GIS: A case study of the Limassol area in Cyprus

This paper presents the development of an image-based integrated method for determining and mapping aerosol optical thickness (AOT). Using the radiative transfer (RT) equation, a methodology was developed to create a Geographical Information System (GIS) model that can visually display the AOT distribution over urban areas. In this paper, the model was applied to eleven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) satellite images of Limassol, Cyprus during 2010 and 2011 to determine the AOT levels in Limassol Cyprus during satellite overpass. The study is innovative and unique in that the RT equation, satellite images, the darkest pixel (DP) method of atmospheric correction and GIS were integrated to derive AOT from satellite images and display the AOT distribution over an urban area without the input of any meteorological or atmospheric parameters. The accuracy of the algorithm was verified through statistical analysis by the strong agreement between the AOT values derived using the algorithm and the in situ AOT values from the ground-based sensors.     

Indicator kriging applied to absorption band analysis in hyperspectral imagery: A case study from the Rodalquilar epithermal gold mining area,SE Spain

A new approach to the analysis of hyperspectral data for the purpose of surface compositional mapping is presented in this paper. We use an interpolated value of the absorption band position and the absorption band depth for the diagnostic of mineral absorption features. Using thresholds for this depth and position, the data is transformed to indicator [0,1] values. By kriging these values, we obtain the probability of exceeding certain absorption depth and the probability of a pixel exhibiting absorption features within a specified wavelength region. Using Bayesian statistics, the indicator kriging derived probabilities are used to produce a hard classification result. By adapting the prior probabilities to the dominant mineralogy in the various alteration facies mapped, data stratification is achieved. The classification results are compared to results derived using the spectral angle mapper and maximum likelihood classification. In addition, the results are statistically compared to field spectral data classified into dominant mineralogy. The indicator approach and the spectral angle mapper produce favourable results relative to field data and in comparison to the maximum likelihood classifier. A data set from the Rodalquilar high-sulfidation epithermal gold system in SE Spain, consisting of HyMAP airborne imaging spectrometer data and ASD field spectra focusing on the key minerals alunite, kaolinite, illite and chlorite, is used to illustrate the methodology.     

Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands,South Africa

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.     

Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban–rural landscape in the Brazilian Amazon

This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban–rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method.