Vegetation analysis and study of its dynamics in Chandaka Wildlife Sanctuary (Orissa) using aerospace remote sensing

Changes brought in habitat conditions due to increasing human influences on natural areas have posed serious threat to wildlife. Remote Sensing has probably omerged as one of the most viable techniques to assess and monitor habitat conditions. Comparative analysis of maps of two-time period can provide authentic data with respect to changes brought in the habitat conditions. Chandaka Wildlife Sanctuary, covering an area of 213.71 sq. km in Orissa is one of the natural reserves of elephants which has undergone serious changes brought in through anthropogenic activities of urban areas of Cuttack and Bhubaneshwar lying within the proximity of the sanctuary. The natural reserve, an ideal habitat for elephants, was connected to neighbouring extensive forest belts. These connections have been either degraded or deforested over the years. The present study analyses the types of habitat available in the sanctuary using remote sensing data (aerial and satellite). Vegetation-type maps of 1975 have been prepared from B/W aerial photographs of 1:25,000 scale. For assessing the current vegetation types, maps have been prepared from Indian Remote Sensing Satellite (LISS II) false colour composite on 1:50,000 scale. Comparative evaluation of the maps indicates changes in the vegetation pattern, increase in mining and agriculture areas within the sanctuary. Stratified field sampling of vegetation types provide structural characteristics of the vegetation. Bamboo has been found to extend in the valleys and side slopes of the sanctuary area during past 15 years. An analysis on response of vegetation in all major vegetation types mapped have been made in the context of the invasion of Eupatorium odoratum. Finally, bamboo biomass has been assessed through stratified random sampling as it constitutes a major elephant food source.     

Change detection in Sal forest in Dehradun Forest Division using remote sensing and geographical information system

The review of study site have revealed the change in vegetation cover of Sal Dense to Sal Medium and Sal Open in 6 forest Mosaics owing to biotic and abiotic conditions prevailing in the specific areas. Analysis carried out using thematic map derived from aerial photograph of 1976 and satellite data of IRS 1C LISS III False Colour Composite (FCC) of March 1999 revealed the cause for change in forest density classes. Deforestation, encroachment and agriculture have been identified as the underlying causes, which have affected some specific locations to a marked extent. There has been a progressive and remarkable change among vegetation classes from 1976 to 1999. It is evident from forest type and density map that Sal density has significantly reduced from Sal Dense 65.61 % in 1976 to Sal Dense 11.12% in the year 1999 followed by Sal Open 11.18 % and Sal Medium 18.24 %. The overall change has been estimated to be 42.11% of the total forested area.     

无人机与卫星影像的叶面积指数遥感反演研究

针对卫星遥感影像获取的叶面积指数精度较低的问题,该文结合无人机低空航拍影像和卫星影像,基于最小二乘法建立了一种叶面积指数遥感反演方法,并与卫星影像像元二分模型进行了比较。结果表明:从单一植被类型到整体植被叶面积指数的反演,新方法均优于卫星影像的像元二分法,两者整体相对误差分别为27%和35%。4种植被类型中,草本植物对模型的反演精度影响较大,两者相对误差分别为32%和56%。使用该方法准确计算了长汀县相关区域叶面积指数分布,与他人结果一致。该方法提高了卫星遥感影像获取叶面积指数的精度,为大面积高精度估算区域植被提供了一种方法。     

Damage assessment after 2001 Gujarat earthquake using Landsat-7 satellite images

In this paper, we present a method of earthquake damage detection by comparing the optical images with panchromatic bands for the Gujarat, India earthquake, which occurred on January 26, 2001. The data used in this study are optical remote sensing images taken by Landsat-7 satellite on January 8 and February 29, 2001, before and after the earthquake. We have investigated the pre and post-earthquake satellite images calculating the differences in the reflection intensity (digital number) of the two images. The estimated affected area has been subtracted on a pixel unit based on the obtained frequency distributions of the differences in the optical sensor values, which show significant changes in the reflectance due to the earthquake disaster. We have investigated the accuracy of our analysis result using a classification method for the training areas with aerial photographs taken after the earthquake. The two damage detection methods show a very similar result.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan,China

Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.     

Mapping of tropical dry deciduous forest and landuse in part of Vindhyan range using satellite remote sensing

Forest vegetation of Vindhyan range located in the north of G.B. Pant Sagar (dam) has been subjected to degradation due to high biotic pressure caused by the installation of thermal power plants, coal mining, heavy cattle grazing etc. In the present study Landsat TM FCC of 1∶250,000 scale was visually analysed with respect to forest vegetation types, crown density and structure along with other landuse/land cover classes. ExceptShorea robusta (Sal) andLagerstroemia parviflora (Lendia) all forest vegetation types show higher percentage of degradation and under-stocked condition with respect to their areal extent under study. Overall classification accuracy of the forest types has been found to be 88.94%. This indicates that for obtaining reliable mapping accuracy in dry deciduous areas, satellite remote sensing data of appropriate season is essential.

     

高分四号卫星在干旱遥感监测中的应用

高分四号(GF-4)是国家高分辨率对地观测系统重大专项天基系统中的一颗地球同步卫星,为探索GF-4号卫星在大面积干旱监测中的应用,本文对该卫星在快速监测大面积干旱方面的应用能力进行了初步探讨。以2016年内蒙古自治区巴林左旗和巴林右旗地区严重旱灾为例,利用NDVI差值对该区域的干旱情况进行了监测,并与MODIS NDVI产品进行对比分析,得到了研究区内2016年干旱分布情况,结果表明其总体趋势与MODIS NDVI产品一致,且细节信息更加丰富。本文主要是GF-4卫星数据结合GF-1卫星数据对内蒙部分干旱区域进行监测和分析,体现了国产高分辨率卫星数据,尤其是GF-4卫星数据,对提高中国突发灾害的应对能力具有重要意义。     

义能矿区开采沉陷演化的TS-DInSAR监测与影响分析

为研究济宁市义能矿区开采沉陷演化特点及其对周边建筑物的影响,本文采用69景长时间序列中等分辨率Sentinel-1影像,以TS-DInSAR技术体系中的小基线集DInSAR分析为研究方法,获取了2016年初至2018年底该矿区开采面周边的沉降信息。结果表明,义能矿区在监测时段内沉陷范围较为集中,最大累计沉降量约为627 mm,通过居民点位置与沉陷结果的叠加分析,发现周边3处村庄受到了不同程度的影响,该结果能为煤矿开采沉陷控制方案的效果评估及设计优化提供重要参考作用。     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

Geology,geomorphology and mineral resources of khodana and adjoining areas (Haryana) and their significance in landuse planning

The area around Khodana, Bhiwani district, Haryana forms a part of the semi-arid region of western India. As vegetation is sparse and mainly xerophytic, photointerpretation technology was effectively utilised in bringing out the broad geology and structural pattern of the mature Precambrian terrain, which were ground-checked and correlated with the results of detailed integrated surveys carried out for base metal sulphide mineralisation in the area. A number of shear/fault zones, sub-parallel to the formational contacts and the regional fold axes, could be recognised in aerial photographs and on ground checking, were found to be intensely limonitised. The possibility of direct application of the pyrite-pyrthotite, encountered in drilling through shear zones, in landuse planning has been discussed. Discrete use of the quartzites as road ballasts, building stone and bund material, use of clay-rich pockets within pasture lands or micro-depressions in the surrounding terrain for locally-constructed brick kilns and ochreous material as pigments are some other uses of finite land resources of Haryana State.     

Vegetation cover type mapping in mouling national park in Arunachal Pradesh, Eastern Himalayas- an integrated geospatial approach

Improving image classification and its techniques have been of interest while handling satellite data especially in hilly regions with evergreen forests particularly with indistinct ecotones. In the present study an attempt has been made to classify evergreen forests/vegetation in Moulirig National Park of Arunachal Pradesh in Eastern Himalayas using conventional unsupervised classification algorithms in conjunction with DEM. The study area represents climax vegetation and can be broadly classified into tropical, subtropical, temperate and sub-alpine forests. Vegetation pattern in the study area is influenced strongly by altitude, slope, aspect and other climatic factors. The forests are mature, undisturbed and intermixed with close canopy. Rugged terrain and elevation also affect the reflectance. Because of these discrimination among the various forest/vegetation types is restrained on satellite data. Therefore, satellite data in optical region have limitations in pattern recognition due to similarity in spectral response caused by several factors. Since vegetation is controlled by elevation among other factors, digital elevation model (DEM) was integrated with the LISS III multiband data. The overall accuracy improved from 40.81 to 83.67%. Maximum-forested area (252.80 km2) in national park is covered by sub-tropical evergreen forest followed by temperate broad-leaved forest (147.09 km²). This is probably first attempt where detailed survey of remote and inhospitable areas of Semang sub-watershed, in and around western part of Mouling Peak and adjacent areas above Bomdo-Egum and Ramsingh from eastern and southern side have been accessed for detailed ground truth collection for vegetation mapping (on 1:50,000 scale) and characterization. The occurrence of temperate conifer forests and Rhododendron Scrub in this region is reported here for the first time. The approach of DEM integrated with satellite data can be useful for vegetation and land cover mapping in rugged terrains like in Himalayas.     

Reservoir capacity estimation of Singoor reservoir,India, by per-pixel and sub-pixel processing of multi-date satellite images

Traditional approaches of image classification, such as maximum likelihood and the band thresholding method, involve the per-pixel approach to delineate the water spread area of a reservoir. One of the limitations of these approaches is that the pixels representing the reservoir border, containing a mixture of water, soil and vegetation, are classified entirely as water, thereby resulting in inaccurate estimates of the water spread area. To compute the water spread area accurately, the sub-pixel approach has been used in this study. The water spread areas extracted using per-pixel and sub-pixel approaches from IRS-1D and P6 satellite image data were in turn used to quantify the capacity of the Singoor reservoir, Andhra Pradesh, India. The estimated capacity of the reservoir using the per-pixel and sub-pixel approaches was 727.75 Mm³ and 716.11 Mm³, respectively. The validation shows that the sub-pixel approach produced much less error (1.08%) than the per-pixel based approach (3.14%).     

Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data

High spatial resolution mapping of natural resources is much needed for monitoring and management of species, habitats and landscapes. Generally, detailed surveillance has been conducted as fieldwork, numerical analysis of satellite images or manual interpretation of aerial images, but methods of object-based image analysis (OBIA) and machine learning have recently produced promising examples of automated classifications of aerial imagery. The spatial application potential of such models is however still questionable since the transferability has rarely been evaluated.We investigated the potential of mosaic aerial orthophoto red, green and blue (RGB)/near infrared (NIR) imagery and digital elevation model (DEM) data for mapping very fine-scale vegetation structure in semi-natural terrestrial coastal areas in Denmark. The Random Forest (RF) algorithm, with a wide range of object-derived image and DEM variables, was applied for classification of vegetation structure types using two hierarchical levels of complexity. Models were constructed and validated by cross-validation using three scenarios: (1) training and validation data without spatial separation, (2) training and validation data spatially separated within sites, and (3) training and validation data spatially separated between different sites.Without spatial separation of training and validation data, high classification accuracies of coastal structures of 92.1% and 91.8% were achieved on coarse and fine thematic levels, respectively. When models were applied to spatially separated observations within sites classification accuracies dropped to 85.8% accuracy at the coarse thematic level, and 81.9% at the fine thematic level. When the models were applied to observations from other sites than those trained upon the ability to discriminate vegetation structures was low, with 69.0% and 54.2% accuracy at the coarse and fine thematic levels, respectively.Evaluating classification models with different degrees of spatial correlation between training and validation data was shown to give highly different prediction accuracies, thereby highlighting model transferability and application potential. Aerial image and DEM-based RF models had low transferability to new areas due to lack of representation of aerial image, landscape and vegetation variation in training data. They do, however, show promise at local scale for supporting conservation and management with vegetation mappings of high spatial and thematic detail based on low-cost image data.     

珠峰及周边地区强震影响垂直形变特征研究

2015年尼泊尔地震对珠穆朗玛峰高程的影响,近年一直受到全世界关注。2020年珠穆朗玛峰高程测量在珠穆朗玛峰及周边地区布设了高精度的全球导航卫星系统(global navigation satellite system, GNSS)形变监测网,收集了1999—2020年跨喜马拉雅山脉的32个连续运行参考站(continuously operating reference stations, CORS)的GNSS连续观测数据。利用GNSS数据监测了珠穆朗玛峰周边地区地壳三维形变特征,定量获取了2015年尼泊尔强震对珠穆朗玛峰周边CORS同震位移,以及地震对区域地壳三维形变长期趋势的影响,特别是对该地区垂直形变的影响。研究结果表明,该区域地壳垂直形变由南至北跨喜马拉雅山脉呈明显的阶梯型分布特征;震后印度板块与欧亚板块存在加速汇聚趋势,导致震后地壳隆升速率同步增大。     

面向区域与要素的城市1∶2000地形图数据库增量更新方法

大比例尺地形图数据库的更新是一项长期的重要任务。本文分析了1∶2000地形图数据库快速更新的难点,提出了一种航空摄影与卫星遥感、区域更新与要素更新相结合的大比例尺地形图数据库半自动快速更新方法。采用变化检测方法从更新前后卫星遥感影像中提取变化区域和变化要素,然后分别采用面向区域和面向要素的方法从高分辨率航空影像上测量变化地物,最后通过半自动空间实体匹配的方法建立现状库与历史库中要素的回溯关联,从而实现地形图数据库的半自动增量式快速更新。利用该方法对中山市东区的1∶2000地形图进行了更新试验。试验结果表明,引入卫星影像进行自动变化检测后,在航空影像上分区域和要素两种模式采集更新城市大比例尺地形图数据库,效率比传统方法提高25%。     

Burnt area mapping of Bandipur National Park,India using IRS 1C/1D LISS III data

The Bandipur National Park situated in the Western Ghats of Karnataka State, is one of the biodiversity hotspots of the world. During recent years, this park has witnessed repeated fires, affecting considerable areas under vegetation. The temporal satellite data from 1997 to 2006 have been analyzed to map the burnt areas using Remote Sensing (RS) and Geographic Information System (GIS) techniques. The vegetation cover is moist deciduous, dry deciduous, scrub forests and teak plantation. Information on extent of the burnt areas and the type of vegetation affected were derived forest range-wise. The fire prone regions have been identified by integrating vegetation type/density, road and settlement network and past history of forest fire occurrence, by assigning subjective weightage according to their fire-inducing capability or their sensitivity to fire. Comparison between each temporal dataset in terms of the extent of burnt area was also carried out to interpret fire incidence pattern. Three categories of fire risk regions such as Low, Moderate and High fire intensity zones were identified and it was found that almost 40% of the study area falls under low risk zone. An evaluation of the existing fire management systems and the implication of fire prevention programmes has been discussed, besides an assessment of causal factors for fire incidence in the park.     

高潜水位矿区采煤沉陷地DEM的无人机构建方法

针对高潜水位矿区采煤沉陷地植被密度大、无人机摄影测量难以获取地面点云的问题,从采煤沉陷地地形特征出发,提出了一种基于断面式点云滤波和DEM模型修正的采煤沉陷地DEM构建方法,经分析,精度可达到1：500比例尺地形图要求,可以为采煤沉陷地损毁评价和土地复垦工作开展提供重要依据。     

Wavelet-based detection of bush encroachment in a savanna using multi-temporal aerial photographs and satellite imagery

Although increased woody plant abundance has been reported in tropical savannas worldwide, techniques for detecting the direction and magnitude of change are mostly based on visual interpretation of historical aerial photography or textural analysis of multi-temporal satellite images. These techniques are prone to human error and do not permit integration of remotely sensed data from diverse sources. Here, we integrate aerial photographs with high spatial resolution satellite imagery and use a discrete wavelet transform to objectively detect the dynamics in bush encroachment at two protected Zimbabwean savanna sites. Based on the recently introduced intensity-dominant scale approach, we test the hypotheses that: (1) the encroachment of woody patches into the surrounding grassland matrix causes a shift in the dominant scale. This shift in the dominant scale can be detected using a discrete wavelet transform regardless of whether aerial photography and satellite data are used; and (2) as the woody patch size stabilises, woody cover tends to increase thereby triggering changes in intensity. The results show that at the first site where tree patches were already established (Lake Chivero Game Reserve), between 1972 and 1984 the dominant scale of woody patches initially increased from 8 m before stabilising at 16 m and 32 m between 1984 and 2012 while the intensity fluctuated during the same period. In contrast, at the second site, which was formely grass-dominated site (Kyle Game Reserve), we observed an unclear dominant scale (1972) which later becomes distinct in 1985, 1996 and 2012. Over the same period, the intensity increased. Our results imply that using our approach we can detect and quantify woody/bush patch dynamics in savanna landscapes.     

基于高分二号卫星数据的农作物分类方法研究

针对目前高分二号卫星数据(GF-2)有较高的空间分辨率而在农业领域应用较少和农作物分类普遍存在"同谱异物"和"同物异谱"的现象,以辽宁省沈阳市苏家屯区以西的新开河村周边为试验基地,利用最佳波段组合指数法(OIF)对所选取的高分二号(GF-2)卫星数据的纹理特征和植被指数以及波段信息进行筛选,选取最佳的波段组合,以增加分类信息、减少数据冗余。最后,针对筛选后的数据,使用最大似然法进行分类,得到农作物的分类结果。结果表明,利用该方法对农作物进行分类,分类精度得到了一定程度的提高,为目前大规模农作物种植面积的精确、迅速统计提供了一套可行的方案。