SOME SURVEY ODDITIES FROM BRITISH NORTH BORNEO

Abstract

A Survey gang recently built a hardwood beacon on Bukit Toodan, 1,600 feet, overlooking the sleepy hollow of Kota Belud, and an observer on returning to the station a week later found that one of the four legs of the beacon had been torn to shreds by lightning and hurled many feet away, while the concrete slab marking the exact point was discovered to have been shattered to fragments. A week or two later an observer reported the Pulau Gaya hardwood trig. beacon to be lying at an angle from the perpendicular, and on observation with a powerful telescope it was disclosed that the whole beacon was covered with flying foxes roosting upside down, whose talons had in the course of time severed one of the beacon legs–rather a shock to any ordinary observer. But that is the kind of thing that surveyors in the East have to contend with quite frequently.     

Flying-fox species and Hendra virus spillover risk: comment on ‘Factors explaining the dispersal of Hendra virus disease in South-East Queensland’ by Burnham and Chong (2016)

The approach taken by Burnham and Chong to explain the occurrence of Hendra virus incidents in Australia, while analytically valid, is compromised by erroneous and incomplete biological data underpinning the analyses. Firstly, they erroneously state the peak mating, pregnancy and birthing periods in black, grey-headed and little red flying-foxes, thus their related interpretations and conclusions are flawed. Secondly, the reported association between grey-headed flying-fox density and HeV incidents is at odds with recent findings suggesting grey-headed flying-foxes are an insubstantial host. While this broader epidemiological perspective was not within their stated aims, it is appropriate that it be canvassed in discussion of their geo-spatial analyses given the level of horse-industry, public and media interest in Hendra virus and the potential for confusion and mixed messages regarding HeV exposure risk management. We suggest that a broader collaborative cross-disciplinary approach offers synergies that produce robust, real-world ‘one health’ outputs.     

Zoning and monitoring dominant mangrove communities of a part of The Marine National Park, Gulf of Kachchh

Mangroves of the Marine National Park constitute the second largest patch of mangroves in Gujarat, extending up to 11,000 ha, comprising six species of mangroves. Earlier studies carried out using remote sensing data pertained to baseline data generation and mapping and monitoring the mangroves (density-wise) of the Park from 1975 to 1993. Using IRS IC/ID LISS III data (1998–2001) supported by ground data, the distribution of different mangrove communities in the Park has been attempted. Amongst various image-processing techniques, band ratioing followed by supervised classification gave the best result (classification accuracy was 92%).Avicennia community is the most dominant community accounting for more than 70% of the area. TheRhizophora community occupies the inward margins of the creeks and theCeriops community is present in the interior regions. The ecotone between the marsh and mangrove communities has been identified as the transitional mangroves (Avicennia alba, Sueada), representing the transition from the less saline mangrove to the highly saline marsh community. The zoning of the mangroves has also helped in assessing the diversity of the region. Based on the richness of species, three areas, namely Bhains Bid, North-east Dide Ka Bet and South-east Chhad Island have been identified as highly diverse (most suitable area for preservation).     

Spectral Characteristics of Domestic and Wild Mammals

Few studies have recorded the spectral signatures of domesticated live animals and in particular few have examined wild species. Using in situ radiometry we acquired visual and near infrared spectral signatures of wild elk (Cervus elaphus) and domesticated cattle (Bos taurus) and horses (Equus caballus). Signatures were significantly different among species across all bands with the exception of cattle and horses in the red band. Further research is needed to determine if the shallower slopes in the red-shift region of the animal signatures would allow for distinction from vegetation using various remote sensors. Application of in situ spectral signatures to remotely sensed imagery could provide an efficient method for counting wildlife.     

Estimating mineral content of indigenous browse species using laboratory spectroscopy and sentinel-2 imagery

Trees provide low-cost organic inputs, with the potential to improve livelihoods for rural communities. Understanding foliar nutrients of tree species is crucial for integration of trees into agroecosystems. The study explored nitrogen (N), phosphorus (P), potassium (K) and calcium (Ca) concentrations of nine browse species collected from the bushveld region of South Africa using wet analysis and laboratory spectroscopy in the region 400–2500 nm, along with partial least squares (PLS) regression. We further explore the relationship between canopy reflectance of Sentinel-2 image and foliar N, P, K & Ca. Laboratory spectroscopy was significant for N estimation, while satellite imagery also revealed useful information about the estimation of nitrogen at landscape level. Nitrogen was highly correlated with spectral reflectance (R² = 0.72, p < 0.05) for winter and (R² = 0.88, p < 0.05) for summer, whilst prediction of phosphorus potassium and calcium were considered not accurate enough to be of practical use. Modelling the relationship using Sentinel-2 data showed lower correlations for nitrogen (R² = 0.44, p < 0.05) and the other nutrients when compared to the dried samples. The findings indicate that there is potential to assess and monitor resource quality of indigenous trees using nitrogen as key indicator. This multi-level remote sensing approach has promise for providing rapid plant nutrient analyses at different scales.     

Predicting the distribution of C3 (Festuca spp.) grass species using topographic variables and binary logistic regression model

The spatial distribution of different C3 and C4 grass species in tropical montane areas is commonly influenced by a number of factors that include site-specific topography. Hence, the distribution of these grasses across topographic gradients can vary significantly. In this study, we investigate the influence of topographic factors (elevation, slope and aspect) on the spatial distribution of Festuca grass species in a commonage area, comprising agro-biodiversity conservation land use. Integration of the topographic variables using GIS and binary logistic regression (LR) modelling showed that C3, Festuca grass species distribution can be predicted or mapped with an accuracy of 80% in the landscape under study. The study contributes to understanding the spatial distribution of C3 grass species and provides valuable information for designing and optimizing rangeland conservation in the subtropical montane landscapes.     

ANGLO-ETHIOPIAN (SOMALILAND) BOUNDARY COMMISSION, 1932-5

Abstract

Azimuth.—The method was that of star altitudes in the prime vertical, except at X. 34 where hour angles on the P.V. were observed. At B.P. 79 and 99, NE. Terminal, and X. 12 and 34, the measures were made by Major Godfrey-Faussett or Capt. Taylor with the 8-inch C.T. & S. micrometer theodolite; and at all other stations by Capt. Taylor with the Tavistock theodolite Y. 2304.     

Multi-scale assessment of invasive plant species diversity using Pléiades 1A,RapidEye and Landsat-8 data

We used a full remote sensing-based approach to assess plant species diversity in large and inaccessible areas affected by Lantana camara L., a common invasive species within the deciduous forests of Western Himalayan region of India, using spectral heterogeneity information extracted from optical data. The spread of L. camara was precisely mapped by Pléiades 1A data, followed by comparing Pléiades 1A, RapidEye and Landsat-8 OLI – assessed plant species diversities in invaded areas. The single plant species analysis was improved by Pléiades 1A-based diversity analysis, and higher species diversity values were observed for mixed vegetation cover. Furthermore, lower Coefficient of Variation and Renyi diversity values were observed where L. camara was the only species, while higher variations were observed in areas with a mixed spectral reflectance. This study was concluded to add a crucial baseline to the previous studies on remote sensing-based solutions for rapid estimation of biodiversity attributes.     

Geospatial modelling of biological richness in Sunderbans

The present study attempts to assess the biological richness in Sunderban Biosphere Reserve (SBR) using a three-pronged approach i.e. satellite image (IRS 1D LISS-III) for vegetation/land use stratification, landscape analysis for disturbance regimes assessment and the disturbance regimes together with the ecosystem uniqueness, species richness and importance value for biological richness modelling. The study showed that four mangrove categories, viz., Avicennia, Phoenix, mixed mangroves and mangrove scrub, cover 23.21 per cent of the total geographical area of SBR. The largest area is occupied by mixed mangroves (18.31%). The overall accuracy of the vegetation/land use map worked out to be 91.67 per cent. The disturbance analysis revealed that the vegetation types were not much disturbed. Shannon-Weaver’s index of diversity was highest in case of mixed mangrove. The results revealed that 75 per cent forest area has high biological richness.     

Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data

The sustainable management and monitoring of urban forests is an important activity in the urbanized world, and operational approaches require information about the status of urban trees to determine the best strategy. One limitation in urban forest studies is the detection and discrimination of tree species using limited training data. Thus, this study focuses on developing generic rule sets from high-resolution WorldView-2 imagery in conjunction with spectral, spatial, colour and textural information for automated urban tree species detection. The object-based image analysis and its combination with statistical analysis of object features is utilized for this purpose. Results of attribute selection indicated that from 55 attributes, only 26 were useful to discriminate urban tree species, namely Messua ferrea L., Samanea saman and Casuarina sumatrana. Finally, the high overall accuracy, approximately 86.87% with kappa of 0.75 confirmed the transferability of the generic model.     

Estimation of LAI and above-ground biomass in deciduous forests: Western Ghats of Karnataka, India

This study demonstrates the potentials of IRS P6 LISS-IV high-resolution multispectral sensor (IGFOV  6 m)-based estimation of biomass in the deciduous forests in the Western Ghats of Karnataka, India. Regression equations describing the relationship between IRS P6 LISS-IV data-based vegetation index (NDVI) and field measured leaf area index (ELAI) and estimated above-ground biomass (EAGB) were derived. Remote sensing (RS) data-based leaf area index (PLAI) image is generated using regression equation based on NDVI and ELAI (r² = 0.68, p ≤ 0.05). RS-based above-ground biomass (PAGB) image was generated based on regression equation developed between PLAI and EAGB (r² = 0.63, p ≤ 0.05). The mean value of estimated above-ground biomass and RS-based above-ground biomass in the study area are 280(±72.5) and 297.6(±55.2) Mg ha⁻¹, respectively. The regression models generated in the study between NDVI and LAI; LAI and biomass can also help in generating spatial biomass map using RS data alone. LISS-IV-based estimation of biophysical parameters can also be used for the validation of various coarse resolution satellite products derived from the ground-based measurements alone.     

Measuring rapid variations in Earth orientation,geocenter and crust with satellite laser ranging

This analysis was performed with the GEOSAT software developed at NDRE for high-precision analysis of satellite tracking and VLBI data for geodetic and geodynamic applications. To determine the amplitudes of the tidally coherent daily and sub-daily variations in the Earth's orientation, geocenter, and crust, we have analyzed twelve months of SLR tracking data from the LAGEOS I & II and ETALON I & II satellites, obtained between October 1992 and September 1993. Station coordinates and mean geocenter are determined with an accuracy of 1 to 2 cm. Amplitudes of diurnal and semidiurnal variations in UT1, polar motion, and geocenter are determined with a precision of ~2µts, ~20µas, and 1–3 mm in each component. It is demonstrated that it is possible to determine a one-year continuous high-precision series in UT1 using multi-satellite laser ranging.     

机载激光雷达及高光谱的森林乔木物种多样性遥感监测

利用机载LiDAR和高光谱数据并结合37个地面调查样本数据,基于结构差异与光谱变异理论,通过相关分析法分别筛选了3个最优林冠结构参数和6个最优光谱指数,在单木尺度上利用自适应C均值模糊聚类算法,在神农架国家自然保护区开展森林乔木物种多样性监测,实现了森林乔木物种多样性的区域成图。研究结果表明,(1)基于结合形态学冠层控制的分水岭算法可以获得较高精度的单木分割结果(R~2=0.88,RMSE=13.17,P0.001);(2)基于LiDAR数据提取的9个结构参数中,95%百分位高度、冠层盖度和植被穿透率为最优结构参数,与Shannon-Wiener指数的相关性达到R~2=0.39—0.42(P0.01);(3)基于机载高光谱数据筛选的16个常用的植被指数中,CRI、OSAVI、Narrow band NDVI、SR、Vogelmann index1、PRI与Shannon-Wiener指数的相关性最高(R~2=0.37—0.45,P0.01);(4)在研究区,利用以30 m×30 m为窗口的自适应模糊C均值聚类算法可预测的最大森林乔木物种数为20,物种丰富度的预测精度为R~2=0.69,RMSE=3.11,Shannon-Wiener指数的预测精度为R~2=0.70,RMSE=0.32。该研究在亚热带森林开展乔木物种多样性监测,是在区域尺度上进行物种多样性成图的重要实践,可有效补充森林生物多样性本底数据的调查手段,有助于实现生物多样性的长期动态监测及科学分析森林物种多样性的现状和变化趋势。     

Using in situ hyperspectral reflectance data to distinguish nine aquatic plant species

In situ hyperspectral reflectance data were studied at 50 bands (10 nm bandwidth) over the 400–900 nm spectral range to determine their potential for distinguishing among nine aquatic plant species: American lotus [Nelumbo lutea (Willd.) Pers.], American pondweed (Potamogeton nodusus Poir.), giant duckweed [Spirodela polyrrhiza (L.) Schleid.], Mexican waterlily (Nymphaea mexicana Zucc.), white waterlily (Nymphaea odorata Aiton), spatterdock [Nuphar lutea (L.) Sm.], giant salvinia (Salvinia molesta Mitchell), waterhyacinth [Eichhornia crassipes (Mart.) Solms] and waterlettuce (Pistia stratiotes L.). The species were studied on three dates: 30 May, 1 July and 3 August 2009. All nine species were studied in July and August, while only eight species were studied in May; giant duckweed was not studied in May due to insufficient availability. Two procedures were used to determine the optimum bands for discriminating among species: multiple comparison range tests and stepwise discriminant analysis. Multiple comparison range tests results for May showed that most separations among species occurred at bands 795–865 nm in the near-infrared (NIR) spectral region where up to six species could be distinguished. For July, few species could be distinguished amongthe 50 bands; most separations occurred at the 715 nm red-NIR edge band where four species could be differentiated. The optimum bands in August occurred in the green (525–595 nm), red (605–635 nm) and red-NIR edge (695–705 nm) spectral regions where up to six species could be distinguished. Stepwise discriminant analysis identified 11 bands in the blue, green, red-NIR edge and NIR spectral regions to be significant to discriminate among the eight species in May. For July and August, stepwise discriminant analysis identified 15bands and 13 bands, respectively, from the blue to NIR regions to be significant for discriminating among the nine species.     

Landscape characterization of Sariska National Park (India) and its surroundings

Landscape characterization gives an overall information on the status of Land Use and Land Cover(LULC),changes in its composition and the impact of natural and human influences operating at different spatial and temporal scales.This information can be used to monitor changes in natural forest resources and protected areas,delineate potential conservation areas and can serve in effective management of ecologically fragile landscapes.In the present study,geo-spatial tools were used to characterize the landscape of Sariska National Park and its surroundings.Satellite data was used to prepare LULC maps for 1989 and 2000,change detection analysis and computation of landscape metrics.Climatic data,field records and modeling tools were used to map the po-tential spread of two invasive species,Prosopis juliflora and Adhatoda vasica.The results show that the forest area increased from 1989 to 2000,indicating better management practices.Landscape metrics(PAFRAC,PLADJ and AI)also support this argument.Improvements in the degraded forest can further enhance this effect.The entire reserve however is suitable for the invasion of P.juliflora and A.vasica but is more pronounced in Boswellia serrata and Anogeissus pendula-Acacia catechu(open)forests.A detailed landscape characterization map can help forest managers to make important policy decisions concerning issues such as in-vasive species.     

无人机遥感的大型野生食草动物种群数量及分布规律研究

以黄河源玛多县为研究区,利用无人机分别于2017年冬春季开展了航拍调查,航拍有效面积达326.6 km2,获取影像23784张,建立了藏野驴、藏原羚、岩羊等野生动物,以及牦牛、藏羊和马等家畜的无人机图像解译标志库。通过人机交互方式解译,获取调查样带内的种群数量:藏野驴的样带密度为1.15只/km2,藏原羚为0.61只/km2,岩羊为0.62只/km2,家养牦牛为4.12只/km2,家养藏羊为7.34只/km2,马为0.06只/km2。利用冷暖季草场的估算方法,通过地面同步调查验证、统计数据验证,估算出玛多县藏野驴、藏原羚和岩羊,以及家牦牛、藏羊和马的种群数量为:藏野驴17109头,藏原羚15961只,岩羊9324只,牦牛70846头,藏羊102194只,马1156匹。大型野生食草动物藏野驴、藏原羚和岩羊总计8.57万羊单位;家畜藏羊、牦牛和马总计38.90万羊单位;大型野生食草动物和家畜总计47.5万羊单位。大型野生食草动物羊单位数量与家畜羊单位数量之比为1∶4.5。并分析了野生动物分布密度与栖息地生境因子的关系:藏野驴偏好选择高程4200—4400 m,坡度为2°—5°,离农村居民点距离1—2 km和4—5 km,离水源距离小于1 km,离公路距离2—3 km和4—5 km的范围内,草地盖度60—80%。藏原羚偏好选择高程4100—4200 m和4400—4500 m,坡度为大于5°,草地盖度80%以上,离农村居民点距离2—3 km,离水源距离小于1—2 km,离公路距离小于1—2 km和4—5 km的范围内。岩羊偏好选择高程4100—4200 m,坡度为大于5°,草地盖度较低,离农村居民点距离小于1 km,离水源距离小于1—2 km,离公路距离大于5 km的范围内。与传统的地面调查方法相比,基于无人机遥感的大型野生食草动物种群数量调查,具有快速、经济、可靠等优点,为今后野生动物调查提供了一种有效、可靠的技术途径。     

Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands

Abstract

The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas. The objectives are: (1) to test the applicability of UAV visible an near-infrared (VNIR) and geometrical data based on Z values of point dense cloud (PDC) raster to separate forest species and dead trees in the study area; (2) to explore the relationship between UAV VNIR data and individual spruce health indicators from field sampling; and (3) to explore the possibility of the qualitative classification of spruce health indicators. Analysis based on NDVI and PDC raster was successfully applied for separation of spruce and silver fir, and for identification of dead tree category. Separation between common beech and fir was distinguished by the object-oriented image analysis. NDVI was able to identify the presence of key indicators of spruce health, such as mechanical damage on stems and stem resin exudation linked to honey fungus infestation, while stem damage by peeling was identified at the significance margin. The results contributed to improving separation of coniferous (spruce and fir) tree species based on VNIR and PDC raster UAV data, and newly demonstrated the potential of NDVI for qualitative classification of spruce trees. The proposed methodology can be applicable for monitoring of spruce health condition in the local forest sites.     

Sentinel-2卫星落叶松林龄信息反演

林龄结构信息能够有效反映区域森林群落不同生长阶段的固碳能力,对于评估森林生态系统的健康状况具有重要意义。本研究以中国温带典型优势树种落叶松林为研究对象,分别选择其芽萌动期、展叶期和落叶期时段的Sentinel-2影像,采用多元线性回归（MLR）、随机森林（RF）、支持向量机回归（SVR）、前馈反向传播神经网络（BP）以及多元自适应回归样条（MARS）等5种方法依次构建落叶松林龄反演模型。通过相关性分析首先确定最佳遥感反演物候期,并在此基础上根据相关性差异筛选出5个最优特征变量用于模型反演,分别为冠层含水量（CWC）,归一化水体指数（NDWI）,叶面积指数（LAI）,光合有效辐射吸收率（FAPAR）和植被覆盖度（FVC）。研究结果表明,展叶期为落叶松林最佳遥感反演物候期。除植被衰减指数（PSRI）以及落叶期的NDVI、RVI外,落叶松林龄与各指标之间均呈负相关关系,其中与冠层含水量（CWC）的相关性最高,pearson相关系数达到-0.74（p<0.01）。此外,不同模型反演结果表明,随机森林模型（RF）为最佳落叶松林龄估测模型,其平均决定系数R²和平均均方根误差RMSE分别为0.89和2.91 a;多元线性回归模型（MLR）的林龄估测结果最差,其平均决定系数R²和平均均方根误差RMSE仅为0.57和5.69 a,非线性模型能更好的解释林龄与建模变量之间的关系。     

Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China

Abstract

Determining oil slick thickness plays an important role in assessing oil spill volume and its environmental impacts on the ocean. In this study, we used a Hyperion image of an oil spill accident area and seawater and fresh crude oil samples collected in the Bohai Sea of China. A well-controlled laboratory experiment was designed to simulate spectral responses to different oil slick thicknesses. Spectral resampling and normalization methods were used to reduce the differences in spectral reflectances between the experimental background seawater sample and real background seawater. Fitting the analysis with laboratory experimental data results showed a linear relationship between normalized oil slick reflectance and normalized oil slick thickness [20th band (R ²=0.92938, n=49, p<0.01), 26th band (R ²=0.93806, n=49, p<0.01), 29th band (R ²=0.93288, n=49, p<0.01)]. By using these statistical models, we successfully determined the normalized oil slick thickness with the Hyperion image. Our results indicate that hyperspectral remote sensing technology is an effective method to monitor oil spills on water. The spectral ranges of visible green and red light were the optimal bands for estimating oil slick thickness in case 2 water. The high, stabilized spectral reflectance of background seawater will be helpful in oil slick thickness inversion.