Advances in Remote Sensing Research for Biodiversity Monitoring

Evaluating the effect of habitat diversity on the species-area relationship using land-bridge islands in Thousand Island Lake, China.

介绍了黑河流域遥感—地面观测同步试验的科学背景、科学问题、研究目标以及观测试验方案和观测系统布置。总体目标是,开展航空—卫星遥感与地面观测同步试验,为发展流域科学积累基础数据;发展能够融合多源遥感观测的流域尺度陆面数据同化系统,为实现卫星遥感对流域的动态监测提供方法和范例。以具备鲜明的高寒与干旱区伴生为主要特征的黑河流域为试验区,以水循环为主要研究对象,利用航空遥感、卫星遥感、地面雷达、水文气象观测、通量观测、生态监测等相关设备,开展航空、卫星和地面配合的大型观测试验,精细观测干旱区内陆河流域高山冰雪和冻土带、山区水源涵养林带、中游人工绿洲及天然荒漠绿洲带的水循环和生态过程的各个分量;并且以航空遥感为桥梁,通过高精度的真实性验证,发展尺度转换方法,改善从卫星遥感资料反演和间接估计水循环各分量及与之密切联系的生态和其他地表过程分量的模型和算法。由寒区水文试验、森林水文试验和干旱区水文试验,以及一个集成研究——模拟平台和数据平台建设组成。拟观测的变量划分为5大类,分别是水文与生态变量、驱动数据、植被参数、土壤参数和空气动力参数。同步试验在流域尺度、重点试验区、加密观测区和观测小区4个尺度上展开。布置了加密的地面同步观测、通量和气象水文观测、降雨、径流及其他水文要素观测网络;使用了5类机载遥感传感器,分别是微波辐射计、激光雷达、高光谱成像仪、热红外成像仪和多光谱CCD相机;获取了丰富的可见光/近红外、热红外、主被动微波、激光雷达等卫星数据。

A recurring question in ecology is how species diversity arises and persists. Theoretical ecology tries to find underlying principles that explain spatial and temporal species diversity. Models are a valuable tool for this endeavour as they allow to study systems in well-known settings and pin down decisive processes that shape diversity. Consensus on the core mechanisms that shape diversity is achieved, namely an interplay of evolutionary and spatial processes, but many aspects still need to be included in an overarching theory. One aspect often neglected in models for the sake of simplicity is spatial heterogeneity even though heterogeneity is considered a main driver for species diversity. A similar problem exists for trophic structure. Food web theory has successfully reduced the high dimensional complexity of an ecosystem to predator-prey interactions and proven to capture essential features of empirical food webs like fraction of basal, intermediate and top species. Still many models that try to answer which processes shape diversity neglect food web structure. This work incorporates both aspects, food web structure and spatial heterogeneity, into the model-based examination of species diversity. Two different food web models considering different scales of space and time are studied: First, a meta-food web model on smaller spatial scales with classical population dynamics to examine diversity patterns found in heterogeneous landscapes and particularly at ecotones. The model suggests that the coupling strength between habitats is crucial for the final outcome of species diversity. A hump-shaped diversity-dispersal relation is observed which is enhanced compared to former studies in homogeneous spatial settings. Second, a new evolutionary food web model developed in this work which is employed to study species diversity on large spatial and temporal scales first in homogeneous and then in heterogeneous landscapes. In both settings the model reproduces a set of well-known empirical diversity patterns, namely species-area relationship, range size distribution, similarity decay of diversity with distance as well as lifetime distributions and evolution of species range sizes, but the exact shape of the relations depends on the spatial setting. Trophic levels have major impacts on the dynamics of species in both settings. Basal species have larger ranges and longer lifetimes than species on higher trophic levels. The most striking difference occurs in geographic range size evolution curves. Homogeneous spatial settings lead to symmetric curves for basal species, whilst in heterogeneous systems these curves become asymmetric. This work demonstrates that heterogeneity and complex trophic structure must not be neglected and can easily extend existing ecological models. This enhances the usability of such tools in tackling the questions related to the emergence of biodiversity in space and time. The good agreement with many results found in real systems indicates that the models presented here, despite their simplicity, capture the essence of the processes at work in reality. Consequently such models can guide future research direction and help specify empirical testable hypotheses.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images.

Biological diversity

Characterized by longitudinal mountain ranges, major rivers and deep valleys, the longitudinal range-gorge region (LRGR) in southwest China is regarded as unique worldwide in terms of landscape and biodiversity. The distinct “corridor-barrier” phenomenon caused by energy transportation and human activities makes the region an important ecological and economic corridor that links China and the Southeast Asian countries. While it houses every ecosystem in the northern Hemisphere except desert and ocean types, and is widely acknowledged as the concentrated area for various species and global gene reserve, it is subject to ecological vulnerability and frequent occurrences of natural disasters. Different ethnic groups scatter in small basins embraced by big mountains and present vast differences in respect of social and economic development. As a result, LRGR has long been regarded as a key area for earth sciences and biology research in search of environment and ecosystem change; meanwhile, LRGR is also a typical area in Western China characterized by high concentration of resources, worsening of environmental deterioration, impoverished rural poor, as well as contradiction between protection and development.Located in the upstream of the four major international rivers in Asia, LRGR is the hinge for China's regional cooperation with southeast and south Asia. At present, the region is under multi- and large-scale disturbance, projecting serious ecological problems. For example, in China's ongoing development programs such as West Development, the region serves as resource bases for biology, ferrous metal and hydropower (with more than 30 cascade dams); while in the international passage program, 3 international expressways, 3 railways and 3 navigation channels will span the region. Along with the construction of such major engineering projects, ecosystem degradation and transboundary ecological security will become increasingly serious, which in turn makes LRGR a sensitive area that deserves global attention. Multi-disciplinary and cross-sector studies on transboundary resources in the region are directed to serve 3 major national needs: ①targeting at the national West Development Strategy to identify core scientific issues of ecological development and infrastructure construction in plateau mountains; ②targeting at national ecological security to develop maintenance mechanism and control methodology for transboundary ecological security and resource bases development; ③targeting at the national opening-up to south Asia to provide scientific grounds for multi-lateral diplomacy, trade and economic cooperation, as well as conflict resolution.

Performative Microforests: Investigating the potential benefits of integrating spatial vegetation environments into buildings, in regards to the performance of buildings, their occupants + local ecosystems

Nowadays, our ability to acquire remote sensing data has been improved to an unprecedented level.[...]

Ryu, J.-H.; Jung, H.-S.; Lee, S., and Cui, T., 2019. Special Issue on “Advances in Remote Sensing and Geoscience Information Systems of the Coastal Environments”. In: Jung, H.S.; Lee, S.; Ryu, J.H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. v–xi. Coconut Creek (Florida), ISSN 0749-0208.Advanced remote sensing (RS) and geoscience information system (GIS) have become more essential to understanding the coastal environmental characteristics of Earth surfaces. In this special issue, a total of 52 papers have been published. These papers studied on a variety seas including the Yellow Sea (YS), East China Sea (ECS), South China Sea, Arctic Ocean, North West Pacific, and the Greenland Sea. Forty of these papers studied on the YS and the ECS. Remotely sensed data from various platforms, including satellite, airborne, unmanned aircraft, Helikite and Unmanned Surface Vehicle (USV) images, were used for analysis, and GIS spatial data, reanalysis data and models were also utilized. Ocean colour images were mainly applied to detect marine environment changes (SST, chlorophyll-a and suspended particle matter) and benthic and floating vegetation. High-resolution images were mainly used in the analysis of topographic changes, sedimentary phases and habitat changes in small study areas. SAR images were mainly used for detection of oil spill and sea ice, and could also be used in studies to estimate the moving speed of the target using dual receive antenna mode of the SAR sensor. Unmanned aerial vehicles were mainly used to analyze geographical features and topographic deformation along the coast. Furthermore, hyperspectral images were used for precise detection of vegetation and oil spill studies.

More and more methods in the area of biodiversity research grounds upon new opportunities arising from modern sensing devices that in principle make it possible to continuously record sensor data from the environment. However, these opportunities allow easy recording of huge amount of data, while its evaluation is difficult, if not impossible due to the enormous effort of manual inspection by the researchers. At the same time, we observe impressive results in computer vision and machine learning that are based on two major developments: firstly, the increased performance of hardware together with the advent of powerful graphical processing units applied in scientific computing. Secondly, the huge amount of, in part, annotated image data provided by today's generation of Facebook and Twitter users that are available easily over databases (e.g., Flickr) and/or search engines. However, for biodiversity applications appropriate data bases of annotated images are still missing. In this presentation we discuss already available methods from computer vision and machine learning together with upcoming challenges in automatic monitoring in biodiversity research. We argue that the key element towards success of any automatic method is the possibility to keep the human in the loop - either for correcting errors and improving the system's quality over time, for providing annotation data at moderate effort, or for acceptance and validation reasons. Thus, we summarize already existing techniques from active and life-long learning together with the enormous developments in automatic visual recognition during the past years. In addition, to allow detection of the unexpected such an automatic system must be capable to find anomalies or novel events in the data. We discuss a generic framework for automatic monitoring in biodiversity research which is the result of collaboration between computer scientists and ecologists of the past years. The key ingredients of such a framework are initial, generic classifier, for example, powerful deep learning architectures, active learning to reduce costly annotation effort by experts, fine-grained recognition to differentiate between visually very similar species, and efficient incremental update of the classifier's model over time. For most of these challenges, we present initial solutions in sample applications. The results comprise the automatic evaluation of images from camera traps, attribute estimation for species, as well as monitoring in-situ data in environmental science. Overall, we like to demonstrate the potentials and open issues in bringing together computer scientists and ecologist to open new research directions for either area.

Zn contamination of rice with different concentrations of stress was identified by the remote sensing diagnosis method from the potential Hyperspectral index and the representative spectral reflectance.At the spectral index level,the authors systematically analyzed the responsive relationships of the Hyperspectral index and four important physiological parameters under the stress of Zn pollution,which include chlorophyll content,water content,cell structure and leaf area index.Through the experiments,the authors extracted Hyperspectral remote sensing indexes which reflect the change of ecological parameters and their interactive reglarity,thus establishing the three-dimensional identification model of Hyperspectral remote sensing indexes which reflect the change of Zn contamination.At the spectral reflectance level,spectral reflectance of representative bands in visible and near infrared spectral bands were decomposed using the method of independent component analysis(ICA),and the independent components which reflect the change of Zn contamination concentration were found.Thus the visible-near infrared independent component space was established.Zn contamination with different concentrations exhibits different laws in the Hyperspectral index and independent component space,Zn contamination of rice with different concentrations can be determined combined with Hyperspectral index and independent component space,the reliability and sensitivity is improved.

The current paper is part of a complex study: “The modeling agricultural exploitation feed-back to the integration of economic and environmental principles through a sustainable management of soil resources”. This study is co-ordinated by the Institute of Agrarian Economy and Academy of Economic Studies is partner in a consortium. The objective of the study is designing scenarios of sustainable development of types of agricultural exploitation. The main objectives of the present paper are elaboration of conceptual databases of research and indicators’ selection, elaboration of methodological instruments of research and their testing in practical research, inquiries. The indicators system was grouped through many categories, thus: soil and forest indicators (the evolution of soil utilization, the relatively evolution of the arable surfaces, forest surfaces, etc.); water indicators (access to the spring water reserves; the proportion of the distributed spring water, whitch not allowed the calitative normatives, etc.); residues indicators (municipality solid residues; the rate of collecting the household residues; industrial solid residues) Pollution atmospheric indicators (the emission of the answerable gas of the green house effect; the rate of utilization of the ecological gasoline; rate of agglomerations up to 100.000 citizens which has air pollution measuring system); biodiversity indicators (native species menace with disappearing; rate of fishing fleet) seaside indicators (the distance of the artificial coasts/the total distance of the coasts; the rate of treating the used waters before emerging into the sea for coasts with more than 100.000 citizens). Preliminary results: requirements of agro-environmental policies, methods, techniques and models of sustainable development; research study: economic and mathematical methods of sustainable development of the rural area; analysis of structural issues of agricultural exploitation and soil resources; factors of agricultural productivity in the context soil resources’ preservation and protection.

Global coral reef ecosystems degrade quickly as a result of disturbances from climate change and human activities.Getting knowledge of coral reef benthic cover through remote sensing is of great importance to coral reef management and protection.A significant problem involved with remote sensing of submerged coral reef ecosystems is that water column overlying the substrate significantly affects the remotely sensed signal due to optical attenuation of light in water.Water depth,water quality,tidal variability,surface roughness,spectral similarity of various substrata,as well as spatial heterogeneity,combine to limit the accuracy with which remote sensing can be used to identify coral reef substrate type.In this article,international research work in coral reef remote sensing field were reviewed and five main topics were summarized and expounded,which were separabiltiy analysis of substrate spectral reflectance, water correction methods,spectral unmixing modeling and methods,remote sensing images classification and change detection.Research results acknowledge that classification of benthic covers,especially at fine levels,needs high spectral resolution.When spectral information is not enough,utilizing spatial information can be expected to give improvements.Optical remote sensors usually fail to get coral reef information as a result of their limited ability of water penetration and cloud contamination.Based on the understanding of coral reef remote sensing status,prospects of future development were put forward.They include as follows: hyperspectral data will be used more frequently due to their more accessibility;spatial information,including textural and contextual information,would be attached with greater importance in order to enhance classification and change detection accuracy;much more efforts need to be devoted to improving theoretical physical models of coral reef remote sensing;optical sensors can be combined with acoustic sensors;optical sensors especially to monitor coral reef environments should be designed and launched.

The basis for the 102nd voyage of NIS «Professor vodyanitsky» was the Permission of the Ministry of education and science of the Russian Federation to conduct marine scientific research №14-17 / 41 dated 30.03.2018, letter of the Ministry of education and science of the Russian Federation №14-858 dated 18.04.2018, license of the Federal service for Hydrometeorology and environmental monitoring of the Ministry of natural resources and ecology of the Russian Federation №R/2016/3020/100/L of may 30, 2017, as well as the written consent of the black sea fleet Headquarters (ex. No. 46/216 from 21.05.2018 g). Scientific research in the voyage was carried out in accordance with the themes No. 0827-2018-0001 «Fundamental research of interaction processes in the ocean-atmosphere system, determining the regional space-time variability of the natural environment and climate», No. 0827-2018-0002 «Development of operational Oceanology methods based on interdisciplinary studies of the processes of formation and evolution of the marine environment and mathematical modeling involving data of remote and contact measurements», No. 0827- 2018-0003 « Fundamental research of Oceanological processes, state and evolution of the marine environment under the influence of natural and anthropogenic factors, on the basis of observation and modeling methods» and No. 0827-2018-0004 «Complex interdisciplinary studies of Oceanological processes that determine the functioning and evolution of ecosystems of the coastal zones of the Black and Azov seas», performed within the framework of the state task by the Federal state budgetary institution of science « Marine hydrophysical Institute of the Russian Academy of Sciences), and also in accordance with the themes No. 0828-2018- 0001 «Seismological and biogeochemical bases of homeostasis of marine ecosystems», No. 0828-2018-0002 «Regularities of formation and anthropogenic transformation of biodiversity and bioresources of the Azov-black sea basin and other areas of The world ocean», No. 0828-2018-0003 « Functional, metabolic and Toxicological aspects of the existence of hydrobionts and their populations in biotopes with different physical and chemical regime», No. 0828-2018-0004 «Research of mechanisms of management of productive processes in biotechnological complexes for the purpose of development of scientific bases of reception of biologically active substances and technical products of marine Genesis» and No. 0828- 2018-0005 «the Structural and functional organization, productivity and stability of marine pelagic ecosystems» performed within the state task by Federal state budgetary institution of science « Institute of marine biological researches. A. O. Kovalevsky Russian Academy of Sciences» (INSTITUTE IMBI).