Since rapid human population growth, overconsumption of natural resources by human activities and climate change, loss and extinction of species is increasing, and biodiversity become an important global issue. Traditional ground-based biodiversity researches focus on the species or community, which can not provide necessary information for biodiversity conservation and assessment at a large scale. Since the advantages in spatial coverage and time series, remote sensing is very useful in large-scale biodiversity monitoring, mapping and assessment. According to the height of the platform, remote sensing platforms can be classified into satellite remote sensing, airborne remote sensing and near-surface remote sensing, which can obtain biodiversity information at different spatial scales. The purpose of this study is to review the recent advances of application of different remote sensing platforms for biodiversity research. We focus on the following aspects, such as observation methods, research scale, and analyze advantages and limitations of different remote sensing platforms. Finally, we summary the future application of remote sensing in biodiversity research. From the literature statistics result, we found that satellite platform were used more frequently in biodiversity research than other remote sensing platform. Due to the high flight cost, the biodiversity researches used airborne remote sensing was fewer than the researches used satellite. Near-surface remote sensing includes the UAV platform and the ground-based platform, which is an emerging remote sensing platform and hotspot in remote sensing of biodiversity. Compared to satellite and airborne remote sensing platforms, the near-surface remote sensing platform can directly observe the individuals and can directly obtain information from species or population. Although there are some limitations in these three platforms, we believe that remote sensing technology can better serve biodiversity conservation and assessment from different temporal and spatial scales with the development of remote sensing platforms and the improvement of sensors.

Human activities has increasingly threatened the biodiversity of the world. Biodiversity science is a discipline that depends on scale, and research questions are often affected by the ecological process of multi-temporal scales. The traditional survey methods of biodiversity are often limited by human and material resources. It is therefore urgent to integrate different data sources in the biodiversity sciences. The remote sensing technique has developed from optical remote sensing to the multi-source remote sensing including different platforms combined with various sensors, and further to integrate the hyperspectral and hyper spatial resolution and light detection and ranging (LiDAR). The large coverage, the accessibility to remote areas, and the long-term repeatability of the remote sensing technique provide new and better solutions for studying ecological and scientific issues at different temporal and spatial scales. In this paper, we review the opportunity and challenges in the application of remote sensing in biodiversity sciences and conservation practices. Specifically, we focus on the applications of remote sensing in the issues related to the population dynamics, species interaction and community diversity, functional traits and functional diversity and biodiversity management. We suggest that the satellite and airborne remotes that employed multi-band or hyperspectral, high spatial resolution and LiDAR provide biodiversity information from different scopes, and will play essential roles in the investigation of biodiversity in large-scale and remote areas. In the near future, species discrimination technique based on spectral characteristics and structure detection based on LiDAR will improve our understanding of the biodiversity sciences and management. We suggest to strengthen the communication between remote-sensing scientists and biodiversity researchers to promote the application of remote sensing technologies in biodiversity research and at different temporal and spatial scales.

Developing effective policies for biodiversity conservation and restoration policies requires spatially and temporally explicit data on distribution of species and habitats. Remote sensing provides an effective technical tool to meet this requirement. In recent years, the rapid development of integrated multi-platform, multi-scale, multi-mode remote sensing technology the implementation of integrated remote sensing observations across space-air-field provides novel opportunities for biodiversity monitoring. In this paper, we review the main methods of remote sensing that aids biodiversity monitoring and assess existing remote sensing observation experiments. We found that current methods of biodiversity monitoring using remotely-sensed data lacked the support of space-air-field integrated observations and the existing space-air-field integrated observations did not include biodiversity parameters. The Wanglang integrated observation and experiment station for mountain ecological remote sensing illustrates the potential to integrate experimental station-based and space-air-field integrated observations for biodiversity monitoring. Our review highlights that integrating direct observations with remote sensing can provide spatio-temporally explicit information on species and habitats and improve the informed monitoring of biodiversity.

Poyang Lake, the largest freshwater lake in China, is one of two lakes that maintain a natural hydrological link with the Yangtze River. The lake system is critical for biodiversity conservation globally, harboring large number of migratory waterbirds. During the dry season, Poyang Lake fragments in to numerous sub-lakes, and different sub-lakes have different hydrological control and management mode. However, the recent hydrological alternation, presumably caused by the operation of Three Gorge Dam (TGD), is threatening the ecological integrity of the lake system, especially as a wintering ground for waterbirds. A robust investigation of the effects of TGD on vegetation cover and productivity at this critical biodiversity hotspot is therefore timely, and could incorporate recent advances in remote sensing and ecological modelling. In this study, using MODIS EVI (enhanced vegetation index) time series, we investigated the spatiotemporal patterns of growth in the lake for the period of 2000-2014, which includes periods before (2000-2006) and after (2007-2014) TGD was commissioned. Firstly, we extracted 107 16-day MODIS EVI time series (February 2000 to April 2015) for 10 randomly placed transects across the lake. We then applied the adaptive Savitzky-Golay smoothing algorithm to the EVI time series, and extracted four key growth metrics, namely, the starting date of growth season, growth season length, seasonal peak EVI, and productivity index. We found significant associations between the hydrological alternation and changes in vegetation seasonality. First, we found that the vegetation growth characteristics of wetlands under different hydrological control modes showed significant differences. In particular, the vegetation located in the freely connected sub-lakes had a later start of growing season, shorter growing season, lower peak EVI value, and lower primary productivity compared to sub-lakes of other modes. Second, due to the hydrological alteration, growth characteristics of sites in freely connected sub-lakes displayed two cycles per year and differed significantly before and after 2006. The advance of the autumn growing season led to excessive accumulation of biomass, which reduced the palatability of the food of migratory geese. However, this difference does not exist in the sites located in the local controlled sub-lake. Third, free connected and local controlled sub-lakes are both important for the protection of migratory birds of Poyang Lake. It is necessary to protect areas harboring both types of sub-lakes to provide a wider food source for wintering migratory birds. Local hydrology control can, to some extent, slow down the impact of much larger scale hydrological alteration on wetland vegetation growth.

Mangrove forests are ecological communities growing in the intertidal zone of tropical and subtropical coastlines. Due to their high productivity, mangrove forests are essential to persistence of biodiversity along coastlines and have high carbon sequestration ability. In this article we review aspects of monitoring mangrove forests using recent multi-source remote sensing data. First, we reviewed studies on monitoring mangrove dynamics. By integrating object-based and pixel-based classification, high spatial resolution images were used to classify different mangrove species. Landsat images were then used to monitor the dynamics of mangrove forests and analyze factors driving them. Second, we reviewed studies measuring structural parameters of mangroves. Specifically, unmanned aerial vehicle multispectral data and ground-based Light Detection and Ranging (LiDAR) data were used to compute leaf area index of mangrove forests. Finally, we reviewed studies examining physiology and biochemistry parameters. These studies explored adaption of chlorophyll content in mangrove forests under different submergence conditions, whether the invasive species Spartina alterniflora affects the light use efficiency and changed the response of photochemical reflectance index (PRI) to LUE. Our review provides a useful reference for selecting appropriate analytical methods when extracting information of mangroves from remotely sensed data. We emphasize the effectiveness of remote sensing in studying mangrove spatiotemporal patterns, extracting structural parameters, monitoring biochemical parameters, thus aiding efforts to conserve mangrove ecosystems.

Wildlife population is low and most of them are very sensitive to human disturbing, which makes traditional survey difficult. As known, infra-red camera technology has many advantages in wildlife study. However, with its wide application and increased data amount, researchers are facing some problems concerning infra-red camera monitoring and later data processing and analyzing. This paper describes in details three key problems on infra-red camera data management and use, such as lacking standardization, integration and normalization. The present paper also lists and analyzes eight aspects about photo data mining, based on researches carried out in the Qinling Mountains, Wolong Nature Reserve, etc. It involves individual recognition, temporal/spatial activity pattern, information-extracting of occasional species, behavior and reproduction, disease situation and interference by humans. If all this information can be used effectively, we hope to provide scientific support at some extent on wildlife and biodiversity conservation and management in future.

Avian ecological studies tend to center on birds and their habitats. According to the literature, studies in avian ecology have shifted from focusing on behavior and habitat selection to focusing on human disturbance, habitat suitability and habitat structure, which has been made possible partially due to remote sensing (RS) technology. Characteristics and applications of RS data are varied. Here, we assessed various RS methods, considering the current state of avian ecology. Light remote sensing is most commonly used. Infrared trigger cameras and video complement field work to monitor brooding, defensive and other behaviors, while the infrared images contain massive amounts of data. Multi-spectral images are used most frequently for mapping habitat and can directly track species when captured at a high spatial resolution. Hyperspectral data has great potential in classifying objects with similar spectral characteristics. LiDAR data mainly contributes to studies of habitat structure. Researchers have used Radar to monitor flying birds over extended periods of time, where the microwave images with multi-polarization may promote the precision of mapping complex habitats. In practice, we recognize that data scale may affect study results and that some RS inversion model parameters lack ecological significance. Multi-source data could enhance mapping accuracy and provide context for the intersection of spatial and temporal resolutions of images. In the future, RS technology development should pay more attention to provide specific spectral information, more convenient interpretation methods, and more rational multi-source data combinaions, for a better use of them.

Ecological niche modeling seeks to infer the relationship between occurrences of a species and environmental covariates and has been widely applied in biodiversity studies. Light detection and ranging (LiDAR) is a new active remote sensing technology that is being increasingly used for acquisition of 3D structural information of forests. However, its applications in ecological niche modeling are rarely studied. In this study, we wanted investigate the effectiveness of LiDAR in modeling the spatial distribution of fisher (Martes pennanti) in the southern Sierra Nevada Mountains, California. We used artificial neural networks, generalized linear model, generalized additive model, discriminative maximum entropy, and multivariate adaptive regression splines to implement the presence and background learning (PBL) method separately. We then combined all the models based on weighted average to create an ensemble model. The generative maximum entropy model was also considered for comparison. Area under the receiver operating characteristic curve (AUC) and F_pb based on presence and background data were used to evaluate the continuous and binary outputs, respectively. Our results show that the values of AUC and F_pb were 0.779 and 1.077, respectively, when only climate variables (such as temperature and precipitation) were included in the models, whereas the values of AUC and F_pb were 0.800 and 1.106, respectively, when both climate and LiDAR-derived variables (such as canopy bulk density, height to live canopy base, leaf area index, digital elevation model, slope, etc.) were included in the models. Therefore, we conclude that LiDAR-derived variables are helpful in modeling the spatial distribution of fisher, and has good potential in ecological niche modeling.

Accurate coordinate position is a prerequisite for combining drone-assisted remotely sensed data and ground survey data. However, in the practice of surveying forests, many factors prevent accurate measurement of coordinate position and inaccurate coordinates may lead to incorrect conclusions. Therefore, researchers must pay attention to factors effecting accuracy of position. In this study, we compared location error of ground control points (GCPs), model error of photogrammetric point cloud (estimated by Photoscan software) and reprojection error of camera exposure position. First, we found that real time kinematic (RTK) global navigation satellite system (GNSS) cannot locate position in tropical forest with high accuracy. The root mean square error (RMSE) of GCPs in canopy gaps were 0.167 ± 0.158 m and 0.297 ± 0.170 m in the horizontal and vertical axes respectively. In comparison, RMSE of GCPs within forests were 0.392 ± 0.368 m and 0.657 ± 0.412 m respectively for horizontal and vertical axes. Second, the number and measurement accuracy of GCPs influenced model error of photogrammetric point cloud. Third, reprojection error of camera exposure position (18.434 ± 5.252 m and 34.042 ± 6.920 m in horizontal and vertical axes respectively) was much greater than location error of GCPs when the drone acquired position with a single-station GPS system. Fourth, standard deviation of difference between estimated digital terrain model (DTM_estimated) and measured digital terrain model (DTM_measured) was positively correlated with mean canopy height (r = 0.713, P < 0.05). DTM_estimated was better estimated at 20 ha scale than at 1 ha scale. Based on these results, we suggest that uniform distribution and sufficient numbers of GCPs can improve drone-assisted mapping accuracy. Lightweight-drone-based photogrammetry has an advantage in requiring fewer equipment and enabling creation of accurate DSM (digital surface model), but remains incapable of estimating ground elevation. Researchers should consider these factors related to accuracy before using drones for surveys.

The application of UAV technology brings new opportunities and revolutions to the monitoring and research on biodiversity in protected area. However, we find that no monitoring technology solutions combining UAV and ground approaches have been formed yet, due to the lack of concerns on protected objects. Considering to better perform various monitoring technology strengths and to effectively support management and monitoring in protected area, we review study cases on UAV and ground investigation domestic and overseas firstly, and then compare the demands from conservation, management and monitoring of all kinds protected area in China. In general, the ecosystems, species, site relics and landscape are regarded as protected objects in all kinds of protected area. Meanwhile, conservation, recovering, study, education, recreation, and sustainable development become the management goals. Based on the demands mentioned above, we present an integrated technology solution which composed of four categories and 14 subjects for UAV and ground to monitor biodiversity coherently. This solution includes image recognition and classification, data inversion and pattern analysis, digital modeling and surface measuring, patrolling and inspection. In addition, monitoring time and frequency, index, integration approach, data postprocessing can be acquired in the solution. Furthermore, monitoring subjects were chosen to apply and test in the Three Parallel Rivers World Heritage, such as plant identification, vegetation growth, landscape pattern, surface measuring, and law enforcement. While achieving good results on the solution verification, we hope that this monitoring solution will do significant help to improve the protected area biodiversity conservation and management level, also be part of technological storage in assessment and supervision.