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Tree species mapping from the individual crown to landscape scales provides crucial information on the diversity and richness of forest ecosystems, supporting major conservation decisions under ongoing climate change. With the emergence of Remote Piloted Aircraft (RPA), high spatial resolution datasets can be obtained and analyzed to inherently improve the current understanding of broadleaf tree species distribution. The utility of RPA for mapping broadleaf species at broader scales using satellite data needs to be explored. This study investigates the use of RPA RGB imagery captured during peak fall foliage to leverage coloration commonly exhibited by different broadleaf tree species during phenology transition to delineate individual tree crowns and map species distribution. Initially, a two-step hybrid segmentation procedure was designed to delineate tree crowns for two broadleaf forests using RPA imagery collected during the fall season. With the tree crowns, a subsequent Object-based Random Forest (ORF) model was tested for classifying common and economically important broadleaf tree species groups. The classified map was further utilized to improve ground reference data for mapping species distribution at the stand and landscape scales using multispectral satellite imagery (1.4 m to 10 m). The results indicated an improvement in the overall accuracy of 0.13 (from 0.68 to 0.81) and a MICE metric of 0.14 (from 0.61 to 0.75) using reference samples derived from RPA data. The results of this preliminary study are promising in utilizing RPA for multi-scale mapping of broadleaf tree species effectively.

Scientific researchers are known for their demanding intellectual efforts and are particularly susceptible to mental health disorders. Urban green spaces (UGSs) are beneficial for people’s mental health, yet there is limited research on what types of UGSs and activities are effective for improving researchers’ mental health. Here, we investigate if researchers’ mental health can be enhanced by visiting affiliated green spaces, pocket parks, comprehensive parks, and coastal beaches. The research hypothesis was that the natural and social pathways of recreational ecosystem services jointly reduce researchers’ mental stress and attentional fatigue. The results suggest that nature contact, nature connectedness, and social interaction directly or indirectly have positive impacts on scientific researchers’ mental health. Both nature connectedness and social interaction serve as crucial mediating variables between nature contact and mental health. In terms of direct impact, high-frequency recreational activities in small UGSs with high accessibility, markedly relieve stress, whereas visit frequency in large UGSs with low accessibility has a limited effect on long-term mental stress relief. In terms of indirect impact, small UGSs alleviate attentional fatigue through the mediation of nature connectedness, while large UGSs mainly mitigate mental stress and attentional fatigue through the mediation of social interaction. This study provides a natural-social integrated perspective on recreational ecosystem services to enhance the well-being of scientific researchers. We provide recommendations for researchers to mitigate work-related stress and fatigue and to offer information useful for decision-making support to create a healthy environment for scientific researchers.

Abstract The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity—an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research.

The COVID-19 pandemic caused drastic changes in human activities and nighttime light (NTL) at various scales, providing a unique opportunity for exploring the pattern of the extreme responses of human community. This study used daily NTL data to examine the spatial variations and temporal dynamics of human activities under the influence of COVID-19, taking Chinese mainland as the study area. The results suggest that the change in the intensity of NTL is not correlated to the number of confirmed cases, but reflects the changes in human activities and the intensity of epidemic prevention and control measures within a region. During the outbreak period, the major provincial capitals and urban agglomerations were affected by COVID-19 more than smaller cities. During the recovery, different regions showed different recovery processes. The cities in West and Northeast China recovered steadily while the recovery in coastal cities showed relatively greater fluctuations due to an increase in imported cases. Wuhan, the most seriously affected city in China, did not recover until the end of March. Nevertheless, as of 31 March, the overall NTL across China had recovered to an 89.5% level of the same period in the previous year. The high consistency between the big data of travel intensity and NTL further proved the validity of the results of this study. These findings imply that daily NTL data are effective for rapidly monitoring the dynamic changes in human activities, and can help evaluate the effects of control measures on human activities during major public health events.

Accuracy assessment is essential in all image classification-related fields, ranging from molecular imaging to earth observation. However, existing accuracy metrics are too sensitive to class imbalance or lack explicit interpretations for assessing classification performance. Consequently, their scores may be misleading when they are applied to compare classification algorithms that address different image data sources. These limitations jeopardize the widespread application of deep learning classification methods for classifying different image types. We introduce the metrics of image classification efficacy from medicine and pharmacology to overcome the limitations of accuracy metrics. We include a baseline classification to derive the metrics of image classification efficacy and apply real-world and hypothetical examples to further examine their usefulness. Image classification efficacies can be applied at the map and class levels and for binary and multiclass classifications. The interpretability and comparability of image classification efficacies facilitate reliable classification method evaluation across data sources. We detail the procedures of classification efficacy assessment for image classification researchers and classifier users.

Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

Precise tree inventory plays a critical role in sustainable forest planting, restoration, and management. LiDAR-based individual tree detection algorithms often focus on finding individual treetops to discern tree positions. However, deliquescent tree forms (broad, flattened crowns) in deciduous forests can make these algorithms ineffective. In this study, we propose a stepwise tree detection approach, by first identifying individual trees using horizontal point density and then analyzing their vertical structure profiles. We first project LiDAR data onto a 2D horizontal plane and apply mean shift clustering to generate candidate tree clusters. Next, we apply a series of structure analyses on the vertical phase, to overcome local variations in crown size and tree density. This study demonstrates that the horizontal point density of LiDAR data provides critical information to locate and isolate individual trees in temperate hardwood plantations with varied densities, while vertical structure profiles can identify spreading branches and reconstruct deliquescent crowns. One challenge of applying mean shift clustering is training a dynamic search kernel to identify trees of different sizes, which usually requires a large number of field measurements. The stepwise approach proposed in this study demonstrated robustness when using a constant kernel in clustering, making it an efficient tool for large-scale analysis. This stepwise approach was designed for quantifying temperate hardwood plantation inventories using relatively low-density airborne LiDAR, and it has potential applications for monitoring large-scale plantation forests. Further research is needed to adapt this method to natural stands with diverse tree ages and structures.

The Karakoram-Himalayan (KH) region comprises high mountains across seven countries (i.e., China, Afghanistan, Pakistan, India, Nepal, Bhutan, and Myanmar) with a variety of management regimes and priorities and capacities for conservation. Currently, there is no comprehensive framework for assessing cooperation on protected areas (PAs) in the KH region. Such a framework is essential to guide managers and policymakers in the formulation of consistent management plans and strategies for regional sustainable development. In this study, an indicator framework for assessing cooperation on PAs was developed based on four factors: the natural environment, the human environment, transport accessibility, and the diplomatic environment. The assessment involved 49 cross-border national parks and nature reserves in this region. Furthermore, the indicators were analyzed by using the analytic hierarchy process, resulting in eight cross-border groups. The analysis reveals that the region has a good foundation for cooperative cross-border conservation in general. Among the eight groups, the Karakoram-Pamir, Kangchenjunga, and Everest groups exhibit greater potential for closer cooperation, whereas the Kashmir group has a lower basis for cooperation. The results suggest that the establishment of transboundary conservation areas (TBCAs) and academic and scientific development can provide opportunities to enhance our understanding of the KH region and to collaborate across national boundaries. This case study explains that the indicator framework can be used as a reference for cooperative cross-border conservation in other regions worldwide.

Mapping surface water distribution and its dynamics over various environments with robust methods is essential for managing water resources and supporting water-related policy design. Thresholding Single Water Index image (TSWI) with threshold is a common way of using water index (WI) for mapping water for it is easy to use and could obtain acceptable accuracies in many applications. As more and more WIs are available and each has its distinct merits, the real-world application of TSWI, however, often face two practical concerns: (1) selection of an appropriate WI and (2) determination of an appropriate threshold for a given WI. These two issues are problematic for many users who rely either on trial-and-error procedures that are time-consuming or on their personal preferences that are somewhat subjective. To better deal with these two practical concerns, an alternative way of using WIs is suggested here by transforming the current paradigm into a simple but robust ensemble approach called Collaborative Decision-making with Water Indices (CDWI). A total of 145 subsite images (900 × 900 m) from 22 Landsat-8 OLI scenes that covering various water-land environments around the world were used to assess the performance of TSWI and the CDWI. Five benchmark WIs were adopted in five TSWI methods and CDWI method: Normalized Difference Water Index (NDWI), the Modified NDWI (MNDWI), the Automated Water Extraction Indices without considering (AWEI0) and with considering (AWEI1) shadows, and the state-of-the-art 2015 water index (WI2015). Two aspects of performance were analyzed: comparing their accuracies (indicated by both F1-scores and Youden’s Index) over various environments and comparing their accuracy sensitivities to threshold. The results demonstrate that CDWI produced higher accuracies than the other five TSWI methods for most application cases. Particularly, more cases (indicated by percentage) produced higher F1-scores by CDWI than the other five TSWI methods, i.e. 67% (CDWI) vs. 15% (TSWINDWI), 54% (CDWI) vs. 22% (TSWIMNDWI), 42% (CDWI) vs. 12% (TSWIAWEI0), 57% (CDWI) vs. 17% (TSWIAWEI1), and 34% (CDWI) vs. 12% (TSWIWI2015). Moreover, the F1-score of the CDWI is less sensitive to the change of thresholds compared with that of the five TSWI methods. These important benefits of CDWI make it a robust approach for mapping water. The uncertainty of CDWI method was thoroughly discussed and a general guidance (or look-up-table) for determining parameters of CDWI method was also suggested. The underlying framework of CDWI could be readily generalizable and applicable to other satellite images, such as Landsat TM/ETM+, MODIS, and Sentinel-2 images.

Compact development is one of the most effective solutions for sustainable urbanization under the rapid growth of the urban population. Great efforts have been made to measure urban physical compactness while limited attention has been paid to functional zoning of urban areas. Here, we introduce a novel index, called the functional compactness index (FCI), to quantify urban functional compactness through the integration of geospatial data sources, including Points of Interest (POIs) data, Road Network of OpenStreetMap (RNO) data, and National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data. The FCI does not require the analysis of the grid scale and thus, is technically simpler than conventional compactness index (CI). We examined the effectiveness of FCI on estimating urban compactness under four land use scenarios and in four Chinese cities. The results suggest that: (1) the FCI can comprehensively reflect the intensity of human activity, the differentiation between residential zones and other functional zones, and the mixing degree of different functional zones; (2) the FCI is not affected by the service radius of residential zones; (3) the FCI can reflect the overall and local-scale functional compactness of a city; and (4) the FCI can be used to effectively compare spatial characteristics of functional compactness among different cities. In conclusion, the FCI considers the rationality of urban functional layout, which not only is helpful for urban planning, but also enriches the quantitative methods of urban compactness evaluation.

Summary: The difficulty of obtaining reliable individual identification of animals has limited researcher's ability to obtain quantitative data to address important ecological, behavioral, and conservation questions. Traditional marking methods placed animals at undue risk. Machine learning approaches for identifying species through analysis of animal images has been proved to be successful. But for many questions, there needs a tool to identify not only species but also individuals. Here, we introduce a system developed specifically for automated face detection and individual identification with deep learning methods using both videos and still-framed images that can be reliably used for multiple species. The system was trained and tested with a dataset containing 102,399 images of 1,040 individuals across 41 primate species whose individual identity was known and 6,562 images of 91 individuals across four carnivore species. For primates, the system correctly identified individuals 94.1% of the time and could process 31 facial images per second.

With the increased availability of unmanned aerial systems (UAS) imagery, digitalized forest inventory has gained prominence in recent years. This paper presents a methodology for automated measurement of tree height and crown area in two broadleaf tree plantations of different species and ages using two different UAS platforms. Using structure from motion (SfM), we generated canopy height models (CHMs) for each broadleaf plantation in Indiana, USA. From the CHMs, we calculated individual tree parameters automatically through an open-source web tool developed using the Shiny R package and assessed the accuracy against field measurements. Our analysis shows higher tree measurement accuracy with the datasets derived from multi-rotor platform (M600) than with the fixed wing platform (Bramor). The results show that our automated method could identify individual trees (F-score > 90%) and tree biometrics (root mean square error < 1.2 m for height and 2 for the crown area) with reasonably good accuracy. Moreover, our automated tool can efficiently calculate tree-level biometric estimations for 4600 trees within 30 min based on a CHM from UAS-SfM derived images. This automated UAS imagery approach for tree-level forest measurements will be beneficial to landowners and forest managers by streamlining their broadleaf forest measurement and monitoring effort.

This paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in natural disturbance-based management (NDBM), which uses treatments such as harvest and prescribed burns toward the removal of vegetation fuel loads. We developed a protocol and applied it to timber harvest and prescribed burn events. Geographic image-based analysis (GEOBIA) was used for the classification of UAS orthomosaics. The land cover classes included (1) bare ground, (2) litter, (3) green vegetation, and (4) burned vegetation for the prairie burn site, and (1) mature canopy, (2) understory vegetation, and (3) bare ground for the timber harvest site. Sample datasets for both kinds of disturbances were used to train a support vector machine (SVM) classifier algorithm, which produced four land cover classifications for each site. Statistical analysis (a two-tailed t-test) indicated there was no significant difference in image classification efficacies between the two disturbance types. This research provides a framework to use UASs to assess land cover, which is valuable for supporting effective land management practices and ensuring the sustainability of land practices along with other planned disturbances, such as construction and mining.

Forest canopy height model (CHM) is useful for analyzing forest stocking and its spatiotemporal variations. However, high-resolution CHM with regional coverage is commonly unavailable due to the high cost of LiDAR data acquisition and computational cost associated with data processing. We present a CHM generation method using U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) LiDAR data for tree height measurement capabilities for entire state of Indiana, USA. The accuracy of height measurement was investigated in relation to LiDAR point density, inventory height, and the timing of data collection. A simple data exploratory analysis (DEA) was conducted to identify problematic input data. Our CHM model has high accuracy compared to field-based height measurement (R2 = 0.85) on plots with relatively accurate GPS locations. Our study provides an easy-to-follow workflow for 3DEP LiDAR based CHM generation in a parallel processing environment for a large geographic area. In addition, the resulting CHM can serve as critical baseline information for monitoring and management decisions, as well as the calculation of other key forest metrics such as biomass and carbon storage.

The timely and accurate recognition of damage to buildings after destructive disasters is one of the most important post-event responses. Due to the complex and dangerous situations in affected areas, field surveys of post-disaster conditions are not always feasible. The use of satellite imagery for disaster assessment can overcome this problem. However, the textural and contextual features of post-event satellite images vary with disaster types, which makes it difficult to use models that have been developed for a specific disaster type to detect damaged buildings following other types of disasters. Therefore, it is hard to use a single model to effectively and automatically recognize post-disaster building damage for a broad range of disaster types. Therefore, in this paper, we introduce a building damage detection network (BDD-Net) composed of a novel end-to-end remote sensing pixel-classification deep convolutional neural network. BDD-Net was developed to automatically classify every pixel of a post-disaster image into one of non-damaged building, damaged building, or background classes. Pre- and post-disaster images were provided as input for the network to increase semantic information, and a hybrid loss function that combines dice loss and focal loss was used to optimize the network. Publicly available data were utilized to train and test the model, which makes the presented method readily repeatable and comparable. The protocol was tested on images for five disaster types, namely flood, earthquake, volcanic eruption, hurricane, and wildfire. The results show that the proposed method is consistently effective for recognizing buildings damaged by different disasters and in different areas.

The downstream reach of the Three Gorges Dam (TGD) along the Yangtze River (1560 km) hosts numerous mid-channel bars (MCBs). MCBs dynamics are crucial to the river’s hydrological processes and local ecological function. However, a systematic understanding of such dynamics and their linkage to TGD remains largely unknown. Using Landsat-image-extracted MCBs and several spatial-temporal analysis methods, this study presents a comprehensive understanding of MCB dynamics in terms of number, area, and shape, over downstream of TGD during the period 1985−2018. On average, a total of 140 MCBs were detected and grouped into four types representing small (2), middle (2 km2 − 7 km2), large (7 km2 − 33 km2) and extra-large size (>33 km2) MCBs, respectively. MCBs number decreased after TGD closure but most of these happened in the lower reach. The area of total MCBs experienced an increasing trend (2.77 km2/yr, p-value < 0.01) over the last three decades. The extra-large MCBs gained the largest area increasing rate than the other sizes of MCBs. Small MCBs tended to become relatively round, whereas the others became elongate in shape after TGD operation. Impacts of TGD operation generally diminished in the longitudinal direction from TGD to Hankou and from TGD to Jiujiang for shape and area dynamics, respectively. The quantified longitudinal and temporal dynamics of MCBs across the entire Yangtze River downstream of TGD provides a crucial monitoring basis for continuous investigation of the changing mechanisms affecting the morphology of the Yangtze River system.

There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods.

Information about urban built-up areas is important for urban planning and management. However, obtaining accurate information about urban built-up areas is a challenge. This study developed a general-purpose built-up area intelligent classification (BAIC) system that supports various types of data and classifiers. All of the steps in the BAIC were implemented using Python modules including Numpy, Pandas, matplotlib, and scikit-learn. We used the BAIC to conduct a classification experiment that involved seven types of input data; namely, Point of Interest (POI), Road Network (RN), nighttime light (NTL), a combination of POI and RN data (POI_RN), a combination of POI and NTL data (POI_NTL), a combination of RN and NTL data (RN_NTL), and a combination of POI, RN, and NTL data (POI_RN_NTL), and five classifiers, namely, Logistic Regression (LR), Decision Tree (DT), Random Forests (RF), Gradient Boosted Decision Trees (GBDT), and AdaBoost. The results show the following: (1) among the 35 combinations of the five classifiers and seven types of input data, the overall accuracy (OA) ranged from 76 to 89%, F1 values ranged from 0.73 to 0.86, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.83 to 0.95. The largest F1 value and OA were obtained using the POI_RN_NTL data and AdaBoost, while the largest AUC was obtained using POI_RN_NTL and POI_NTL data against AdaBoost, LR, and RF; and (2) the advantages of the BAIC include its support for multi-source input data, its objective accuracy assessment, and its robust classifiers. The BAIC can quickly and efficiently realize the automatic classification of urban built-up areas at a reasonably low cost and can be readily applied to other urban areas in the world where any kind of POI, RN, or NTL data coverage is available. The results of this study are expected to provide timely and effective reference information for urban planning and urban management departments, and could also potentially be used to develop large-scale maps of urban built-up areas in the future.

Rapid assessment of natural disasters is essential for disaster analysis and spatially explicit strategic decisions of post-disaster reconstruction but requires timely available data. The recent daily data of the National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provide new opportunities to detect and evaluate natural disasters. Here, we introduce an application of NPP-VIIRS DNB daily data for rapidly assessing the damage of a severe typhoon that struck the urban agglomerations along the western Taiwan Straits in China. Our research explored the methods of rapid identification and extraction of the areas based on changes in nighttime light (NTL) after the typhoon disaster by using a statistical radiation-normalization method. We analyzed the correlations of NTL image derivatives with human population, population density, and gross domestic product (GDP). The strong correlations were found between NTL image light density and population density (R2 = 0.83) and between the total nighttime light intensity and GDP (R2 = 0.96) at the prefecture level. In addition, we examined the interrelationships between changes in NTL images and the areas affected by the typhoon and proposed a method to predict the affected population. Finally, the affected area and the affected population in the study area could be rapidly retrieved based on the proposed remote sensing method. The overall accuracy was 83.2% for the detection of the affected population after disaster and the recovery rate of the affected area was 86.9% in the third week after the typhoon. This research demonstrates that the NTL image-based change detection method is simple and effective, and further explains that the NPP-VIIRS DNB daily data are useful for rapidly assessing affected areas and affected populations after typhoon disasters, and for timely quantifying the degree of recovery at a large spatial scale.