Estimates of forest stocking density per hectare (NHa) are important in characterising ecological conditions and assessing changes in forest dynamics after disturbances due to pyrogenic, anthropogenic and biotic factors. We use Unmanned Aircraft Systems (UAS) LiDAR with mean point density of 1485 points m−2 across 39 flight sites to develop a bottom-up approach for individual tree and crown delineation

The study presented in this paper introduced a new fully automatic three-dimensional building reconstruction method that can generate first level of detail (LoD 1) building models from multi-view aerial images without any assistance from other data. The accuracy and completeness of our reconstructed models have approached that of manually delineated models to a large extent. The presented method consists

Terrestrial laser scanners (TLS) record a large number of points within a short time. Temporal correlations between observations are unavoidable but often neglected in stochastic modelling. The main consequences are an overestimated precision of the point clouds and potential wrong test decisions when used for deformation analysis with rigorous statistical procedures. Regarding physical considerations

With the launch of Sentinel-2 new opportunities for large scale urban mapping arise. However, the spectral information embedded in the Sentinel-2 20 m spatial resolution bands cannot yet be fully explored in heterogeneous urban landscapes. The 20 m image pixels are often composed of different land covers, resulting in a difficult to interpret mixed pixel spectrum. Here, we propose an unmixing-based

Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images. Selecting an appropriate algorithm that maintains the spectral and spatial information content of input images is a challenging task. This review paper investigates a wide range of algorithms, including 41 methods. For this purpose, the methods were categorized as Component Substitution (CS-based), Multi-Resolution

Motivated by the need to identify erroneous disparity estimates, various methods for the estimation of aleatoric uncertainty in the context of dense stereo matching have been presented in recent years. Especially, the introduction of deep learning based methods and the accompanying significant improvement in accuracy have greatly increased the popularity of this field. Despite this remarkable development

Line segment matching in two or multiple views is helpful to 3D reconstruction and pattern recognition. To fully utilize the geometry constraint of different features for line segment matching, a novel graph-based algorithm denoted as GLSM (Graph-based Line Segment Matching) is proposed in this paper, which includes: (1) the employment of three geometry types, i.e., homography, epipolar, and trifocal

In temperate forests, autumn leaf phenology signals the end of leaf growing season and shows large variability across tree-crowns, which importantly mediates photosynthetic seasonality, hydrological regulation, and nutrient cycling of forest ecosystems. However, critical challenges remain with the monitoring of autumn leaf phenology at the tree-crown scale due to the lack of spatially explicit information

Due to severe speckle noise in synthetic aperture radar (SAR) images and the large nonlinear intensity differences between SAR and optical images, the registration of SAR and optical images is a challenging problem that remains to be solved. In this paper, an improved nonlinear scale-invariant feature transform (SIFT)-framework-based algorithm that combines spatial feature detection with local frequency-domain

The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods for the mapping of forest tree species do not exploit the respective, rich spatial information. Here, we assessed the potential of convolutional neural networks (CNNs) and very high-resolution RGB imagery from UAVs for

The utility of sheer volumes of very high spatial resolution (VHSR) commercial imagery in mapping the Arctic region is new and actively evolving. Commercial satellite sensors typically record image data in low-resolution multispectral (MS) and high-resolution panchromatic (PAN) mode. Spatial resolution is needed to accurately describe feature shapes and textural patterns, such as ice-wedge polygons

Lakes are essential components of the water cycle and ecosystems. Therefore, the ecology and water security of lakes is of great concern. However, on the Tibetan Plateau (TP), which is known as the Asian water tower, knowledge of lake water quality is in its infancy. In this study, we developed a Moderate Resolution Imaging Spectroradiometer (MODIS)-based Secchi disk depth (Zsd) retrieval model and

In this paper, we establish a workflow for estimation of built-up density and height based on multispectral Sentinel-2 data. To do so, we render the estimation of built-up density and height as a supervised learning problem. Given the rational level of measurement of those two target variables, the regression estimation problem is regarded as finding the mapping between an incoming vector, i.e., ubiquitously

The modeling of underwater light field is essential for the understanding of biogeochemical processes, such as photosynthesis, carbon fluxes, and sediment transports in inland waters. Water-column light attenuation can be quantified by the diffuse attenuation coefficient of the downwelling irradiance (Kd) using semi-analytical algorithms (SAA). However, the accuracy of these algorithms is currently

Edge detection in SAR images is a difficult task due to the strong multiplicative noise. Many researches have been dedicated to edge detection in SAR images but very few try to address the most challenging 1-look situations. Motivated by the success of CNNs for the analysis of natural images, we develop a CNN edge detector for 1-look SAR images. We propose to simulate a SAR dataset using the optical

The availability of high spatial resolution synthetic aperture radar (SAR) sensors with a wide range of acquisition modes has increased greatly over the past decade and contributed significantly to the study of wetland ecosystems. However, the relative influence of acquisition configurations (i.e. band frequency, polarization mode, number of acquisition dates) in wetland analysis remains poorly explored

The identification and annotation of buildings has long been a tedious and expensive part of high-precision vector map production. The deep learning techniques such as fully convolution network (FCN) have largely promoted the accuracy of automatic building segmentation from remote sensing images. However, compared with the deep-learning-based building segmentation methods that greatly benefit from

Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed

In multiple image mosaicking, color inconsistency is a common and severe problem that degrades the quality of composite images. To avoid the appearance of visible seams, we need to optimize the color consistency before mosaicking multiple images. To facilitate the global optimization framework, existing approaches mainly use less flexible global models, e.g., the linear or gamma model, to eliminate

Parsing very high resolution (VHR) urban scene images into regions with semantic meaning, e.g. buildings and cars, is a fundamental task in urban scene understanding. However, due to the huge quantity of details contained in an image and the large variations of objects in scale and appearance, the existing semantic segmentation methods often break one object into pieces, or confuse adjacent objects

The reconstruction of buildings using inhomogeneous and unstructured point clouds is a challenging task for photogrammetry and computer vision research communities. A new approach for 3D building surface modeling, based on closed constraints, is proposed. First, a region growth algorithm is applied to fit the input point clouds by a set of candidate planes. Then, additional candidate planes are generated

Existing techniques of 3-D reconstruction of buildings from SAR images are mostly based on multibaseline SAR interferometry, such as PSI and SAR tomography (TomoSAR). However, these techniques require tens of images for a reliable reconstruction, which limits the application in various scenarios, such as emergency response. Therefore, alternatives that use a single SAR image and the building footprints

With increasing urbanization, in recent years there has been a growing interest and need in monitoring and analyzing urban flood events. Social media, as a new data source, can provide real-time information for flood monitoring. The social media posts with locations are often referred to as Volunteered Geographic Information (VGI), which can reveal the spatial pattern of such events. Since more images

Object-based image analysis (OBIA) has been widely used to classify high spatial resolution (HSR) imagery. In a traditional OBIA, object-level statistical summaries such as mean values are usually used for classification. This implies that the spectral values within objects follow a Gaussian distribution. However, the pixel values in an object do not necessarily conform to a Gaussian distribution because

To better understand scene images in the field of remote sensing, multi-label annotation of scene images is necessary. Moreover, to enhance the performance of deep learning models for dealing with semantic scene understanding tasks, it is vital to train them on large-scale annotated data. However, most existing datasets are annotated by a single label, which cannot describe the complex remote sensing

Wild animal surveys play a critical role in wild animal conservation and ecosystem management. Unmanned aircraft systems (UASs), with advantages in safety, convenience and inexpensiveness, have been increasingly used in wild animal surveys. However, manually reviewing wild animals from thousands of images generated by UASs is tedious and inefficient. To support wild animal detection in UAS images,

Soil water repellency (SWR) is a natural phenomenon occurring in soils throughout the world, which impacts upon ecosystem services at multiple temporal and spatial scales (nano to ecosystem scale). In pastures, the development of SWR is primarily determined by the cycling of hydrophobic materials at the soil surface, and is controlled by climate, soil and water management, and soil properties. The

Semantic segmentation is a fundamental task in understanding urban mobile laser scanning (MLS) point clouds. Recently, deep learning-based methods have become prominent for semantic segmentation of MLS point clouds, and many recent works have achieved state-of-the-art performance on open benchmarks. However, due to differences of objects across different scenes such as different height of buildings

Oriented object detection in remote sensing images is an active yet challenging task. State-of-the-art detectors adopt oriented bounding box as a basic representation of an object. The design of them always multiplies the number of anchor boxes with arbitrary angles. Coupled with the rotated Non-Maximum-Suppression (NMS) algorithm, their computational complexities and design difficulties are dramatically

We introduce a multiscale approach to population estimation of maldanid polychaetes on intertidal sandflats. Animals that live in marine sediments play important roles in ecosystem processes and are frequently the target of environmental monitoring but are notoriously time consuming to sample. Our goal was to enable the mapping of patterns of abundance of estuary lifeforms over long tracks at spatial

Tree height is one of the most important tree attributes in forest inventory. However, using conventional field methods to measure tree height is a laborious and time-consuming process. Despite the great interest in the past to facilitate tree height measurements, new, upcoming solutions are not yet thoroughly investigated. In this study, we investigated the applicability of different close-range remote

Accurate and regularly updated information on plastic distribution in urban areas is of great significance for understanding the potential impact of plastic materials on human health and the environment. Remote sensing provides abundant data to identify and map plastics in the natural environment, such as marine plastic debris, and in a heterogeneous urban environment. In existing studies, airborne

Hyperspectral anomaly detection methods based on representation model have attracted much attention in recent years. In the method, a background dictionary is used to represent each pixel linearly, and the residual is taken as the abnormal level of the pixel. Therefore, building a functional model with strong representation ability and a matching background dictionary is an important factor for success

SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. While for both data sources images are usually provided in a georeferenced manner, the geo-localization of optical images is often inaccurate due to

In this research a machine learning framework was developed for cotton yield estimation using multi-temporal remote sensing data collected from unmanned aircraft system (UAS). The proposed machine learning model was based on an artificial neural network (ANN) and used three types of crop features derived from UAS data to predict the yield, namely; multi-temporal features including canopy cover, canopy

In order to use the airborne LiDAR intensity in conjunction with the height-derived information for forest modeling and classification purposes, radiometric correction is deemed to be a critical pre-processing requirement. In this study, we implemented a LiDAR scan line correction (LSLC) and an overlap-driven intensity correction (OIC) to remove the stripe artifacts that appeared within the individual

Front-line remote sensing tools, coupled with machine learning (ML), have a significant role in crop monitoring and disease surveillance. Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise, timely, and cost-effective information at different spatial, temporal, and spectral resolutions. To our knowledge, most disease surveillance

Global or quasi-global digital elevation model (DEM) datasets provide three-dimensional information on terrain surface, and they have been extremely useful in geoscience research and applications. However, the wide application of DEMs is constrained by differences in the means of observation and processing, and in the resolution of global public DEM datasets. An adaptive regularization variation model

Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using

One main challenge in detecting built-up land cover changes using synthetic aperture radar (SAR) instruments is that complicated backscattering behaviours and the superimposition of speckles on rich textures cause a large number of false alarms. Using trajectory-based analyses from time-series SAR imagery can mitigate false alarms since the temporal variability in backscattering during construction

Supervised training of a deep neural network for semantic segmentation of point clouds requires a large amount of labelled data. Nowadays, it is easy to acquire a huge number of points with high density in large-scale areas using current LiDAR and photogrammetric techniques. However it is extremely time-consuming to manually label point clouds for model training. In this paper, we propose an active

Physically-based methods in remote sensing provide benefits over statistical approaches in monitoring biophysical characteristics of vegetation. However, physically-based models still demand large computational resources and often require rather detailed informative priors on various aspects of vegetation and atmospheric status. Spectral invariants and photon recollision probability theories provide

The development of ground-based sampling strategies is vital for validating medium or coarse-resolution satellite-derived land surface temperature (LST) products. Conventional LST sampling at the satellite pixel scale has been limited to the homogeneous surfaces. In this study, an optimal sampling strategy called spatial and diurnal temperature cycle-constrained sampling (SDCS) is proposed to extend

Statistical downscaling of land surface temperature (SDLST) algorithms with diverse scaling factors and regression models have been used to produce high spatial resolution LSTs based on Landsat-8 LST. However, the optimal choice of scaling factors and regression models and their associated combinations over various land surfaces, especially from a global perspective, remain unclear and even controversial

Wetness is one of the important physical parameters of snowpack. Its spatial and temporal changes play a key role in snowmelt runoff forecast, regional climate change, and agricultural irrigation. In this study, we proposed a new method to retrieve snow wetness from full-polarimetric synthetic aperture radar (SAR) data. First, the dominant scattering components, i.e. surface and volume scattering,

This paper analyzes inconsistencies in remotely sensed land surface phenology (LSP) reported by AVHRR GIMMS3g and MODIS datasets across land cover types of the Northern Hemisphere. We extracted the start of the growing season (SOS) and the end of the growing season (EOS) from the AVHRR GIMMS3g (1982–2015) and MODIS (2000–2015) datasets, and weekly GPP mean data from the Fluxnet2015 dataset to compare

Automatic road extraction from high-resolution remote sensing imagery has various applications like urban planning and automatic navigation. Existing methods for automatic road extraction however, focus on regional accuracy but not on the boundary quality; and most of these road extraction methods yield discontinuous results due to noise and occlusions. To address these two problems, a Boundary and

There is a limited understanding of how forest structure affects the performance of methods based on terrestrial laser scanning (TLS) in characterizing trees and forest environments. We aim to improve this understanding by studying how different forest management activities that shape tree size distributions affect the TLS-based forest characterization accuracy in managed Scots pine (Pinus sylvestris

The emerging single-photon laser scanning has made technological breakthrough in the collection of airborne laser scanning data. In principle, single-photon systems require only one detected photon for successful ranging. Further, the point density on the ground can be 10–100 times higher for single-photon lidar data than that obtained with multi-photon systems at the same flight altitude. This has

The point spread function (PSF) is ubiquitous in remote sensing. This paper investigated the effect of the PSF on the downscaling of continua. Geostatistical approaches were adopted to incorporate explicitly, and reduce the influence of, the PSF effect in downscaling. Two general cases were considered: univariate and multivariate. In the univariate case, the input coarse spatial resolution image is

The availability of Landsat 8 and Sentinel-2 has led to a steady increase in both temporal and spatial resolution of satellite data, offering new opportunities for large-scale crop condition monitoring and crop yield mapping. This study investigated the potential of using Landsat 8 and Sentinel-2 data from the harmonized Landsat 8 and Sentinel-2 (HLS) products for crop biomass estimation for six crops

Spring frost is one of the major weather-related threats to winter wheat. The damage to winter wheat caused by spring frost is aggravated by the increase in extreme weather events and the advance of spring phenology driven by a warming climate. Until recently, studies of frost damage were primarily based on controlled field experiments and crop model simulations, which cannot accurately represent the

Many of the point cloud processing techniques have their origin in image processing. But mathematical morphology, despite being one of the most used image processing techniques, has not yet been clearly adapted to point clouds. The aim of this work is to design the basic operations of mathematical morphology applicable to 3D point cloud data, without the need to transform point clouds to 2D or 3D images

Stomata is an important channel for the exchange of water and gas between crops and the external environment. Stomatal behavior can shed light on how environmental stressors affect crop growth. Traditional stomata investigation methods involve experimental laboratory measurements of specific plants using stoma-related instruments or a field plot. However, developing a new remote sensing canopy resistance