Today, both point cloud and imagery datasets processed for mapping aims. The precise fusion of both datasets is a major issue that leads to the fine registration problem. This article proposes a fine registration method based on a novel concept of tie plane. The assumption of our solution is that the laser scanner point cloud is much more accurate than the image interior and exterior geometric accuracy

La présence de cibles hétérogènes, comme la glace, reste un défi majeur pour l’utilisation des données altimétriques au-dessus des plans d’eau continentaux. Les satellites Jason-2 et SARAL/Altika utilisent des algorithmes de retraitement conçus pour traiter les formes d’onde non continentales afin d’obtenir des estimations améliorées. Dans cette étude, nous analysons le potentiel des produits dérivés

Due to the fragmented compositional structure of urban scenes, many pixels are mixtures of multiple materials even in high spatial resolution airborne hyperspectral data. In the past ten years, sparse regression based spectral unmixing methods have achieved some noticeable results. Recently, Chen et al. proposed a jointly sparse spectral mixture analysis model for urban mapping. Their model has a high

Crop nitrogen (N) needs to be accurately predicted to allow farmers to effectively match the N supply to the crop N demand during crop growth in order to minimize environmental impacts as excess N could seep into the water supplies around the field. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral MicaSense imagery validated with ground hyperspectral measurements to

Wetland vegetation classification often treated the saltmarsh as a single type of land-cover (LCT). Mapping the dynamic and spatially complex coastal zones using optical remote sensing is still challenging. This study firstly analyzed the spectral properties of target objects generated by Landsat 8 (OLI), formulated new spectral indices and then proposes a rule-based approach to mapping five vegetated

Abstract Wildfire is a critical process shaping the structure and composition of forest landscapes of western Canada. Spatially-explicit forest disturbance history and forest structure estimated using remotely-sensed data enables the characterization of burn probability, defined as the susceptibility of landscapes to fire hazard over time. In this research, we leveraged the Landsat archive to determine

Large SAR images usually contain a variety of land-cover types and accordingly complicated change types, which cause great difficulty for accurate change detection. The U-Net is a special fully convolutional neural network that not only can capture multiple features in the image context but also enables precise pixel-by-pixel image classification. Therefore, we explore the U-Net to describe accurately

Hyperspectral Images (HSI) require sufficient labeled samples and a complex classifier to identify an area. Support Vector Machine (SVM) is one of the most competent algorithms in this field. Neural Networks (NN) is another approach used for classification problems, and both have been widely proposed in the literature. The Convolutional Neural Network (CNN) method has also received significant attention

The main objective of this study is to develop a robust soil moisture retrieval approach using multi-sensor remote sensing data. Firstly, the water cloud model was employed to eliminate the vegetation effects on SAR observations over vegetated areas, thus to obtain the bare soil backscatter associated with soil moisture. Then, against the underdetermined system for soil moisture retrieval, the advanced

An accurate forest inventory is crucial for forest monitoring and quantifying forest aboveground biomass (AGB). This study aimed to investigate the feasibility of Terrestrial Laser Scanning (TLS) in forest inventory and AGB estimation in the tropical forest of Malaysia. Individual trees were detected using manual and automatic detection methods. An average tree detection rate of 99.55% and 93.75% were

Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. Recently, an effective approach for spatial-spectral classification has been proposed using Minimum Spanning Forest (MSF) algorithm. Our goal is to improve this approach to the classification of hyperspectral images in urban areas. In the proposed method two spatial/texture

Bud and leaf development are important phenological events and help in defining the growing period of trees. Canopy greenness derived from PhenoCam has been used to investigate leaf phenology. Questions remain on how much the continuous records of canopy greenness represent bud developmental phases, and how growing period boundaries are related to canopy greenness and bud phenology. In this study,

The objective of this study was to estimate forest variables using TanDEM-X interferometric synthetic aperture radar (InSAR) data acquired over the Shastkalate forest of Gorgan in northern Iran. Inventory variables, including diameter at breast height, tree height (Lorey’s mean tree height), basal area and volume, were collected from 112 circular sample plots with a size of 0.1 ha. Interferometric

Identification of forest gaps is a prerequisite for quantification of their size, shape, and dynamics, and for clarification of both complex structural forest species regeneration and understory species diversity. Although airborne LiDAR and digital orthophoto maps (DOM) have been used separately to identify forest gaps, few studies have considered integration of the two data sources for forest gap

In recent decades, spatial feature extraction has greatly improved the performance of hyperspectral image (HSI) classification. This paper presents an HSI classification method based on multiple spatial features extraction and fusion (MSFs-EF). The method consists of five sequential steps. 1- Principal component analysis is applied for HSI dimensionality reduction. 2- A mean curvature filter is used

The ability to expand the use of predictive Airborne Laser Scanning (ALS)-derived Forest Resource Inventory (FRI) models to broader regional scales is crucial for supporting large scale sustainable forest management. This research examined the transferability of ALS-based FRI attributes between two forest estates located in the eastern and western boreal forest regions of Canada. The sites were structurally

Subsurface hydrocarbon occurrences can be detected by clay mineral (CM) alteration at the surface as a consequence of hydrocarbon migration. This study analyzed CM alteration in an oil and gas (O&G) field in the West Tugu field, located in the northwest Java Basin of Indonesia. Landsat 8 OLI data acquired on 25 September 2015 and soil spectral reflectance (SSR) data recorded using analytical spectral

The objective of this research was to determine what technologies could be used to improve current forest fire detection methodology. Although human observation provides a wealth of information on the presence and size of fires, remote sensing technologies can provide increasingly more detailed and accurate information. Thus, the cost efficiency, thermal accuracy, spatial accuracy, range and resolution

This study aims to assess which spectral variables and at which time late blight can be detected over potato crops. Two experiments were done in a walk-in chamber under controlled environments. To determine the time, the reflectance spectra were plotted as a function of the day post inoculation (DPI), then a Principal Component Analysis (PCA) was applied and the Jeffries–Matusita distance was computed

The use of aerial remote sensing platforms such as Unmanned Aircraft Systems (UAS) has been proven as a cost and time effective way to perform tasks related to precision agriculture and decision making. Two machine learning (ML) algorithms have been implemented on UAS blue and red band imagery to delineate field areas and extents of various bare soil fields: the Random Forest non-parametric supervised

An accurate forest inventory is crucial for forest monitoring and quantifying forest aboveground biomass (AGB). This study aimed to investigate the feasibility of Terrestrial Laser Scanning (TLS) in forest inventory and AGB estimation in the tropical forest of Malaysia. Individual trees were detected using manual and automatic detection methods. An average tree detection rate of 99.55% and 93.75% were

Burn severity maps based on remotely sensed reflectance data provide a useful way for land managers and researchers to represent and compare spatial variation in fire effects among wildfires and prescribed fires. A need exists for an objective and rigorous selection approach that ensures the best possible spatial predictions of burn severity. The aim of this study was to present and test a methodology

Accurate estimation of biomass and Leaf Area Index (LAI) requires appropriate models and predictor variables. These biophysical parameters are indicative of crop productivity, and thus, are of interest in applications such as crop yield forecasting and precision farming. This study evaluated the potential of leveraging vegetation indices derived from multi-temporal RapidEye data using a machine learning

Accurate and timely monitoring of urban land-use and land-cover (LULC) change is useful for understanding the various impacts of human activity on the urban environment. In order to demonstrate the advantage of time series imaging for urban LULC change detection, we selected time series Landsat images over a two-year period to detect inter-annual changes. A time series trajectory for each pixel was

Despite their natural and societal importance, wetlands are becoming increasingly threatened. The goal of this study is to investigate the potential of polarimetric synthetic aperture radar (SAR) for monitoring one important vegetation constituent of wetlands: Typha. An idealized cylindrical scattering model is developed to portray double bounce microwave scattering from Typha stalks. Then a thin cylinder

Efficient denoising is of great significance to unmixing hyperspectral images. In the present study, a fast unmixing method for noisy hyperspectral images based on the combination of vertex component analysis and singular spectrum analysis is proposed. First, the noisy endmember spectra are extracted by using the vertex component analysis algorithm. Then the singular spectrum analysis is used to denoise

Detailed information on the spatial distribution of wetlands is crucial for sustainable management and resource assessment. Furthermore, regularly updated wetland inventories are of particular importance given that wetlands comprise a dynamic, rather than permanent, land condition. Accordingly, satellite-derived wetland maps are greatly beneficial, as they capture a synoptic and multi-temporal view

This study presents a modified semi-empirical radar scattering model for weathered rough rock surfaces. Weathered rocks generally have dry surfaces except for a few hours after heavy rain due to their rapid drainage compared to bare soils. We find that the dielectric properties of the rocks themselves and the moisture content of a marginal amount of soil patches in and around the rock surfaces have

The spatial resolution of Chinese Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) image, a pixel size of 500 m at azimuth/flight direction for Global Observation (GLO) and 100 m for Wide ScanSAR (WSC) modes, is relatively coarse, and waves shorter than 100 m are undetectable. In this study, SAR images collected under typhoon conditions by the GF-3 mission are exploited for wave retrieval purposes. In

This study attempts to develop an effective algorithm to directly derive near surface air temperature from EOS/MODIS data. From a theoretical viewpoint, the split-window algorithm for retrieving near surface air temperature was developed based on the radiative transfer equation, which includes the calculation of atmospheric thermal radiance, the linearization of Planck functions, the transformation

Over fertilization has resulted in serious soil compaction and acidification, as well as nonpoint pollution. A strategy for variable fertilization according to Site-Specific Management Zones (SSMZ) is urgently needed. We developed an object-oriented method using image segmentation for SSMZ delineation for a farming cooperative founded in 2013 in northeastern China. The method is based on Normalized

Large, e.g., provincial or national, scale near-real-time surface water monitoring is an ambitious task, which can be accomplished by using Synthetic Aperture Radar (SAR) satellite data. SAR has demonstrated the ability to distinguish water and land, but there are many common errors of commission and omission that arise due to the side-looking nature of SAR and due to some landcover types with similar

A field study was conducted to estimate the regional wheat yield by integration of remotely sensed soil moisture index into CERES-Wheat model. The calibration and evaluation of model was performed using experimental data and then applied on the area of Faisalabad district for yield estimation. Area of Faisalabad district was divided into 7929 cells for independent simulations. The weather data of the

Because of the low reflection value of the shadow regions in hyperspectral image (HSI), these regions were deleted directly or ignored in target detection or classification. There are some studies on improving the reflectivity of shadow regions, but it is still difficult to determine the actual substances contained in shadow regions. In this paper, an improved target detection method in shadow regions

Due to the severe speckle noise of a fully polarimetric synthetic aperture radar image and the complex backscattering mechanism at the junction of different land covers, some of the pixels are easily mislabeled, especially on the edge of the land covers. To address this issue, this study presents a novel scheme that selects polarimetric features step by step to participate in classification through

Satellites produce global monitoring data, while field measurements are made at a local station over the land. Due to difference in scale, it has been a challenge how to define and correct the satellite retrieval biases. Although the relative approach of cumulative distribution functions (CDF) matching compares a long-term climatology of reference data with that of satellite data, it does not mitigate

Radar scatterometer observations at 17.2 GHz and 9.6 GHz were made of the snow cover in mid-latitude agricultural fields, using the University of Waterloo scatterometer, to determine the sensitivity of the frequency-dependent radar response to snow water equivalent. Observations were made in alfalfa fields near Maryhill, Ontario during the 2013–2014 and 2014–2015 winter seasons. Additional observations

Land surface temperature (LST) and soil moisture are important factors in environmental hazard modeling. The main objective of this research is to derive the LST and a soil moisture index (SMI) from thermal satellite images. A split-window algorithm is applied to derive the spectral radiance and emissivity from two thermal infrared (TIR) bands of the Landsat 8 satellite in four consecutive years (2015–2018)