Search Results (12)

Land-use changes cause disturbance to sediment dynamics, increasing downstream sediment loads discharged into ecosystems and provoking impacts on stream quality and damage to current stormwater infrastructures. Wastewater nature-based solutions (NBSWT) are bioretention techniques that alleviate downstream degradation caused by runoff sediment accumulation and are projected as an off-line street device that enhances treatment of runoff contaminant loads. This research assesses the economic, social, and environmental benefits from sediment load reduction in runoff by designing a new NBSWT in a selected urban area of the Mantiqueira Mountain Range (São Paulo, Brazil), considered an irreplaceable protected area for biodiversity and urban water supply. To achieve this quantification, the shadow prices methodology has been used. The results obtained here show the adaptive capacity that NBSWT have according to the territory and its climatic particularities, quantified at USD 40,475,255. This value demonstrates that the retention of runoff sediment generates a direct environmental benefit related to the ecosystem improvement of the river system located downstream, preserving its environmental and social importance. Hence, this study demonstrates the potential of using shadow prices methodology as a management tool for quantifying the environmental benefit of removing runoff solids by using NBSWT in developing urban areas. Full article

Global warming and intensified human activities increase flood disasters, causing annual casualties and economic losses. Mountain shadows are a major source of interference in floodwater extraction from SAR imagery, severely impacting the accuracy of water body detection. This study proposes an innovative approach based on the Inverted Exponential Shadow Removal Model (IESRM). This model can adaptively and dynamically adjust the slope threshold according to the terrain characteristics. It is easy to use, eliminating the need for manual parameter setting. The experimental results demonstrate the following: (1) Water body detection tests across diverse terrains (mountains, plains, and foothill plains) show robust results even in complex foothill regions, with an overall accuracy of 94.51% and a Kappa coefficient of 0.86. (2) A comparative analysis with the shadow formation mechanism method and the HAND (Height Above Nearest Drainage) method revealed that the inverted exponential function model achieved the highest accuracy, with an overall accuracy of 96.46% and a Kappa coefficient of 0.89. The IESRM provides an innovative solution for removing mountain shadows, enhancing SAR imagery-based flood monitoring in complex terrains. It offers timely and accurate data support for flood disaster management agencies. Full article

Accurately mapping urban above-ground vegetation carbon density presents challenges due to fragmented landscapes, mixed pixels, and shadows induced by buildings and mountains. To address these issues, a novel methodological framework is introduced, utilizing a linear spectral unmixing analysis (LSUA) for shadow removal and vegetation information extraction from mixed pixels. Parametric and nonparametric models, incorporating LSUA-derived vegetation fraction, are compared, including linear stepwise regression, logistic model-based stepwise regression, k-Nearest Neighbors, Decision Trees, and Random Forests. Applied in Shenzhen, China, the framework integrates Landsat 8, Pleiades 1A & 1B, DEM, and field measurements. Among the key findings, the shadow removal algorithm is effective in mountainous areas, while LSUA-enhanced models improve urban vegetation carbon density mapping, albeit with marginal gains. Integrating kNN and RF with LSUA reduces errors, and Decision Trees, especially when integrated with LSUA, outperform other models. This study underscores the potential of the proposed framework, particularly the integration of Decision Trees with LSUA, for advancing the accuracy of urban vegetation carbon density mapping. Full article

Under the background of intensified human activities and global climate warming, the frequency and intensity of flood disasters have increased, causing many casualties and economic losses every year. Given the difficulty of mountain shadow removal from large-scale watershed flood monitoring based on Sentinel-1 SAR images and the Google Earth Engine (GEE) cloud platform, this paper first adopted the Support Vector Machine (SVM) to extract the water body information during flooding. Then, a function model was proposed based on the mountain shadow samples to remove the mountain shadows from the flood maps. Finally, this paper analyzed the flood disasters in the middle and lower basin of the Yangtze River (MLB) in 2020. The main results showed that: (1) compared with the other two methods, the SVM model had the highest accuracy. The accuracy and kappa coefficients of the trained SVM model in the testing dataset were 97.77% and 0.9521, respectively. (2) The function model proposed based on the samples achieved the best effect compared with other shadow removal methods with a shadow recognition rate of 75.46%, and it alleviated the interference of mountain shadows for flood monitoring in a large basin. (3) The flood inundated area was 8526 km², among which, cropland was severely affected (6160 km²). This study could provide effective suggestions for relevant stakeholders in policy making. Full article

Solar photovoltaic (PV) power generation is a vital renewable energy to achieve carbon neutrality. Previous studies which explored mapping PV using open satellite data mainly focus in remote areas. However, the complexity of land cover types can bring much difficulty in PV identification. This study investigated detecting PV in diverse landscapes using freely accessible remote sensing data, aiming to evaluate the transferability of PV detection between rural and urbanized coastal area. We developed a random forest-based PV classifier on Google Earth Engine in two provinces of China. Various features including Sentinel-2 reflectance, Sentinel-1 polarization, spectral indices and their corresponding textures were constructed. Thereafter, features with high permutation importance were retained. Three classification schemes with different training and test samples were, respectively, conducted. Finally, the VIIRS nighttime light data were utilized to refine the initial results. Manually collected samples and existing PV database were used to evaluate the accuracy of our method. The results revealed that the top three important features in detecting PV were the sum average texture of three bands (NDBI, VV, and VH). We found the classifier trained in highly urbanized coastal landscape with multiple PV types was more transferable (OA = 97.24%, kappa = 0.94), whereas the classifier trained in rural landscape with simple PV types was erroneous when applied vice versa (OA = 68.84%, kappa = 0.44). The highest accuracy was achieved when using training samples from both regions as expected (OA = 98.90%, kappa = 0.98). Our method recalled more than 94% PV in most existing databases. In particular, our method has a stronger detection ability of PV installed above water surface, which is often missing in existing PV databases. From this study, we found two main types of errors in mapping PV, including the bare rocks and mountain shadows in natural landscapes and the roofing polyethylene materials in urban settlements. In conclusion, the PV classifier trained in highly urbanized coastal landscapes with multiple PV types is more accurate than the classifier trained in rural landscapes. The VIIRS nighttime light data contribute greatly to remove PV detection errors caused by bare rocks and mountain shadows. The finding in our study can provide reference values for future large area PV monitoring. Full article

The dynamic variation in the water surfaces of the river networks within the Qinghai-Tibet Plateau affects the water resource availability for downstream ecosystems and human activities. Small rivers (with a river width less than 30 m) are an important component of this network, but are difficult to map in the Qinghai-Tibet Plateau. Firstly, the width of most rivers is very narrow, at around 20 m, which appears as only one or two pixels in Sentinel-2 images and thus is susceptible to salt-and-pepper noise. Secondly, local mountain shadows, cloud shadows, and snow pixels have spectral characteristics similar to those of rivers, leading to misclassification. Therefore, we propose an automated small river mapping (ASRM) method based on Sentinel-2 imagery to address these two difficulties. A preprocessing procedure was designed to remove the salt-and-pepper noise and enhance the linear characteristic of rivers with specific widths. A flexible digital elevation model (DEM)-based post-processing was then imposed to remove the misclassifications caused by mountain shadows, cloud shadows, and snow pixels. The ASRM results achieved an overall accuracy of 87.5%, outperforming five preexisting remote sensing-derived river network products. The proposed ASRM method has shown great potential for small river mapping in the entire Qinghai-Tibet Plateau. Full article

The mountainous vegetation is important to regional sustainable development. However, the topographic effect is the main obstacle to the monitoring of mountainous vegetation using remote sensing. Aiming to retrieve the reflectance of frequently-used red–green–blue and near-infrared (NIR) wavebands of rugged mountains for vegetation mapping, we developed a new integrated topographic correction (ITC) using the SCS + C correction and the shadow-eliminated vegetation index. The ITC procedure consists of image processing, data training, and shadow correction and uses a random forest machine learning algorithm. Our study using the Landsat 8 Operational Land Imager (OLI) multi-spectral images in Fujian province, China, showed that the ITC achieved high performance in topographic correction of regional mountains and in transferability from the sunny area of a scene to the shadow area of three scenes. The ITC-corrected multi-spectral image with an NIR–red–green composite exhibited flat features with impressions of relief and topographic shadow removed. The linear regression of corrected waveband reflectance vs. the cosine of the solar incidence angle showed an inclination that nearly reached the horizontal, and the coefficient of determination decreased to 0.00~0.01. The absolute relative errors of the cast shadow and the self-shadow all dramatically decreased to the range of 0.30~6.37%. In addition, the achieved detection rate of regional vegetation coverage for the three cities of Fuzhou, Putian, and Xiamen using the ITC-corrected images was 0.92~6.14% higher than that using the surface reflectance images and showed a positive relationship with the regional topographic factors, e.g., the elevation and slope. The ITC-corrected multi-spectral images are beneficial for monitoring regional mountainous vegetation. Future improvements can focus on the use of the ITC in higher-resolution imaging. Full article

It is significant to study the vegetation of protected areas in rugged mountains where the vegetation grows naturally with minimal eco-society environmental stress from anthropogenic activities. The shadow-eliminated vegetation index (SEVI) was used to monitor the vegetation of protected areas, since it successfully removes topographic shadow effects. In order to auto achieve the best adjustment factor for SEVI calculation from regional area images, we developed a new calculation algorithm using block information entropy (BIE-algorithm). The BIE-algorithm auto-detected typical blocks (subareas) from slope images and achieved the best adjustment factor from a block where the SEVI obtained the highest information entropy in an entire scene. Our obtained regional SEVI result from two scenes of Landsat 8 OLI images using the BIE-algorithm exhibited an overall flat feature with the impression of the relief being drastically removed. It achieved balanced values among three types of samples: Sunny area, self-shadow, and cast shadow, with SEVI means of 0.73, 0.77, and 0.75, respectively, and the corresponding SEVI relative errors of self-shadow and cast shadow were only 4.99% and 1.84%, respectively. The linear regression of SEVI vs. the cosine of the solar incidence angle was nearly horizontal, with an inclination of −0.0207 and a coefficient of determination of 0.0042. The regional SEVI revealed that the vegetation growth level sequence of three protected areas was Wuyishan National Park (SEVI mean of 0.718) > Meihuashan National Nature Reserve (0.672) > Minjiangyuan National Nature Reserve (0.624) > regional background (0.572). The vegetation growth in the protected areas was influenced by the terrain slope and years of establishment of the protected area and by the surrounding buffer zone. The homogeneous distribution of vegetation in a block is influenced by many factors, such as the actual vegetation types, block size, and shape, which need consideration when the proposed BIE-algorithm is used. Full article

Correction of spectral reflectance for shadow and topography in optical remote sensing data is challenging. Here, we corrected for canopy self-shadowing in an evergreen conifer forest in mountainous terrain using a three-dimensional (3D) point cloud. In our approach, the surface was modeled from structure-from-motion processed images provided by an unmanned aerial vehicle; then, the relationship between the observed spectral reflectance of the Sentinel-2A/B multispectral instrument, and the simulated sunlit fraction (the percentage of the solar-illuminated area within a Sentinel-2 pixel) was determined based on a ray-tracing scheme using a 3D point cloud. Scene-based and pixel-based linear regressions were applied to remove canopy-shadow and topographic effects from satellite-observed reflectance. Scene-based regression resulted in large seasonal changes that caused overcorrection in winter. Pixel-based regression generated stable seasonal profiles in both the red and near-infrared reflectance values over the conifer canopy; however, excessively smoothed seasonal changes were implemented over deciduous vegetation. In both correction methods, the reflection of incident light by the canopy likely improved the correction accuracy by decreasing the contrast between illuminated and shaded pixels in summer and increasing it in winter. The results were also visually compared with those from the Sun-Canopy-Sensor with C (SCS+C) method and the Sentinel-2 Level-2A product. This research demonstrates the effectiveness of using a 3D point cloud to correct for self-shadowing and topographic effects on remotely sensed reflectance. Full article

Cloud-free remote sensing images are required for many applications, such as land cover classification, land surface temperature retrieval and agricultural-drought monitoring. Cloud cover in remote sensing images can be pervasive, dynamic and often unavoidable. Current techniques of cloud removal for the VNIR (visible and near-infrared) bands still encounters the problem of pixel values estimated for the cloudy area incomparable and inconsistent with the cloud-free region in the target image. In this paper, we proposed an efficient approach to remove thick clouds and their shadows in VNIR bands using multi-temporal images with good maintenance of DN (digital number) value consistency. We constructed the spectral similarity between the target image and reference one for DN value estimation of the cloudy pixels. The information reconstruction was done with 10 neighboring cloud-free pair-pixels with the highest similarity over a small window centering the cloudy pixel between target and reference images. Four Landsat5 TM images around Nanjing city of Jiangsu Province in Eastern China were used to validate the approach over four representative surface patterns (mountain, plain, water and city) for diverse sizes of cloud cover. Comparison with the conventional approaches indicates high accuracy of the approach in cloud removal for the VNIR bands. The approach was applied to the Landsat8 OLI (Operational Land Imager) image on 29 April 2016 in Nanjing area using two reference images. Very good consistency was achieved in the resulted images, which confirms that the proposed approach could be served as an alternative for cloud removal in the VNIR bands using multi-temporal images. Full article

Tree heights are the principal variables for forest plantation inventory. The increasing availability of high-resolution three-dimensional (3D) point clouds derived from low-cost Unmanned Aerial Vehicle (UAV) and modern photogrammetry offers an opportunity to generate a Canopy Height Model (CHM) in the mountainous areas. In this paper, we assessed the capabilities of tree height estimation using UAV-based Structure-from-Motion (SfM) photogrammetry and Semi-Global Matching (SGM). The former is utilized to generate 3D geometry, while the latter is used to generate dense point clouds from UAV imagery. The two algorithms were coupled with a Radial Basis Function (RBF) neural network to acquire CHMs in mountainous areas. This study focused on the performance of Digital Terrain Model (DTM) interpolation over complex terrains. With the UAV-based image acquisition and image-derived point clouds, we constructed a 5 cm-resolution Digital Surface Model (DSM), which was assessed against 14 independent checkpoints measured by a Real-Time Kinematic Global Positioning System RTK GPS. Results showed that the Root Mean Square Errors (RMSEs) of horizontal and vertical accuracies are approximately 5 cm and 10 cm, respectively. Bare-earth Index (BEI) and Shadow Index (SI) were used to separate ground points from the image-derived point clouds. The RBF neural network coupled with the Difference of Gaussian (DoG) was exploited to provide a favorable generalization for the DTM from 3D ground points with noisy data. CHMs were generated using the height value in each pixel of the DSM and by subtracting the corresponding DTM value. Individual tree heights were estimated using local maxima algorithm under a contour-surround constraint. Two forest plantations in mountainous areas were selected to evaluate the accuracy of estimating tree heights, rather than field measurements. Results indicated that the proposed method can construct a highly accurate DTM and effectively remove nontreetop maxima. Furthermore, the proposed method has been confirmed to be acceptable for tree height estimation in mountainous areas given the strong linear correlation of the measured and estimated tree heights and the acceptable t-test values. Overall, the low-cost UAV-based photogrammetry and RBF neural network can yield a highly accurate DTM over mountainous terrain, thereby making them particularly suitable for rapid and cost-effective estimation of tree heights of forest plantation in mountainous areas. Full article

The remote-sensing based Flood Crop Loss Assessment Service System (RF-CLASS) is a web service based system developed and managed by the Center for Spatial Information Science and Systems (CSISS). The system uses Moderate Resolution Imaging Spectroradiometer (MODIS)-based flood data, which was implemented by the Dartmouth Flood Observatory (DFO), to provide an estimation of crop loss from floods. However, due to the spectral similarity between water and shadow, a noticeable amount of false classification of shadow can be found in the DFO flood products. Traditional methods can be utilized to remove cloud shadow and part of mountain shadow. This paper aims to develop an algorithm to filter out noise from permanent mountain shadow in the flood layer. The result indicates that mountain shadow was significantly removed by using the proposed approach. In addition, the gold standard test indicated a small number of actual water surfaces were misidentified by the proposed algorithm. Furthermore, experiments also suggest that increasing the spatial resolution of the slope helped reduce more noise in mountains. The proposed algorithm achieved acceptable overall accuracy (>80%) in all different filters and higher overall accuracies were observed when using lower slope filters. This research is one of the very first discussions on identifying false flood classification from terrain shadow by using the highly efficient method. Full article