scholarly journals The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1183 ◽  
Author(s):  
Shichao Jin ◽  
Yanjun Su ◽  
Shang Gao ◽  
Tianyu Hu ◽  
Jin Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Spatial Resolution ◽  
Prediction Accuracy ◽  
Vegetation Type ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Spatial Extent ◽  
Vegetation Types ◽  
Height Prediction ◽  
Study Sites

Canopy height is an important forest structure parameter for understanding forest ecosystem and improving global carbon stock quantification accuracy. Light detection and ranging (LiDAR) can provide accurate canopy height measurements, but its application at large scales is limited. Using LiDAR-derived canopy height as ground truth to train the Random Forest (RF) algorithm and therefore predict canopy height from other remotely sensed datasets in areas without LiDAR coverage has been one of the most commonly used method in large-scale canopy height mapping. However, how variances in location, vegetation type, and spatial scale of study sites influence the RF modelling results is still a question that needs to be addressed. In this study, we selected 16 study sites (100 km2 each) with full airborne LiDAR coverage across the United States, and used the LiDAR-derived canopy height along with optical imagery, topographic data, and climate surfaces to evaluate the transferability of the RF-based canopy height prediction method. The results show a series of findings from general to complex. The RF model trained at a certain location or vegetation type cannot be transferred to other locations or vegetation types. However, by training the RF algorithm using samples from all sites with various vegetation types, a universal model can be achieved for predicting canopy height at different locations and different vegetation types with self-predicted R2 higher than 0.6 and RMSE lower than 6 m. Moreover, the influence of spatial scales on the RF prediction accuracy is noticeable when spatial extent of the study site is less than 50 km2 or the spatial resolution of the training pixel is finer than 500 m. The canopy height prediction accuracy increases with the spatial extent and the targeted spatial resolution.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies

2021 ◽  
Vol 13 (8) ◽  
pp. 1588
Author(s):  
Annie S. Guillaume ◽  
Kevin Leempoel ◽  
Estelle Rochat ◽  
Aude Rogivue ◽  
Michel Kasser ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Species Distribution Modelling ◽  
Airborne Lidar ◽  
Swiss Alps ◽  
The Arctic ◽  
Site Characteristics ◽  
Digital Elevation ◽  
Stereo Photogrammetry ◽  
Study Sites ◽  
Very High

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1 m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aim of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant, Arabis alpina, in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs, up to a spatial resolution of at least 1 m, rivalled the accuracy of LiDAR DEMs, largely owing to the customizability of PHOTO DEMs to the study sites compared to commercially available LiDAR DEMs. We obtained DEMs at spatial resolutions of 6.25 cm–8 m for PHOTO and 50 cm–32 m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32 m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50 cm in such studies.

scholarly journals The Influence of CLBP Window Size on Urban Vegetation Type Classification Using High Spatial Resolution Satellite Images

2020 ◽  
Vol 12 (20) ◽  
pp. 3393
Author(s):  
Zhou Chen ◽  
Xianyun Fei ◽  
Xiangwei Gao ◽  
Xiaoxue Wang ◽  
Huimin Zhao ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Vegetation Type ◽  
Window Size ◽  
Urban Heat Islands ◽  
Spectral Information ◽  
Urban Vegetation ◽  
Vegetation Types ◽  
Human Beings ◽  
Type Classification

Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

scholarly journals A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Shuo Xu ◽  
Jie Cheng ◽  
Quan Zhang
Keyword(s):  
Random Forest ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Mainland China ◽  
Fusion Method ◽  
Moisture Condition

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

scholarly journals Satellite monitoring of different vegetation types by differential optical absorption spectroscopy (DOAS) in the red spectral range

2007 ◽  
Vol 7 (1) ◽  
pp. 69-79 ◽  
Author(s):  
T. Wagner ◽  
S. Beirle ◽  
T. Deutschmann ◽  
M. Grzegorski ◽  
U. Platt
Keyword(s):  
Optical Absorption ◽  
Absorption Spectroscopy ◽  
Spectral Range ◽  
Near Infrared ◽  
Vegetation Type ◽  
Differential Optical Absorption Spectroscopy ◽  
Trace Gas ◽  
Satellite Monitoring ◽  
Optical Absorption Spectroscopy ◽  
Vegetation Types

Abstract. A new method for the satellite remote sensing of different types of vegetation and ocean colour is presented. In contrast to existing algorithms relying on the strong change of the reflectivity in the red and near infrared spectral region, our method analyses weak narrow-band (few nm) reflectance structures (i.e. "fingerprint" structures) of vegetation in the red spectral range. It is based on differential optical absorption spectroscopy (DOAS), which is usually applied for the analysis of atmospheric trace gas absorptions. Since the spectra of atmospheric absorption and vegetation reflectance are simultaneously included in the analysis, the effects of atmospheric absorptions are automatically corrected (in contrast to other algorithms). The inclusion of the vegetation spectra also significantly improves the results of the trace gas retrieval. The global maps of the results illustrate the seasonal cycles of different vegetation types. In addition to the vegetation distribution on land, they also show patterns of biological activity in the oceans. Our results indicate that improved sets of vegetation spectra might lead to more accurate and more specific identification of vegetation type in the future.

scholarly journals Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks

2020 ◽  
Vol 2 (1) ◽  
pp. 23-36
Author(s):  
Syed Aamir Ali Shah ◽  
Muhammad Asif Manzoor ◽  
Abdul Bais
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Convolutional Neural Networks ◽  
Forest Structure ◽  
State Of The Art ◽  
Spatial Association ◽  
Canopy Height ◽  
Landsat Images ◽  
Structure Estimation ◽  
Vegetation Height

Forest structure estimation is very important in geological, ecological and environmental studies. It provides the basis for the carbon stock estimation and effective means of sequestration of carbon sources and sinks. Multiple parameters are used to estimate the forest structure like above ground biomass, leaf area index and diameter at breast height. Among all these parameters, vegetation height has unique standing. In addition to forest structure estimation it provides the insight into long term historical changes and the estimates of stand age of the forests as well. There are multiple techniques available to estimate the canopy height. Light detection and ranging (LiDAR) based methods, being the accurate and useful ones, are very expensive to obtain and have no global coverage. There is a need to establish a mechanism to estimate the canopy height using freely available satellite imagery like Landsat images. Multiple studies are available which contribute in this area. The majority use Landsat images with random forest models. Although random forest based models are widely used in remote sensing applications, they lack the ability to utilize the spatial association of neighboring pixels in modeling process. In this research work, we define Convolutional Neural Network based model and analyze that model for three test configurations. We replicate the random forest based setup of Grant et al., which is a similar state-of-the-art study, and compare our results and show that the convolutional neural networks (CNN) based models not only capture the spatial association of neighboring pixels but also outperform the state-of-the-art.

Body size and mean individual biomass variation of ground-beetles community (Coleoptera: Carabidae) as a response to increasing altitude and associated vegetation types in mountainous ecosystem

Biologia ◽  
2017 ◽  
Vol 72 (9) ◽  
Author(s):  
Aleksandra Cvetkovska-Gjorgjievska ◽  
Slavčo Hristovski ◽  
Dana Prelić ◽  
Lucija Šerić Jelaska ◽  
Valentina Slavevska-Stamenković ◽  
...  
Keyword(s):  
Body Size ◽  
Altitudinal Gradient ◽  
Vegetation Type ◽  
Ground Beetle ◽  
Vegetation Types ◽  
Forest Stands ◽  
Geologic History ◽  
White Oak ◽  
Mountain Ecosystems ◽  
Individual Biomass

AbstractCarabid fauna is not sufficiently explored in Central and Western Balkan areas, especially in mountain ecosystems with unique biodiversity which is a result of specific environmental factors and geologic history. Furthermore, distribution of species and adaptation to varying environmental parameters change along the altitudinal gradients. All this highlights the need for biodiversity and ecological studies in order to assess the state of the mountain ecosystems and conservation significance. Carabids as good bioindicator group can be used as a tool for monitoring those changes. The aim of this study was to analyse the differences of body size distribution and mean individual biomass (MIB) of ground beetle assemblages as a response of changing conditions and vegetation types along an altitudinal gradient on Belasitsa Mountain in south Macedonia. Both parameters significantly decreased with increasing altitude and were consequently associated with the vegetation type. Larger bodied individuals and higher values of MIB were recorded in the white oak and oriental hornbeam forest stands with the values decreasing in sessile oak forests towards submontane and montane beech forest stands. This research yielded first list of carabid species inhabiting Belasitsa Mountain with insight of carabid body length and biomass distribution along altitudinal gradient.

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

Soil Moisture Responses Under Different Vegetation Types to Winter Rainfall Events in a Humid Karst Region

2021 ◽  
Author(s):  
Weihong Yan ◽  
Qiuwen Zhou ◽  
Dawei Peng ◽  
Xiaocha Wei ◽  
Xin Tang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Type ◽  
Arable Land ◽  
Karst Region ◽  
Control Group ◽  
Vegetation Types ◽  
Recovery Model ◽  
Winter Rainfall ◽  
Rainfall Events ◽  
Karst Ecosystems

Abstract Humid karst ecosystems are fragile, with precipitation being the main source of soil moisture recharge. The process of soil moisture recharge and usage varies by vegetation type. To analyze the dynamics of soil moisture under different vegetation types during rainfall events, we continuously monitored soil moisture in arable land, grassland, shrub, and forest areas at 10-minute intervals from November 6, 2019, to January 6, 2020.The arable land was used as a control group. Soil moisture under the different vegetation types responded to light, moderate, and rainstorm events with large rainfall amounts. However, only the soil moisture in the grassland areas responded to a light rainfall event with a rainfall amount of 0.87 mm. The largest soil moisture recharge (12.63 mm) and decline (2.08%) were observed for the grassland areas, with the smallest observed for the forest areas. While the grassland areas showed the greatest decline in soil moisture following rainfall, they were more easily recharged during the winter rainfall events. Soil moisture in forests and shrubs was less recharged than in grasslands but also declined less. Therefore, forests and shrubs are better at retaining soil moisture in winter, which is informative for the formulation of a regional vegetation recovery model.

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