scholarly journals Fusing Sentinel-2 Imagery and ALS Point Clouds for Defining LULC Changes on Reclaimed Areas by Afforestation

2019 ◽  
Vol 11 (5) ◽  
pp. 1251 ◽  
Author(s):  
Marta Szostak ◽  
Kacper Knapik ◽  
Piotr Wężyk ◽  
Justyna Likus-Cieślik ◽  
Marcin Pietrzykowski
Keyword(s):  
Laser Scanning ◽  
Canopy Cover ◽  
Point Clouds ◽  
Test Site ◽  
Vegetation Growth ◽  
Landsat 7 ◽  
Forested Areas ◽  
Primary Focus ◽  
Year 2000 ◽  
Sentinel 2

The study was performed on two former sulphur mines located in Southeast Poland: Jeziórko, where 216.5 ha of afforested area was reclaimed after borehole exploitation and Machów, where 871.7 ha of dump area was reclaimed after open cast strip mining. The areas were characterized by its terrain structure and vegetation cover resulting from the reclamation process. The types of reclamation applied in these areas were forestry in Jeziórko and agroforestry in the Machów post-sulphur mine. The study investigates the possibility of applying the most recent Sentinel-2 (ESA) satellite imageries for land cover mapping, with a primary focus on detecting and monitoring afforested areas. Airborne laser scanning point clouds were used to derive precise information about the spatial (3D) characteristics of vegetation: the height (95th percentile), std. dev. of relative height, and canopy cover. The results of the study show an increase in afforested areas in the former sulphur mines. For the entire analyzed area of Jeziórko, forested areas made up 82.0% in the year 2000 (Landsat 7, NASA), 88.8% in 2009 (aerial orthophoto), and 95.5% in 2016 (Sentinel-2, ESA). For Machów, the corresponding results were 46.1% in 2000, 57.3% in 2009, and 60.7% in 2016. A dynamic increase of afforested area was observed, especially in the Jeziórko test site, with the presence of different stages of vegetation growth.

scholarly journals Reclaimed Area Land Cover Mapping Using Sentinel-2 Imagery and LiDAR Point Clouds

2020 ◽  
Vol 12 (2) ◽  
pp. 261
Author(s):  
Marta Szostak ◽  
Marcin Pietrzykowski ◽  
Justyna Likus-Cieślik
Keyword(s):  
Land Cover ◽  
Coniferous Forest ◽  
Canopy Cover ◽  
Point Clouds ◽  
Land Cover Mapping ◽  
Vegetation Development ◽  
Vegetation Growth ◽  
Broad Leaved Forest ◽  
Primary Focus ◽  
Sentinel 2

This paper investigates the possibility of using fusion Sentinel-2 imageries (2016, ESA) and light detection and ranging (LiDAR) point clouds for the automation of land cover mapping with a primary focus on detecting and monitoring afforested areas and deriving precise information about the spatial (2D and 3D) characteristics of vegetation for reclaimed areas. The study was carried out for reclaimed areas – two former sulfur mines located in Southeast Poland, namely, Jeziórko, where 216.5 ha of afforested area was reclaimed after borehole exploitation, and Machów, where 871.7 ha of dump area was reclaimed after open cast strip mining. The current land use and land cover (LULC) classes at the Machów and Jeziórko former sulfur mines are derived based on Sentinel-2 image processing, and confirmed the applied type of reclamation for both analysed areas. The following LULC classes showed a significant spatial range: broad-leaved forest, coniferous forest, and transitional woodland shrub. The progress of afforested areas, not only in terms of the occupied area, but also in terms of the growth of trees and shrubs, was confirmed. The results of the study showed differences in vegetation parameters, namely, height and canopy cover. Various stages of vegetation growth were also observed. This indicates an ongoing process of vegetation development, as an effect of the reclamation treatment for these areas.

scholarly journals ALS-based wood volume models of forest stands and comparison with forest inventory data

2016 ◽  
Vol 64 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Tauri Arumäe ◽  
Mait Lang
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Point Clouds ◽  
Test Site ◽  
Sample Plot ◽  
Volume Estimate ◽  
Wood Volume ◽  
Level Model ◽  
Volume Models

Abstract Airborne laser scanning (ALS) based standing wood volume models were analysed in two contrasting test sites with different forest types in Estonia. In Aegviidu test site main tree species are Scots pine and Norway spruce and Laeva test site is mainly dominated by deciduous species. ALS data measurements were carried out for Aegviidu in 2008 and for Laeva in 2013. Approximately 450 sample plots were established additionally to the forest inventory dataset in both test sites. Exclusive to the sample plots, 46 stands were measured in 2012 in Aegviidu for stand level model. The sample plot-based model standard error in Aegviidu was Se = 59.8 m3/ha (22%) and in Laeva Se = 69.2 m3/ha (29%). The stand-level model based on 46 measured stands from Aegviidu had Se = 38.4 m3/ha. Based on the models a cross-validation between the two test sites was carried out and systematic differences between the two test sites were found. The reasons are related to differences in optical properties of trees, crown shapes, flight configuration and canopy cover even though the sample plot based models included ALS-based canopy cover variable. The ALS-based wood volume estimate was also compared to forest inventory (FI) data and systematically larger estimates compared to FI dataset in both test sites were found. This average systematic error increased substantially (by 100 m3/ha) for stands with volume over 250 m3/ha. It was also detected that a model developed on small point clouds drawn for sample plots may produce systematic errors when applied to stand-level point clouds.

scholarly journals Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

2021 ◽  
Vol 13 (2) ◽  
pp. 257 ◽  
Author(s):  
Shaun R. Levick ◽  
Tim Whiteside ◽  
David A. Loewensteiner ◽  
Mitchel Rudge ◽  
Renee Bartolo
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Structural Diversity ◽  
Point Clouds ◽  
Large Area ◽  
Calibration And Validation ◽  
Savanna Vegetation ◽  
Landscape Scales

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

scholarly journals OBJECT-BASED COREGISTRATION OF TERRESTRIAL PHOTOGRAMMETRIC AND ALS POINT CLOUDS IN FORESTED AREAS

2016 ◽  
Vol III-3 ◽  
pp. 347-354
Author(s):  
P. Polewski ◽  
A. Erickson ◽  
W. Yao ◽  
N. Coops ◽  
P. Krzystek ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Local Coordinate System ◽  
Real Data ◽  
Point Clouds ◽  
Forest Understory ◽  
Maximum Matching ◽  
Coast Range ◽  
Forested Areas ◽  
Matching Techniques

Airborne Laser Scanning (ALS) and terrestrial photogrammetry are methods applicable for mapping forested environments. While ground-based techniques provide valuable information about the forest understory, the measured point clouds are normally expressed in a local coordinate system, whose transformation into a georeferenced system requires additional effort. In contrast, ALS point clouds are usually georeferenced, yet the point density near the ground may be poor under dense overstory conditions. In this work, we propose to combine the strengths of the two data sources by co-registering the respective point clouds, thus enriching the georeferenced ALS point cloud with detailed understory information in a fully automatic manner. Due to markedly different sensor characteristics, coregistration methods which expect a high geometric similarity between keypoints are not suitable in this setting. Instead, our method focuses on the object (tree stem) level. We first calculate approximate stem positions in the terrestrial and ALS point clouds and construct, for each stem, a descriptor which quantifies the 2D and vertical distances to other stem centers (at ground height). Then, the similarities between all descriptor pairs from the two point clouds are calculated, and standard graph maximum matching techniques are employed to compute corresponding stem pairs (tiepoints). Finally, the tiepoint subset yielding the optimal rigid transformation between the terrestrial and ALS coordinate systems is determined. We test our method on simulated tree positions and a plot situated in the northern interior of the Coast Range in western Oregon, USA, using ALS data (76&thinsp;x&thinsp;121&thinsp;m<sup>2</sup>) and a photogrammetric point cloud (33&thinsp;x&thinsp;35&thinsp;m<sup>2</sup>) derived from terrestrial photographs taken with a handheld camera. Results on both simulated and real data show that the proposed stem descriptors are discriminative enough to derive good correspondences. Specifically, for the real plot data, 24 corresponding stems were coregistered with an average 2D position deviation of 66&thinsp;cm.

State-of-the-art in 4D measurements

2021 ◽  
Author(s):  
Eetu Puttonen ◽  
Juha Hyyppä ◽  
Paula Litkey ◽  
Mariana Batista Campos ◽  
Heikki Hyyti ◽  
...  
Keyword(s):  
Vegetation Dynamics ◽  
Laser Scanning ◽  
Structural Changes ◽  
Structural Information ◽  
Laser Scanner ◽  
Point Clouds ◽  
Test Site ◽  
Forest Monitoring ◽  
Monitoring Technologies

&lt;p&gt;Light detection and ranging (lidar) has become an essential tool in mapping and change detection in different environments over the last 20 years. Laser scanners capture point clouds to create accurate digital snapshots of their surroundings. These snapshots tell about the structural information in the scene and can be readily returned to again and again to detect and measure any changes with multi-temporal measurements. However, multitemporal measurements cannot typically resolve the change events nor can they resolve more high frequency dynamics that happen on daily or weekly basis in the scene. Also, lidar systems operate still mainly with single wavelength limiting their usability in classification tasks. First multi- and hyperspectral systems have been already demonstrated, but have yet to break through in wider usage. Finnish Geospatial Research Institute (FGI) has been prototyping with different 3D measurement systems for the last 10 years to improve multitemporal mapping (4D) solutions. The prototypes include both hyperspectral and long-term multi- and hypertemporal lidar systems, and their combinations in static and mobile configurations. FGI started early on to experiment with hyperspectral laser sources (2007) and successfully demonstrated the first hyperspectral laser scanner prototype in 2012. The system was later used in detecting intraday vegetation dynamics in 2015. Multitemporal multispectral ALS measurements have been conducted since 2015 in Evo and in Espoolahti. The first long-term multitemporal studies with FGI mapping platforms were started with ALS to monitor changes in forests (1998) and built environment (2001) and with mobile laser scanning in studying the erosion of an arctic river basin (2008) annually. &amp;#160;Multitemporal ALS studies with vegetation started in 1998 in Kalkkinen and in 2007 in Evo followed with bi-temporal studies with TLS. Test Site Evo has been acquired with ALS. In 2020, Evo test site was granted Academy of Finland Research Infrastructure (RI) status. The RI will collect a 30-year-long time series with annual measurements using various laser scanning sensors for investigating single tree growth processes, forest dynamics, understanding cyclic forest while having variation at diurnal and annual scales and forest monitoring technologies. Vegetation dynamics monitoring was extended in 2020, when FGI started set up a permanent TLS measurement station in a boreal forest. The TLS station accurately detects structural changes of hundreds of tree crowns around it. The experiment aims to detect the changes of phenological state the trees and further link them with the environmental parameter variation. 4D measurements have successfully demonstrated their potential in extending the information available from laser scanning systems. To improve the usage of these novel information, automated pre-filtering of the vast data amounts already at sensor level will be imperative. Different lidar platforms can operate throughout the spatial scale from millimeter precision all way to national coverage. Thus, development of new scalable lidar RIs open new possibilities to complement already existing infrastructures.&lt;/p&gt;

scholarly journals Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas

2020 ◽  
Vol 12 (12) ◽  
pp. 1943
Author(s):  
Ranjith Gopalakrishnan ◽  
Daniela Ali-Sisto ◽  
Mikko Kukkonen ◽  
Pekka Savolainen ◽  
Petteri Packalen
Keyword(s):  
Standard Deviation ◽  
Urban Areas ◽  
Laser Scanning ◽  
Point Clouds ◽  
Image Point ◽  
Vertical Coordinate ◽  
Cloud Data ◽  
Suburban Areas ◽  
Aerial Photogrammetry ◽  
Forested Areas

Globally, urban areas are rapidly expanding and high-quality remote sensing products are essential to help guide such development towards efficient and sustainable pathways. Here, we present an algorithm to address a common problem in digital aerial photogrammetry (DAP)-based image point clouds: vertical mis-registration. The algorithm uses the ground as inferred from airborne laser scanning (ALS) data as a reference surface and re-aligns individual point clouds to this surface. We demonstrate the effectiveness of the proposed method for the city of Kuopio, in central Finland. Here, we use the standard deviation of the vertical coordinate values as a measure of the mis-registration. We show that such standard deviation decreased substantially (more than 1.0 m) for a large proportion (23.2%) of the study area. Moreover, it was shown that the method performed better in urban and suburban areas, compared to vegetated areas (parks, forested areas, and so on). Hence, we demonstrate that the proposed algorithm is a simple and effective method to improve the quality and usability of DAP-based point clouds in urban areas.

scholarly journals Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements

Silva Fennica ◽  
2021 ◽  
Vol 55 (1) ◽  
Author(s):  
Mikko Kukkonen ◽  
Eetu Kotivuori ◽  
Matti Maltamo ◽  
Lauri Korhonen ◽  
Petteri Packalen
Keyword(s):  
Tree Species ◽  
Small Area ◽  
Laser Scanning ◽  
Point Clouds ◽  
Unmanned Aircraft ◽  
Remotely Sensed Data ◽  
Alternative Source ◽  
Species Specific ◽  
Species Proportions ◽  
Sentinel 2

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by nearest neighbor (-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.kk

scholarly journals Assessment of forest thinning intensity using sparse point clouds from repeated airborne lidar measurements

2018 ◽  
Vol 68 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Mait Lang ◽  
Tauri Arumäe
Keyword(s):  
Ground Surface ◽  
Canopy Cover ◽  
Point Clouds ◽  
Test Site ◽  
Airborne Lidar ◽  
Measurement Interval ◽  
Wood Volume ◽  
Thinning Intensity ◽  
Lidar Measurements ◽  
Volume Estimates

Abstract Thinning cuttings create moderate disturbances in forest stands. Thinning intensity indicates the amount of felled wood relative to the initial standing volume. We used sparse point clouds from airborne lidar measurements carried out in 2008 and 2012 at Aegviidu test site, Estonia, to study stand level relationships of thinning intensity to the changes in canopy cover and ALS-based wood volume estimates. Thinning intensity (Kr, HRV) was estimated from forest inventory data and harvester measurements of removed wood volume. The thinning intensity ranged from 17% to 56%. By raising threshold from 1.3 m to 8.0 m over ground surface we observed less canopy cover change, but stronger correlation with thinning intensity. Correlation between ALS-based and harvester-based thinning intensity was moderate. The ALS-based thinning intensity estimate was systematically smaller than Kr, HRV. Forest height growth compensates for a small decrease in canopy cover and intensity estimates for weak thinnings are not reliable using sparse point clouds and a four-year measurement interval.

scholarly journals EVALUATION OF VERTICAL LACUNARITY PROFILES IN FORESTED AREAS USING AIRBORNE LASER SCANNING POINT CLOUDS

2016 ◽  
Vol III-8 ◽  
pp. 93-99
Author(s):  
B. Székely ◽  
A. Kania ◽  
T. Standovár ◽  
H. Heilmeier
Keyword(s):  
Laser Scanning ◽  
Dynamic Range ◽  
Canopy Structure ◽  
Three Dimensional ◽  
Point Clouds ◽  
Reference Points ◽  
Airborne Laser Scanning ◽  
Topographic Effects ◽  
Airborne Laser ◽  
Forested Areas

The horizontal variation and vertical layering of the vegetation are important properties of the canopy structure determining the habitat; three-dimensional (3D) distribution of objects (shrub layers, understory vegetation, etc.) is related to the environmental factors (e.g., illumination, visibility). It has been shown that gaps in forests, mosaic-like structures are essential to biodiversity; various methods have been introduced to quantify this property. As the distribution of gaps in the vegetation is a multi-scale phenomenon, in order to capture it in its entirety, scale-independent methods are preferred; one of these is the calculation of lacunarity. <br><br> We used Airborne Laser Scanning point clouds measured over a forest plantation situated in a former floodplain. The flat topographic relief ensured that the tree growth is independent of the topographic effects. The tree pattern in the plantation crops provided various quasi-regular and irregular patterns, as well as various ages of the stands. The point clouds were voxelized and layers of voxels were considered as images for two-dimensional input. These images calculated for a certain vicinity of reference points were taken as images for the computation of lacunarity curves, providing a stack of lacunarity curves for each reference points. These sets of curves have been compared to reveal spatial changes of this property. As the dynamic range of the lacunarity values is very large, the natural logarithms of the values were considered. Logarithms of lacunarity functions show canopy-related variations, we analysed these variations along transects. The spatial variation can be related to forest properties and ecology-specific aspects.

scholarly journals AUTOMATED STRUCTURAL FOREST CHANGES USING LIDAR POINT CLOUDS AND GIS ANALYSES

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 603-608
Author(s):  
A. Novo ◽  
H. González-Jorge ◽  
J. Martínez-Sánchez ◽  
J. M. Fernández-Alonso ◽  
H. Lorenzo
Keyword(s):  
Spatial Data ◽  
Forest Structure ◽  
Laser Scanning ◽  
Structural Model ◽  
Canopy Cover ◽  
Ground Truth ◽  
Point Clouds ◽  
Forest Community ◽  
Structural Behaviour ◽  
Ground Truth Data

Abstract. Forest spatial structure describes the relationships among different species in the same forest community. Automation in the monitoring of the structural forest changes and forest mapping is one of the main utilities of applications of modern geoinformatics methods. The obtaining objective information requires the use of spatial data derived from photogrammetry and remote sensing. This paper investigates the possibility of applying light detection and ranging (LiDAR) point clouds and geographic information system (GIS) analyses for automated mapping and detection changes in vegetation structure during a year of study. The research was conducted in an area of the Ourense Province (NWSpain). The airborne laser scanning (ALS) data, acquired in August 2019 and June of 2020, reveal detailed changes in forest structure. Based on ALS data the vegetation parameters will be analysed.To study the structural behaviour of the tree vegetation, the following parameters are used in each one of the sampling areas: (1) Relationship between the tree species present and their stratification; (2) Vegetation classification in fuel types; (3) Biomass (Gi); (4) Number of individuals per area; and (5) Canopy cover fraction (CCF). Besides, the results were compared with the ground truth data recollected in the study area.The development of a quantitative structural model based on Aerial Laser Scanning (ALS) point clouds was proposed to accurately estimate tree attributes automatically and to detect changes in forest structure. Results of statistical analysis of point cloud show the possibility to use UAV LiDAR data to characterize changes in the structure of vegetation.

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