Estimating Historically Cleared and Forested Land in Massachusetts, USA, Using Airborne LiDAR and Archival Records

In the northeastern United States, widespread deforestation occurred during the 17–19th centuries as a result of Euro-American agricultural activity. In the late 19th and early 20th centuries, much of this agricultural landscape was reforested as the region experienced industrialization and farmland became abandoned. Many previous studies have addressed these landscape changes, but the primary method for estimating the amount and distribution of cleared and forested land during this time period has been using archival records. This study estimates areas of cleared and forested land using historical land use features extracted from airborne LiDAR data and compares these estimates to those from 19th century archival maps and agricultural census records for several towns in Massachusetts, a state in the northeastern United States. Results expand on previous studies in adjacent areas, and demonstrate that features representative of historical deforestation identified in LiDAR data can be reliably used as a proxy to estimate the spatial extents and area of cleared and forested land in Massachusetts and elsewhere in the northeastern United States. Results also demonstrate limitations to this methodology which can be mitigated through an understanding of the surficial geology of the region as well as sources of error in archival materials.

Download Full-text

Regionalization of an Existing Global Forest Canopy Height Model for Forests of the Southern United States

Remote Sensing ◽

10.3390/rs13091722 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1722

Author(s):

Nian-Wei Ku ◽

Sorin Popescu ◽

Marian Eriksson

Keyword(s):

United States ◽

Large Scale ◽

Forest Canopy ◽

Airborne Lidar ◽

Canopy Height ◽

Southern United States ◽

Biomass Estimation ◽

Lidar Data ◽

Local Data ◽

Airborne Lidar Data

A large-scale global canopy height map (GCHM) is essential for global forest aboveground biomass estimation. Four GCHMs have recently been built using data from the Geoscience Laser Altimeter System (GLAS) sensor aboard the Ice, Cloud, and land Elevation Satellite (ICESat), along with auxiliary spatial and climate information. The main objectives of this research were to find out how well a selected GCHM agrees with airborne lidar data from locations in the southern United States and to recalibrate that GCHM to more closely match the forest canopy heights found in the region. The airborne lidar resource was built from data collected between 2010 and 2012, available from in-house and publicly available sources, for sites that included a variety of vegetation types across the southern United States. EPA ecoregions were used to provide ecosystem information for the southern United States. The airborne lidar data were pre-processed to provide lidar-derived metrics, and assigned to four height categories—namely, returns from above 0 m, 1 m, 3 m, and 5 m. The assessment phase results indicated that the 90th and 95th percentiles of the airborne lidar height values were well-suited for use in the recalibration phase of the study. Simple linear regression was used to generate a new, recalibrated GCHM. It was concluded that the characterization of the agreement of a selected GCHM with local data, followed by the recalibration of the existing GCHM to the local region, is both viable and essential for future GCHMs in studies conducted at large scales.

Download Full-text

Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [Picea abies (L.) Karst.] Test Site in Romania

Notulae Botanicae Horti Agrobotanici Cluj-Napoca ◽

10.15835/nbha.44.1.10155 ◽

2016 ◽

Vol 44 (1) ◽

Author(s):

Bogdan APOSTOL ◽

Adrian LORENT ◽

Marius PETRILA ◽

Vladimir GANCZ ◽

Ovidiu BADEA

Keyword(s):

Picea Abies ◽

Norway Spruce ◽

Test Site ◽

Volume Estimation ◽

Airborne Lidar ◽

Lidar Data ◽

Stand Volume ◽

Airborne Lidar Data

Download Full-text

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

Forest Ecosystems ◽

10.1186/s40663-019-0212-0 ◽

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.

Download Full-text

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

Applied Geomatics ◽

10.1007/s12518-021-00371-6 ◽

2021 ◽

Author(s):

Renato César dos Santos ◽

Mauricio Galo ◽

André Caceres Carrilho ◽

Guilherme Gomes Pessoa

Keyword(s):

Urban Space ◽

Airborne Lidar ◽

Lidar Data ◽

Multi Temporal ◽

Airborne Lidar Data

Download Full-text

A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

Remote Sensing ◽

10.3390/rs13040559 ◽

2021 ◽

Vol 13 (4) ◽

pp. 559

Author(s):

Milto Miltiadou ◽

Neill D. F. Campbell ◽

Darren Cosker ◽

Michael G. Grant

Keyword(s):

Data Structure ◽

Data Structures ◽

Airborne Lidar ◽

Memory Allocation ◽

Lidar Data ◽

Full Waveform ◽

Waveform Lidar ◽

Airborne Lidar Data ◽

Full Waveform Lidar ◽

Polygonal Model

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

Download Full-text