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Dr. Izaya Numata

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Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
Interests: land cover change; vegetation monitoring; spatio-temporal analysis

Special Issue Information

Dear Colleagues,

Identification, quantification, and monitoring of vegetation undergoing degradation and recovery is one of the research priorities for ecosystem management. Vegetation degradation and it recover processes vary as functions of disturbance type (e.g., clearing, logging and fire), time since the last disturbance and the number of disturbances over the course of time. Deep tTime series of satellite optical remote sensors such as Landsat, MODIS and Sentinel allow us to detect spatio-temporal changes in vegetation properties and to develop disturbance-recovery history over multiple years. In addition, the new satellite systems such as the Global Ecosystem Dynamics Investigation Lidar (GEDI) and The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) with the Advanced Topographic Laser Altimeter System (ATLAS) can provide vegetation structure data and allow us to measure canopy height and carbon stocks at the global scale. The use of these optical sensors allows us to model a range of vegetation disturbance types as well as associated structural conditions and changes. Moreover, we can characterize/address vegetation recovery process from disturbances such as accumulation and fluxes of carbon stock post-disturbance.

This spetial issue will focus on the spatial and temporal characterization of vegetation degradation associated with disturbance-recovery history using optical remote sensing. . The authors can address any type of vegetation disturbance and post-disturbance change, ie, recovery using either wall-to-wall optical sensors such as Landsat, Sentinel and MODIS, or lidar sensors (ie, GEDI and ICESat-2) or combined both sensors. Spatial and temporal dimension can be determined according to data type and availability.

Dr. Izaya Numata
Guest Editor

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Keywords

Lidar
Optical sensor
Time series
Disturbance history
Recovery
Predictive model

Published Papers (2 papers)

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Research

24 pages, 15290 KiB

Open AccessArticle

A Framework for Improving Wall-to-Wall Canopy Height Mapping by Integrating GEDI LiDAR

by Cangjiao Wang, Andrew J. Elmore, Izaya Numata, Mark A. Cochrane, Shaogang Lei, Christopher R. Hakkenberg, Yuanyuan Li, Yibo Zhao and Yu Tian

Remote Sens. 2022, 14(15), 3618; https://doi.org/10.3390/rs14153618 - 28 Jul 2022

Cited by 12 | Viewed by 2522

Abstract

Spatially continuous canopy height is a vital input for modeling forest structures and functioning. The global ecosystem dynamics investigation (GEDI) waveform can penetrate a canopy to precisely find the ground and measure canopy height, but it is spatially discontinuous over the earth’s surface. A common method to achieve wall-to-wall canopy height mapping is to integrate a set of field-measured canopy heights and spectral bands from optical and/or microwave remote sensing data as ancillary information. However, due partly to the saturation of spectral reflectance to canopy height, the product of this method may misrepresent canopy height. As a result, neither GEDI footprints nor interpolated maps using the common method can accurately produce spatially continuous canopy height maps alone. To address this issue, this study proposes a framework of point-surface fusion for canopy height mapping (FPSF-CH) that uses GEDI data to calibrate the initial wall-to-wall canopy height map derived from a sub-model of FPSF-CH. The effectiveness of the proposed FPSF-CH was validated by comparison to canopy heights derived from (1) a high-resolution canopy height model derived from airborne discrete point cloud lidar across three test sites, (2) a global canopy height product (GDAL RH95), and (3) the results of the FPSF-CH sub-model without fusing with the GEDI canopy height. The results showed that the RMSE and rRMSE of FPSF-CH were 3.82, 4.05, and 3.48 m, and 18.77, 16.24, and 13.81% across the three test sites, respectively. The FPSF-CH achieved improvement over GDAL RH95, with reductions in RMSE values of 1.28, 2.25, and 2.23 m, and reductions in rRMSE values of 6.29, 9.01, and 8.90% across the three test sites, respectively. Additionally, the better performance of the FPSF-CH compared with its sub-model further confirmed the effectiveness of integrating GEDI data for calibrating wall-to-wall canopy height mapping. The proposed FPSF-CH integrates GEDI LiDAR data to provide a new avenue for accurate wall-to-wall canopy height mapping critical to applications, such as estimations of biomass, biodiversity, and carbon stocks. Full article

(This article belongs to the Special Issue Monitoring of Forest Degradation-Recovery Based on Optical Sensors)

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20 pages, 4197 KiB

Open AccessArticle

Examining Vegetation Change and Associated Spatial Patterns in Wuyishan National Park at Different Protection Levels

by Mengzhuo Fan, Kuo Liao, Dengsheng Lu and Dengqiu Li

Remote Sens. 2022, 14(7), 1712; https://doi.org/10.3390/rs14071712 - 01 Apr 2022

Cited by 10 | Viewed by 2162

Abstract

Examining the characteristics of vegetation change and associated spatial patterns under different protection levels can provide a scientific basis for national park protection and management. Based on the dense time-series Landsat enhanced vegetation index (EVI) data between 1986 and 2020, we utilized the Wild Binary Segmentation (WBS) approach to detect spatial and temporal characteristics of abrupt, gradual, and total changes in Wuyishan National Park. The differences in vegetation change in three protection-level areas (strictly protected [Prot_s], generally protected [Prot], and non-protected [NP]) were examined, and the contributions to their spatial patterns were evaluated through Geodetector. The results showed the following: (1) The highest percentage of area without abrupt change was in Prot_s (39.89%), and the lowest percentage was in NP (17.44%). The percentage of abrupt change frequency (larger than three times) increased from 4.40% to 9.10% and 12.49% with the decreases in protection. The significance test showed that the difference in changed frequencies was not significant among these regions, but the interannual variation of abrupt change in Prot_s was significantly different from other areas. (2) The vegetation coverage of the Wuyishan National Park generally improved. The total EVI change (T_EVI) showed that the positive percentage of Prot_s and Prot was 90.43% and 91.71%, respectively, slightly higher than that of NP (88.44%). However, the mean greenness change of NP was higher than that of Prot_s and Prot. (3) The park’s EVI spatial pattern in 1986 was the strongest factor determining the EVI spatial pattern in 2020; the explanatory power reduced as the protection level decreased. The explanation power (q value) of abrupt vegetation change was lower and increased as the protection level decreased. The interaction detection showed that EVI₁₉₈₆ and T_EVI had the strongest explanatory powers, but the explanatory ability gradually weakened from 0.713 to 0.672 to 0.581 in Prot_s, Prot, and NP, respectively. This study provided a systematic analysis of vegetation changes and their impacts on spatial patterns. Full article

(This article belongs to the Special Issue Monitoring of Forest Degradation-Recovery Based on Optical Sensors)

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