scholarly journals Democratic Republic of the Congo Tropical Forest Canopy Height and Aboveground Biomass Estimation with Landsat-8 Operational Land Imager (OLI) and Airborne LiDAR Data: The Effect of Seasonal Landsat Image Selection

2020 ◽  
Vol 12 (9) ◽  
pp. 1360
Author(s):  
Herve B. Kashongwe ◽  
David P. Roy ◽  
Jean Robert B. Bwangoy
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Democratic Republic ◽  
Tree Height ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Landsat Image ◽  
Landsat 8 ◽  
Republic Of The Congo ◽  
The Mean

Inventories of tropical forest aboveground biomass (AGB) are often imprecise and sparse. Increasingly, airborne Light Detection And Ranging (LiDAR) and satellite optical wavelength sensor data are used to map tree height and to estimate AGB. In the tropics, cloud cover is particularly prevalent and so several years of satellite observations must be considered. This may reduce mapping accuracy because of seasonal and inter-annual changes in the forest reflectance. In this paper, the sensitivity of airborne LiDAR and Landsat-8 Operational Land Imager (OLI) based dominant canopy height and AGB 30 m mapping is assessed with respect to the season of Landsat acquisition for a ~10,000 Km2 tropical forest area in the Democratic Republic of the Congo. A random forest regression estimator is used to predict and assess the 30 m dominant canopy height using LiDAR derived test and training data. The AGB is mapped using an allometric model parameterized with the dominant canopy height and is assessed by comparison with field based 30 m AGB estimates. Experiments are undertaken independently using (i) only a wet season Landsat-8 image, (ii) only a dry season Landsat-8 image, and (iii) both Landsat-8 images. At the study area level there is little reported sensitivity to the season of Landsat image used. The mean dominant canopy height and AGB values are similar between seasons, within 0.19 m and 5 Mg ha−1, respectively. The mapping results are improved when both Landsat-8 images are used with Root Mean Square Error (RMSE) values that correspond to 18.8% of the mean study area mapped tree height (20.4 m) and to 41% of the mean study area mapped AGB (204 Mg ha−1). The mean study area mapped AGB is similar to that reported in other Congo Basin forest studies. The results of this detailed study are illustrated and the implications for tropical forest tree height and AGB mapping are discussed.

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 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 Evaluation of an Acacia mangium Provenance Trial after Seven Years, on the Ibi - Batéké Plateau, Democratic Republic of the Congo

2021 ◽  
Author(s):  
Etienne Yusufu Kachaka ◽  
Vincent Poirier ◽  
Alison D. Munson ◽  
Damase P. Khasa
Keyword(s):  
Democratic Republic ◽  
Direct Method ◽  
Acacia Mangium ◽  
Tree Height ◽  
The Philippines ◽  
Soil C ◽  
Carbon And Nitrogen ◽  
Nitrogen Concentrations ◽  
Republic Of The Congo ◽  
C And N

This study reports on the evaluation of 46 Acacia mangium provenances and varieties, which were planted in 2006 on the Ibi-Batéké Plateau, Democratic Republic of the Congo. After seven years, tree height and diameter, biomass and carbon stocks of the 46 Acacia mangium provenances, together with soil carbon and nitrogen concentrations, were compared with savannah soils in which Acacia was no present. Heights and diameters of 20 to 25 trees per provenance were measured. Carbon in the biomass was determined by the direct method. In total, 25 trees were harvested and weighed for each carbon compartment (leaves, branches, litter, trunks and roots). Ninety soil samples were collected at three different depths in the provenance plots and on the savannah and analyzed for their C and N concentrations. There were differences in height and diameter growth and in accumulated carbon among trees of different origins (provenances). Finally, soil C and N differed under different provenances, and with depth. Carbon and nitrogen tended to decrease with depth. The results of the study revealed better performance for provenances originating from Papua New Guinea, Australia, Malaysia, Vietnam, China, Fiji and the Philippines.

scholarly journals Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example

2020 ◽  
Vol 12 (11) ◽  
pp. 1824 ◽  
Author(s):  
Lana L. Narine ◽  
Sorin C. Popescu ◽  
Lonesome Malambo
Keyword(s):  
Land Cover ◽  
Aboveground Biomass ◽  
Canopy Cover ◽  
Canopy Height ◽  
Landsat 8 ◽  
Ecosystem Structure ◽  
Laser Altimeter ◽  
Weak Beam ◽  
Using Data ◽  
Forest Aboveground Biomass

National Aeronautics and Space Administration’s (NASA’s) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides rich insights over the Earth’s surface through elevation data collected by its Advanced Topographic Laser Altimeter System (ATLAS) since its launch in September 2018. While this mission is primarily aimed at capturing ice measurements, ICESat-2 also provides data over vegetated areas, offering the capability to gain insights into ecosystem structure and the potential to contribute to the sustainable management of forests. This study involved an examination of the utility of ICESat-2 for estimating forest aboveground biomass (AGB). The objectives of this study were to: (1) investigate the use of canopy metrics for estimating AGB, using data extracted from an ICESat-2 transect over forests in south-east Texas; (2) compare the accuracy for estimating AGB using data from the strong beam and weak beam; and (3) upscale predicted AGB estimates using variables from Landsat multispectral imagery and land cover and canopy cover maps, to generate a 30 m spatial resolution AGB map. Methods previously developed with simulated ICESat-2 data over Sam Houston National Forest (SHNF) in southeast Texas were adapted using actual data from an adjacent ICESat-2 transect over similar vegetation conditions. Custom noise filtering and photon classification algorithms were applied to ICESat-2’s geolocated photon data (ATL03) for one beam pair, consisting of a strong and weak beam, and canopy height estimates were retrieved. Canopy height parameters were extracted from 100 m segments in the along-track direction for estimating AGB, using regression analysis. ICESat-2-derived AGB estimates were then extrapolated to develop a 30 m AGB map for the study area, using vegetation indices from Landsat 8 Operational Land Imager (OLI), National Land Cover Database (NLCD) landcover and canopy cover, with random forests (RF). The AGB estimation models used few canopy parameters and suggest the possibility for applying well-developed methods for modeling AGB with airborne light detection and ranging (lidar) data, using processed ICESat-2 data. The final regression model achieved a R2 and root mean square error (RMSE) value of 0.62 and 24.63 Mg/ha for estimating AGB and RF model evaluation with a separate test set yielded a R2 of 0.58 and RMSE of 23.89 Mg/ha. Findings provide an initial look at the ability of ICESat-2 to estimate AGB and serve as a basis for further upscaling efforts.

scholarly journals Four new species of the sac spider genus Planochelas Lyle & Haddad, 2009 (Araneae, Trachelidae) from central and southern Africa

2019 ◽  
Vol 60 (2) ◽  
pp. 147-164 ◽  
Author(s):  
Thembile T. Khoza ◽  
Robin Lyle
Keyword(s):  
South Africa ◽  
New Species ◽  
Tropical Forest ◽  
Southern Africa ◽  
Democratic Republic ◽  
Distribution Map ◽  
Number Of Species ◽  
Afrotropical Region ◽  
Canopy Fogging ◽  
Republic Of The Congo

The genus Planochelas Lyle & Haddad, 2009 is endemic to the Afrotropical region. Members of the genus are very small, arboreal sac spiders. They are mainly collected by canopy fogging in tropical forest and savanna. In this study, four new species of Planochelas are described: P.brevissp. nov., P.jocqueisp. nov. (Democratic Republic of the Congo) and P.haddadisp. nov., P.neethlingisp. nov. (South Africa). An updated key to the genus is provided, and the new species are illustrated by photographs and drawings. A distribution map for the genus is provided. This paper increases the number of species in the genus to seven.

scholarly journals The impact ofInfection Prevention and control (IPC) bundle implementationon IPC compliance during the Ebola virus outbreak in Mbandaka/Democratic Republic of the Congo: a before and after design

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e029717 ◽  
Author(s):  
Kevin Ousman ◽  
Landry Kabego ◽  
Ambrose Talisuna ◽  
Janet Diaz ◽  
John Mbuyi ◽  
...  
Keyword(s):  
Prevention And Control ◽  
Ebola Virus ◽  
Democratic Republic ◽  
P Value ◽  
Before And After ◽  
Republic Of The Congo ◽  
The Mean ◽  
And Control ◽  
The Impact

ObjectivesTo assess the impact of refresher training of healthcare workers (HCWs) in infection prevention and control (IPC), ensuring consistent adequate supplies and availability of IPC kits and carrying out weekly monitoring of IPC performance in healthcare facilities (HCFs)DesignThis was a before and after comparison studySettingsThis study was conducted from June to July 2018 during an Ebola virus disease (EVD) outbreak in Equateur Province in the Democratic Republic of the Congo (DRC).Participants48 HCFsInterventionsHCWs capacity building in basic IPC, IPC kit donation and IPC mentoring.Primary outcome measuresIPC scoreResults48 HCFs were evaluated and 878 HCWs were trained, of whom 437 were women and 441 were men. The mean IPC score at baseline was modestly higher in hospitals (8%) compared with medical centres (4%) and health centres (4%), respectively. The mean IPC score at follow-up significantly increased to 50% in hospitals, 39% in medical centres and 36% in health centres (p value<0.001). The aggregate mean IPC score at baseline for all HCFs, combined was 4.41% and at follow-up it was 39.51% with a mean difference of 35.08% (p-value<0.001).ConclusionsImplementation of HCW capacity building in IPC, IPC kit donation to HCF and mentoring in IPC improved IPC compliance during the ninth EVD outbreak in the DRC.

scholarly journals Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data

2020 ◽  
Vol 12 (9) ◽  
pp. 1498 ◽  
Author(s):  
Franciel Eduardo Rex ◽  
Carlos Alberto Silva ◽  
Ana Paula Dalla Corte ◽  
Carine Klauberg ◽  
Midhun Mohan ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Ordinary Least Squares ◽  
Forest Growth ◽  
Selective Logging ◽  
Airborne Lidar ◽  
Support Vector ◽  
Lidar Data ◽  
Field Plots ◽  
Forest Aboveground Biomass

Accurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) in a selectively logged tropical forest in eastern Amazonia. Specifically, we compare results from a suite of different modelling methods with extensive field data. The calibration AGB values were derived from 85 square field plots sized 50 × 50 m field plots established in 2014 and which were estimated using airborne LiDAR data acquired in 2012, 2014, and 2017. LiDAR-derived metrics were selected based upon Principal Component Analysis (PCA) and used to estimate AGB stock and change. The statistical approaches were: ordinary least squares regression (OLS), and nine machine learning approaches: random forest (RF), several variations of k-nearest neighbour (k-NN), support vector machine (SVM), and artificial neural networks (ANN). Leave-one-out cross-validation (LOOCV) was used to compare performance based upon root mean square error (RMSE) and mean difference (MD). The results show that OLS had the best performance with an RMSE of 46.94 Mg/ha (19.7%) and R² = 0.70. RF, SVM, and ANN were adequate, and all approaches showed RMSE ≤54.48 Mg/ha (22.89%). Models derived from k-NN variations all showed RMSE ≥64.61 Mg/ha (27.09%). The OLS model was thus selected to map AGB across the time-series. The mean (±sd—standard deviation) predicted AGB stock at the landscape level was 229.10 (±232.13) Mg/ha in 2012, 258.18 (±106.53) in 2014, and 240.34 (sd ± 177.00) Mg/ha in 2017, showing the effect of forest growth in the first period and logging in the second period. In most cases, unlogged areas showed higher AGB stocks than logged areas. Our methods showed an increase in AGB in unlogged areas and detected small changes from reduced-impact logging (RIL) activities occurring after 2012. We also detected that the AGB increase in areas logged before 2012 was higher than in unlogged areas. Based on our findings, we expect our study could serve as a basis for programs such as REDD+ and assist in detecting and understanding AGB changes caused by selective logging activities in tropical forests.

scholarly journals ABOVEGROUND BIOMASS ESTIMATION USING RECONSTRUCTED FEATURE OF AIRBORNE DISCRETE-RETURN LIDAR BY AUTO-ENCODER NEURAL NETWORK

2018 ◽  
Vol XLII-3 ◽  
pp. 943-948
Author(s):  
T. Li ◽  
Z. Wang ◽  
J. Peng
Keyword(s):  
Neural Network ◽  
Aboveground Biomass ◽  
Point Cloud ◽  
Forest Canopy ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Height Distribution ◽  
Prediction Ability ◽  
Great Ability ◽  
Canopy Area

Aboveground biomass (AGB) estimation is critical for quantifying carbon stocks and essential for evaluating carbon cycle. In recent years, airborne LiDAR shows its great ability for highly-precision AGB estimation. Most of the researches estimate AGB by the feature metrics extracted from the canopy height distribution of the point cloud which calculated based on precise digital terrain model (DTM). However, if forest canopy density is high, the probability of the LiDAR signal penetrating the canopy is lower, resulting in ground points is not enough to establish DTM. Then the distribution of forest canopy height is imprecise and some critical feature metrics which have a strong correlation with biomass such as percentiles, maximums, means and standard deviations of canopy point cloud can hardly be extracted correctly. In order to address this issue, we propose a strategy of first reconstructing LiDAR feature metrics through Auto-Encoder neural network and then using the reconstructed feature metrics to estimate AGB. To assess the prediction ability of the reconstructed feature metrics, both original and reconstructed feature metrics were regressed against field-observed AGB using the multiple stepwise regression (MS) and the partial least squares regression (PLS) respectively. The results showed that the estimation model using reconstructed feature metrics improved R<sup>2</sup> by 5.44&amp;thinsp;%, 18.09&amp;thinsp;%, decreased RMSE value by 10.06&amp;thinsp;%, 22.13&amp;thinsp;% and reduced RMSE<sub>cv</sub> by 10.00&amp;thinsp;%, 21.70&amp;thinsp;% for AGB, respectively. Therefore, reconstructing LiDAR point feature metrics has potential for addressing AGB estimation challenge in dense canopy area.

scholarly journals Neuromeningeal cryptococcosis in HIV-infected patients in Lubumbashi, Democratic Republic of the Congo

2021 ◽  
Vol 11 (3) ◽  
pp. 73-77
Author(s):  
Joe Kabongo Katabwa ◽  
Olivier Mukuku ◽  
Guy Kanja Lwamba ◽  
Stanislas Okitotsho Wembonyama
Keyword(s):  
Opportunistic Infection ◽  
Case Fatality Rate ◽  
Democratic Republic ◽  
Case Fatality ◽  
Hiv Positive ◽  
Fatality Rate ◽  
Clinical Symptomatology ◽  
Appropriate Treatment ◽  
Republic Of The Congo ◽  
The Mean

Introduction: Neuromeningeal cryptococcosis (NMC) is a severe and fatal opportunistic infection. Lethality is higher in the absence of treatment, especially in HIV co-infection. The objective of the present study was to determine the prevalence, epidemiological, clinical, biological, and therapeutic features as well as the outcome of NMC in HIV-infected patients. Methods: This is a retrospective study of 108 cases of NMC diagnosed in HIV-infected patients. Data were collected over 36 months (from January 2015 to December 2017) at the HIV/AIDS Center of Excellence in Lubumbashi (Democratic Republic of the Congo). Results: The overall prevalence of NMC is 2.5%. The mean age of the patients was 41.5±13.1 years, with 72.2% aged less than 50 years. The main clinical symptomatology was headache (100%) and fever (100%). The main cytochemical CSF abnormalities were hyperproteinorachia (91.9%), hypoglycorachia (94%) and hyper-lymphocytosis (98.2%). The mean CD4 count was 168.7±137.1/mm3. All patients were treated with fluconazole. The overall lethality was 43.5%. Conclusion: NMC is a serious opportunistic infection in HIV-infected patients, and the case fatality rate remains unacceptable. Management of NMC in HIV-positive patients requires early diagnosis, increased access to antiretrovirals and prompt initiation of appropriate treatment.

scholarly journals Plot size effects on airborne LiDAR-derived metrics and predicted model performance of subtropical planted forest stand attributes

2021 ◽  
Author(s):  
Chungan Li
Keyword(s):  
Tree Height ◽  
Chinese Fir ◽  
Airborne Lidar ◽  
Forest Types ◽  
Field Plot ◽  
Plot Size ◽  
The Mean ◽  
Stand Attributes ◽  
Field Plots ◽  
Estimation Models

Abstract Background Field plot measurement is an essential task for forest inventory and monitoring and ecological applications based on airborne LiDAR. To optimize the field plot size and reduce cost, it is necessary to investigate the influence of field plot size on LiDAR-derived metrics and the accuracy of forest parameter estimation models. Methods A subtropical planted forest with an area of 4,770 ha was used as the study site, and 104 square plot of 900 m2 (30 m×30 m, subdivided into nine quadrats, each with an area of 100 m2 (10 m×10 m)) was divided into field plots with six different areas (100 m2, 200 m2, 300 m2, 400 m2, 600 m2 and 900 m2) by grouping quadrats. The differences in the LiDAR-derived metrics and stand attributes of different sized plots with four forest types (Chinese fir, pine, eucalyptus and broadleaf) were investigated. Through multivariate power models with stable structures, the differences in forest parameter (BA, VOL) estimation accuracies for plots with different sizes were compared. Results (1) The mean differences in LiDAR-derived metrics related to height, density and vertical structure between the plots with different sizes and the 900 m2 plot containing all forest types were very small, and when the plot size changed, these differences changed irregularly; however, the standard deviations of the differences increased rapidly with decreasing plot size. (2) There were significant differences in the mean of the maximal height of the point cloud (Hmax), density of the 75th percentile of the point cloud (dh75) and mean leaf area density (LADmean) (except for Chinese fir and eucalyptus) between the plots with different sizes and the 900 m2 plot containing all forest types; other LiDAR-derived metrics had significant differences in only some or a certain size of plots, but there was no regularity. (3) Except for the maximal tree height of the plot (Hm), the forest stand attributes, including the mean tree height (H), diameter at breast height (DBH), basal area (BA), and stand volume (VOL), of all forest types showed either no significant differences or minimal differences between plots with different sizes and the 900 m2 plot. (4) With increasing plot size, the coefficient of determination (R2) of the estimation models for VOL and BA of all forest types increased gradually, while the relative root mean square error (rRMSE) and mean prediction error (MPE) decreased gradually, and the estimation accuracy of the models improved. Conclusion Due to the heterogeneity of the vertical and horizontal forest structures, some LiDAR-derived metrics and stand parameters for field plots with different sizes varied. As the plot size increased, the variations in the independent variables (LiDAR-derived metrics) and dependent variables (stand parameters) of the estimation models decreased gradually. These changes improved the robustness and accuracy of the models. In the application of airborne LiDAR in forest inventory and monitoring, both prediction accuracy and cost should be considered. For subtropical planted forests, we preliminarily suggest the following appropriate sizes for field plots: 900 m2 for Chinese fir and pine forests, 400 m2 for eucalyptus forests and 600 m2 for broadleaf forests. However, this protocol still needs to be tested in further studies.

Sign in / Sign up

Export Citation Format

Share Document