Earth Observation models help management of tropical dry savannah forests in the Okavango-Zambezi transfrontier conservation zone (KAZA) region of Southern Africa.

2020 ◽  
Author(s):  
Ruusa David ◽  
Daniel Donoghue ◽  
Nick Rosser
Keyword(s):  
Time Series ◽  
Forest Area ◽  
Human Populations ◽  
Landsat 8 ◽  
Detailed Knowledge ◽  
Above Ground Biomass ◽  
Conservation Area ◽  
Ground Biomass ◽  
Time Series Modelling ◽  
Transfrontier Conservation

<p>The Kavango Zambezi Transfrontier Conservation Area (KAZA) is the World’s largest conservation area with an enclosed area the size of Sweden (519,912 km<sup>2</sup>), and is characterized by savannah forest, woodland and protected lands. KAZA is situated at the heart of the area most vulnerable to climate change in Africa, and forest loss and degradation are major concerns which directly impact wildlife species distributions and a growing human populations. In particular, detailed knowledge of current vegetation density change and forest area estimates throughout the conservation area is sorely missing, which hampers all efforts to mitigate the threats against KAZA and its unique ecosystems. A combination of remotely sensed data and plot-based estimates can provide forest area estimates and above ground biomass (AGB). Previous AGB mapping efforts in Africa focused on tropical humid forests, with little attention on tropical and subtropical savannah forest. The aim of the current study was to establish a link between remote sensing spectral data derived from Landsat 8 and ground characteristics to improve precision of AGB and forest area estimates in savannah forest. We used 114 sample plots distributed on 6 clusters collected over the 2019 winter growing season in Chobe National Park of Botswana and Landsat 8 spectral variables.</p><p>Restricting analysis to sampling dates, before the onset of fire burning and leaf yellowing resulted in increased estimation accuracy. We found a linear relationship between above ground biomass and Landsat 8 derived spectral variables (p < 0.001 and p < 0.005). The normalized difference vegetation index (NDVI) and Green-Red Difference Index (GRVI) exhibited a strong correlation with AGB than other indices (R2 = 0.76) and (R<sup>2</sup> = 0.67), respectively. An improvement in the correlation is seen when AGB (t/ha) and variables relationship is performed in the woodland/forest cluster (n=74), excluding the shrubland and grassland. The AGB of savannah forest in the study area based on spatial analysis was 111.6 Mg/ha. A root-mean-square error (RMSE) value from predicted and observed AGB was 25.6 Mg/ha. The high total AGB value from savannah forest in the study area highlight the importance of the savannah-forest mosaic as a biomass storage and carbon pool. Overall, spectral variables and indices, particularly the NDVI and GRVI and Landsat 8 band 5 (NIR), would be useful predictors and suitable auxiliary information of AGB in the savannah forest. The results of this study show that taking into account stratification/clustering of different vegetation types and senescence period can greatly increase the accuracy of AGB estimation. This results will allow us to build new models to quantify savannah forest change and long-term trends using Landsat time series from 1980 to 2019. Time series modelling will help inform how changing climate threaten the biodiversity of the KAZA region and be able to respond to these threats with appropriate, evidence-based strategies and measures.</p>

scholarly journals Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

2020 ◽  
Vol 12 (19) ◽  
pp. 3121
Author(s):  
Roya Mourad ◽  
Hadi Jaafar ◽  
Martha Anderson ◽  
Feng Gao
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Above Ground Biomass ◽  
Area Index ◽  
Ground Biomass ◽  
Semi Arid ◽  
Sentinel 2

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

scholarly journals The Role of Time-Series L-Band SAR and GEDI in Mapping Sub-Tropical Above-Ground Biomass

2021 ◽  
Vol 9 ◽  
Author(s):  
Unmesh Khati ◽  
Marco Lavalle ◽  
Gulab Singh
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Field Data ◽  
Large Scale ◽  
Test Site ◽  
Above Ground Biomass ◽  
Retrieval Accuracy ◽  
Ground Biomass ◽  
L Band ◽  
Field Plots

Physics-based algorithms estimating large-scale forest above-ground biomass (AGB) from synthetic aperture radar (SAR) data generally use airborne laser scanning (ALS) or grid of national forest inventory (NFI) to reduce uncertainties in the model calibration. This study assesses the potential of multitemporal L-band ALOS-2/PALSAR-2 data to improve forest AGB estimation using the three-parameter water cloud model (WCM) trained with field data from relatively small (0.1 ha) plots. The major objective is to assess the impact of the high uncertainties in field inventory data due to relatively smaller plot size and temporal gap between acquisitions and ground truth on the AGB estimation. This study analyzes a time series of twenty-three ALOS-2 dual-polarized images spanning 5 years acquired under different weather and soil moisture conditions over a subtropical forest test site in India. The WCM model is trained and validated on individual acquisitions to retrieve forest AGB. The accuracy of the generated AGB products is quantified using the root mean square error (RMSE). Further, we use a multitemporal AGB retrieval approach to improve the accuracy of the estimated AGB. Changes in precipitation and soil moisture affect the AGB retrieval accuracy from individual acquisitions; however, using multitemporal data, these effects are mitigated. Using a multitemporal AGB retrieval strategy, the accuracy improves by 15% (55 Mg/ha RMSE) for all field plots and by 21% (39 Mg/ha RMSE) for forests with AGB less than 100 Mg/ha. The analysis shows that any ten multitemporal acquisitions spanning 5 years are sufficient for improving AGB retrieval accuracy over the considered test site. Furthermore, we use allometry from colocated field plots and Global Ecosystem Dynamics Investigation (GEDI) L2A height metrics to produce GEDI-derived AGB estimates. Despite the limited co-location of GEDI and field data over our study area, within the period of interest, the preliminary analysis shows the potential of jointly using the GEDI-derived AGB and multi-temporal ALOS-2 data for large-scale AGB retrieval.

Forest above-ground biomass estimates across three decades from spaceborne scatterometer observations

2020 ◽  
Author(s):  
Maurizio Santoro ◽  
Oliver Cartus ◽  
Nuno Carvalhais ◽  
Simon Besnard ◽  
Naixin Fan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Spatial Resolution ◽  
Numerical Models ◽  
Steady Increase ◽  
Above Ground Biomass ◽  
Continuous Increase ◽  
Ground Biomass ◽  
Multiple Observations ◽  
C Cycle

<p>The large uncertainty characterizing the terrestrial carbon (C) cycle is a consequence of the sparse and irregular observations on the ground. In terms of observations, spaceborne remote sensing has been achieving global, repeated coverages of the Earth since the late 1970s, with a continuous increase in terms of density of observations in time and spatial resolution, thus potentially qualifying as data source to fill such gap in knowledge. Above-ground biomass is a baseline for quantifying the terrestrial C pool; however, remote sensing observations do not measure the organic mass of vegetation. Above-ground biomass (AGB) of forests can only be inferred by inverting numerical models relating and combining multiple remote sensing observations. One of the longest time record of observations from space is represented by the backscattered intensity from the European Remote Sensing Wind Scatterometer (ERS WindScat) and the MetOp Advanced Scatterometer (ASCAT), both operating at C-band (wavelength of 6 cm). An almost unbroken time series of backscatter observations at 0.25° spatial resolution exists since 1991 and data continuity is guaranteed in the next decades. In spite of the weak sensitivity of C-band backscatter to AGB, wall-to-wall estimates of AGB have been derived from high-resolution SAR observations by exploiting multiple observations acquired in a relatively short time period  (Santoro et al., Rem. Sens. Env., 2011; Santoro et al., Rem. Sens. Env., 2015). We have now applied this approach to generate a global time series of AGB estimates for each year between 1992 and 2018 from the C-band scatterometer data at 0.25° spatial resolution. The spatial patterns of AGB match known patterns from in situ records and other remote sensing datasets. The uncertainty of our AGB estimates is between 30% and 40% of the estimated value at the pixel level, providing strong confidence in multi-decadal AGB trends. We identify a constant increase of biomass across most boreal and temperate forests of the northern hemisphere. In contrast, we detect severe loss of biomass throughout the wet tropics during the 1990s and the beginning of the 2000 decade in consequence of massive deforestation. This loss in biomass is followed by a steady increase during the 2000s and the beginning of the most recent decade, coming more recently into saturation. Overall, we find that the global AGB density at 0.25° steadily increased by 9% from 71.8 Mg ha<sup>-1</sup> Pg in the 1990s to 78.1 Mg ha<sup>-1</sup> in the 2010s. Combining our AGB density estimates with the annual maps of the Climate Change Initiative (CCI) Land Cover dataset, we show that total AGB in forests decreased slightly from 566 Pg in the 1990s to 560 Pg in the 2000s, then increased to 593 Pg in the 2010s, resulting in an almost 5% net increase during the last three decades.</p>

scholarly journals Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor

2018 ◽  
Vol 10 (11) ◽  
pp. 1850 ◽  
Author(s):  
Michael Schultz ◽  
Aurélie Shapiro ◽  
Jan Clevers ◽  
Craig Beech ◽  
Martin Herold
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Land Cover ◽  
Forest Cover ◽  
Tree Cover ◽  
Series Data ◽  
Conservation Area ◽  
Vegetation Degradation ◽  
Transfrontier Conservation ◽  
Transfrontier Conservation Area

Forest cover and vegetation degradation was monitored across the Kavango-Zambezi Transfrontier Conservation Area (KAZA) in southern Africa and the performance of three different methods in detecting degradation was assessed using reference data. Breaks for Additive Season and Trend (BFAST) Monitor was used to identify potential forest cover and vegetation degradation using Landsat Normalized Difference Moisture Index (NDMI) time series data. Parametric probability-based magnitude thresholds, non-parametric random forest in conjunction with Soil-Adjusted Vegetation Index (SAVI) time series, and the combination of both methods were evaluated for their suitability to detect degradation for six land cover classes ranging from closed canopy forest to open grassland. The performance of degradation detection was largely dependent on tree cover and vegetation density. Satisfactory accuracies were obtained for closed woodland (user’s accuracy 87%, producer’s accuracy 71%) and closed forest (user’s accuracy 92%, producer’s accuracy 90%), with lower accuracies for open canopies. The performance of the three methods was more similar for closed canopies and differed for land cover classes with open canopies. Highest user’s accuracy was achieved when methods were combined, and the best performance for producer’s accuracy was obtained when random forest was used.

scholarly journals Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests

2017 ◽  
Vol 406 ◽  
pp. 163-171 ◽  
Author(s):  
Mui-How Phua ◽  
Shazrul Azwan Johari ◽  
Ong Cieh Wong ◽  
Keiko Ioki ◽  
Maznah Mahali ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Landsat 8 ◽  
Above Ground Biomass ◽  
Landsat 8 Oli ◽  
Tropical Lowland ◽  
Ground Biomass ◽  
Airborne Lidar Data
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