Numerical Assessments of Leaf Area Index in Tropical Savanna Rangelands, South Africa Using Landsat 8 OLI Derived Metrics and In-Situ Measurements

Knowledge on rangeland condition, productivity patterns and possible thresholds of potential concern, as well as the escalation of risks in the face of climate change and variability over savanna grasslands is essential for wildlife/livestock management purposes. The estimation of leaf area index (LAI) in tropical savanna ecosystems is therefore fundamental for the proper planning and management of this natural capital. In this study, we assess the spatio-temporal seasonal LAI dynamics (dry and wet seasons) as a proxy for rangeland condition and productivity in the Kruger National Park (KNP), South Africa. The 30 m Landsat 8 Operational Land Imager (OLI) spectral bands, derived vegetation indices and a non-parametric approach (i.e., random forest, RF) were used to assess dry and wet season LAI condition and variability in the KNP. The results showed that RF optimization enhanced the model performance in estimating LAI. Moderately high accuracies were observed for the dry season (R2 of 0.63–0.72 and average RMSE of 0.60 m2/m2) and wet season (0.62–0.63 and 0.79 m2/m2). Derived thematic maps demonstrated that the park had high LAI estimates during the wet season when compared to the dry season. On average, LAI estimates ranged between 3 and 7 m2/m2 during the wet season, whereas for the dry season most parts of the park had LAI estimates ranging between 0.00 and 3.5 m2/m2. The findings indicate that Kruger National Park had high levels of productivity during the wet season monitoring period. Overall, this work shows the unique potential of Landsat 8-derived metrics in assessing LAI as a proxy for tropical savanna rangelands productivity. The result is relevant for wildlife management and habitat assessment and monitoring.

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Above-Ground Net Primary Production, Leaf Area Index, and Nitrogen Dynamics in Early Post-Fire Vegetation, Yellowstone National Park

The UW National Parks Service Research Station Annual Reports ◽

10.13001/uwnpsrc.1997.3341 ◽

1997 ◽

Vol 21 ◽

pp. 130-134

Author(s):

Monica Turner ◽

Rebecca Reed ◽

William Romme ◽

Mary Finley ◽

Dennis Knight

Keyword(s):

Primary Production ◽

Leaf Area Index ◽

Leaf Area ◽

Yellowstone National Park ◽

National Park ◽

Net Primary Production ◽

Lodgepole Pine ◽

Fire Severity ◽

Ecosystem Processes ◽

Area Index

The 1988 fires in Yellowstone National Park (YNP), Wyoming, affected >250,000 ha, creating a striking mosaic of burn severities across the landscape which is likely to influence ecological processes for decades to come (Christensen et al. 1989, Knight and Wallace 1989, Turner et al.1994). Substantial spatial heterogeneity in early post-fire succession has been observed in the decade since the fires, resulting largely from spatial variation in fire severity and in the availability of lodgepole pine (Pinus contorta var. latifolia) seeds in or near the burned area (Anderson and Romme 1991, Tinker et al. 1994, Turner et al. 1997). PostÂfire vegetation now includes pine stands ranging from relatively low to extremely high pine sapling density (ca 10,000 to nearly 100,000 stems ha-1) as well as non-forest or marginally forested vegetation across the Yellowstone landscape may influence ecosystem processes related to energy flow and biogeochemisty. We also are interested in how quickly these processes may return to their preÂ disturbance characteristics. In this pilot study, we began to address these general questions by examining the variation in above-ground net primary production (ANPP), leaf area index (LAI) of tree (lodgepole pine) and herbaceous components, and rates of nitrogen mineralization and loss in successional stands 9 years after the fires. ANPP measures the cumulative new biomass generated over a given period of time, and is a fundamental ecosystem property often used to compare ecosystems (Carpenter 1998). Leaf area (typically expressed as leaf area index [LAI], i.e., leaf area per unit ground surface area) influences rates of two fundamental ecosystem processes -Â primary productivity and transpiration -- and is communities (

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

Remote Sensing ◽

10.3390/rs12193121 ◽

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.

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Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data

Remote Sensing ◽

10.3390/rs11212517 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2517 ◽

Cited By ~ 2

Author(s):

Huaan Jin ◽

Weixing Xu ◽

Ainong Li ◽

Xinyao Xie ◽

Zhengjian Zhang ◽

...

Keyword(s):

Time Series ◽

Leaf Area Index ◽

Leaf Area ◽

Landsat 8 ◽

Area Index ◽

Spatio Temporal ◽

Fine Resolution ◽

Modis Lai ◽

Temporal Profiles ◽

Reflectance Data

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.

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Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat

Journal of Integrative Agriculture ◽

10.1016/s2095-3119(15)61293-x ◽

2017 ◽

Vol 16 (2) ◽

pp. 266-285 ◽

Cited By ~ 34

Author(s):

He LI ◽

Zhong-xin CHEN ◽

Zhi-wei JIANG ◽

Wen-bin WU ◽

Jian-qiang REN ◽

...

Keyword(s):

Comparative Analysis ◽

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Landsat 8 ◽

Area Index

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Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2016.02.001 ◽

2016 ◽

Vol 49 ◽

pp. 63-74 ◽

Cited By ~ 62

Author(s):

Taifeng Dong ◽

Jiangui Liu ◽

Budong Qian ◽

Ting Zhao ◽

Qi Jing ◽

...

Keyword(s):

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Landsat 8 ◽

Area Index ◽

Modis Data

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Effect of residual leaf area index on spatial components of Tifton 85 pastures and ingestive behaviour of sheep

Animal Production Science ◽

10.1071/an15087 ◽

2017 ◽

Vol 57 (5) ◽

pp. 903 ◽

Cited By ~ 4

Author(s):

W. L. Silva ◽

J. P. R. Costa ◽

G. P. Caputti ◽

A. L. S. Valente ◽

D. Tsuzukibashi ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Rainy Season ◽

Structural Characteristics ◽

Light Interception ◽

Dry Season ◽

Area Index ◽

Dead Material ◽

Point Quadrat ◽

Ingestive Behaviour

This study compared the effect of residual leaf area index (rLAI) on the spatial distribution of morphological components of Tifton 85 (Cynodon spp.) pastures and the ingestive behaviour of grazing sheep. Also, it was investigated whether any specific correlation could be found between pasture structural characteristics and sheep ingestive behaviour. Four rLAI treatments (0.8; 1.4; 2.0 and 2.6) with four replications were evaluated per period. Sheep grazed under rotational stocking management and they grazed for 4 days in each pasture while pasture regrowth period was determined by the 95% light interception requirement. Pasture structure was evaluated using inclined point-quadrat, LAI estimates, light interception and leaf : stem ratio. The 2.6 rLAI yielded the highest proportion of dead material in the lower canopy. In the post-grazing period the proportion of leaves increased with increasing rLAI, especially on the canopy surface during the rainy season. In the pre-grazing average pasture height ranged between 19 and 26 cm with dead material and stem observed up to the canopy surface in the dry season. The animals grazed longer on the last day (89.72%) compared with the first day (80.25%) in the dry season. However, they spent less time (11.45%) ruminating in the dry season compared with the rainy season (15.38%), regardless of the grazing day. Grazing time decreased and rumination time increased as rLAI increased. Sheep grazing time correlated negatively with pasture height, before and after grazing. The sheep tend to graze longer on Tifton 85 pastures when rLAI was lower and forage supply was possibly less as on the last grazing day and in the dry season.

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The contribution of fire research to fire management: a critical review of a long-term experiment in the Kruger National Park, South Africa

International Journal of Wildland Fire ◽

10.1071/wf06115 ◽

2007 ◽

Vol 16 (5) ◽

pp. 519 ◽

Cited By ~ 61

Author(s):

Brian W. van Wilgen ◽

Navashni Govender ◽

Harry C. Biggs

Keyword(s):

South Africa ◽

National Park ◽

Fire Management ◽

Basic Research ◽

Kruger National Park ◽

Management Policy ◽

Wet Season ◽

Long Term Experiment ◽

Faunal Communities

The present paper reviews a long-term fire experiment in the Kruger National Park, South Africa, established in 1954 to support fire management. The paper’s goals are: (1) to assess learning, with a focus on relevance for fire management; (2) to examine how findings influenced changes in fire management; and (3) to reflect on the experiment’s future. Results show that fire treatments affected vegetation structure and biomass more than species composition. Effects on vegetation were most marked in extreme treatments (annual burning, burning in the summer wet season, or long periods of fire exclusion), and were greater in areas of higher rainfall. Faunal communities and soil physiology were largely unaffected by fire. Since the inception of the experiment, paradigms in savanna ecology have changed to encompass heterogeneity and variability. The design of the experiment, reflecting the understanding of the 1950s, does not cater for variability, and as a result, the experiment had little direct influence on changes in management policy. Notwithstanding this, managers accept that basic research influences the understanding of fundamental ecosystem function, and they recognise that it promotes appropriate adaptive management by contributing to predictive understanding. This has been a major reason for maintaining the experiment for over 50 years.

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