scholarly journals Determining fPAR and leaf area index of several land cover classes in the Pot River and Tsitsa River catchments of the Eastern Cape, South Africa

2017 ◽  
Vol 34 (1) ◽  
pp. 33-37 ◽  
Author(s):  
Anthony R Palmer ◽  
Andiswa Finca ◽  
Sukhmani K Mantel ◽  
Onalenna Gwate ◽  
Zahn Münch ◽  
...  
Keyword(s):  
South Africa ◽  
Land Cover ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Eastern Cape ◽  
Area Index ◽  
River Catchments

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

2019 ◽  
Vol 11 (7) ◽  
pp. 829 ◽  
Author(s):  
Timothy Dube ◽  
Santa Pandit ◽  
Cletah Shoko ◽  
Abel Ramoelo ◽  
Dominic Mazvimavi ◽  
...  
Keyword(s):  
South Africa ◽  
Leaf Area Index ◽  
Leaf Area ◽  
National Park ◽  
Dry Season ◽  
Kruger National Park ◽  
Landsat 8 ◽  
Tropical Savanna ◽  
Wet Season ◽  
Area Index

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.

scholarly journals ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

2013 ◽  
Vol 6 (2) ◽  
pp. 563-582 ◽  
Author(s):  
S. Faroux ◽  
A. T. Kaptué Tchuenté ◽  
J.-L. Roujean ◽  
V. Masson ◽  
E. Martin ◽  
...  
Keyword(s):  
Land Cover ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Climate Models ◽  
Area Index ◽  
Surface Parameters ◽  
Climatic Environment

Abstract. The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and normalized difference vegetation index (NDVI) from SPOT/Vegetation (a global monitoring system of vegetation) yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 plant functional types (PFTs) representing generic vegetation types – principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land – as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere) developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.

Study of land cover classes and retrieval of leaf area index using Landsat 8 OLI data

2016 ◽  
Author(s):  
Amit K. Verma ◽  
P. K. Garg ◽  
K. S. Hari Prasad ◽  
V. K. Dadhwal
Keyword(s):  
Land Cover ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Area Index

scholarly journals Leaf Area Index Variation for Crop, Pasture, and Tree in Response to Climatic Variation in the Goulburn–Broken Catchment, Australia

2014 ◽  
Vol 15 (4) ◽  
pp. 1592-1606 ◽  
Author(s):  
Zelalem K. Tesemma ◽  
Yongping Wei ◽  
Andrew W. Western ◽  
Murray C. Peel
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Remotely Sensed ◽  
Climatic Variables ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Area Index ◽  
The Future

Abstract Previous studies have reported relationships between mean annual climatic variables and mean annual leaf area index (LAI), but the seasonal and spatial variability of this relationship for different vegetation cover types in different climate zones have rarely been explored in Australia. The authors developed simple models using remotely sensed LAI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and gridded climatic data from the Australian Water Availability Project. They were able to relate seasonal and annual LAI of three different land cover types (tree, pasture, and crop) with climatic variables for the period 2000–09 in the Goulburn–Broken catchment, Australia. Strong relationships were obtained between annual LAI of crop, pasture, and tree with annual precipitation (R2 = 0.70, 0.65, and 0.82, respectively). Monthly LAI of each land cover type also showed a strong relationship (R2 = 0.92, 0.95, and 0.95) with the difference between precipitation P and reference crop evapotranspiration (PET; P − PET) for crop, pasture, and tree. Independent model calibration and validation showed good agreement with remotely sensed MODIS LAI. The results from the application of the developed model on the future impact of climate change suggest that under all climate scenarios crop, pasture, and tree showed consistent decreases in mean annual LAI. For the future climate change scenarios considered, crop showed a decline of 7%–38%, pasture showed a decline of 5%–24%, and tree showed a decline of 2%–11% from the historical mean annual. These results can be used to assess the impacts of future climatic and land cover changes on water resources by coupling them with hydrological models.

scholarly journals ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1-km resolution based on satellite information for use in land surface, meteorological and climate models

2012 ◽  
Vol 5 (4) ◽  
pp. 3573-3620 ◽  
Author(s):  
S. Faroux ◽  
A. T. Kaptué Tchuenté ◽  
J.-L. Roujean ◽  
V. Masson ◽  
E. Martin ◽  
...  
Keyword(s):  
Land Cover ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Urban Areas ◽  
Climate Models ◽  
Weather Forecasting ◽  
Area Index ◽  
Surface Parameters ◽  
Climatic Environment

Abstract. The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1-km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and NDVI from SPOT/Vegetation yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 Plant Functional Types (PFTs) representing generic vegetation types – principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land – as incorporated by the SVAT model ISBA developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.

scholarly journals A Sensitivity Assessment of COSMO-CLM to Different Land Cover Schemes in Convection-Permitting Climate Simulations over Europe

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1595
Author(s):  
Mingyue Zhang ◽  
Merja H. Tölle ◽  
Eva Hartmann ◽  
Elena Xoplaki ◽  
Jürg Luterbacher
Keyword(s):  
Land Cover ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Regional Climate ◽  
Plant Cover ◽  
Horizontal Resolution ◽  
Area Index ◽  
Climate Simulations ◽  
Simulation Results ◽  
Land Cover Maps

The question of how sensitive the regional and local climates are to different land cover maps and fractions is important, as land cover affects the atmospheric circulation via its influence on heat, moisture, and momentum transfer, as well as the chemical composition of the atmosphere. In this study, we used three independent land cover data sets, GlobCover 2009, GLC2000 and ESACCI-LC, as the lower boundary of the regional climate model COSMO-CLM (Consortium for Small Scale Modeling in Climate Mode, v5.0-clm15) to perform convection-permitting regional climate simulations over the large part of Europe covering the years 1999 and 2000 at a 0.0275° horizontal resolution. We studied how the sensitivity of the impacts on regional and local climates is represented by different land cover maps and fractions, especially between warm (summer) and cold (winter) seasons. We show that the simulated regional climate is sensitive to different land cover maps and fractions. The simulated temperature and observational data are generally in good agreement, though with differences between the seasons. In comparison to winter, the summer simulations are more heterogeneous across the study region. The largest deviation is found for the alpine area (−3 to +3 °C), which might be among different reasons due to different classification systems in land cover maps and orographical aspects in the COSMO-CLM model. The leaf area index and plant cover also showed different responses based on various land cover types, especially over the area with high vegetation coverage. While relating the differences of land cover fractions and the COSMO-CLM simulation results (the leaf area index, and plant coverage) respectively, the differences in land cover fractions did not necessarily lead to corresponding bias in the simulation results. We finally provide a comparative analysis of how sensitive the simulation outputs (temperature, leaf area index, plant cover) are related to different land cover maps and fractions. The different regional representations of COSMO-CLM indicate that the soil moisture, atmospheric circulation, evaporative demand, elevation, and snow cover schemes need to be considered in the regional climate simulation with a high horizontal resolution.

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