A comparison of remote sensed semi-arid grassland vegetation anomalies detected using MODIS and Sentinel-3, with anomalies in ground-based eddy covariance flux measurements.
(2023) In Master Thesis in Geographical Information Science GISM01 20232Dept of Physical Geography and Ecosystem Science
- Abstract
- Remote sensed vegetation biophysical indicators are derived from various sensors and methods and contribute to the quantification of vegetation growth. They are used as inputs to early warning systems for crop yield estimation, thus improving crop management and food security. However, the performance of any such system is affected by the performance of the chosen remote sensed biophysical indicator. Performance based selection, requires evaluation of the performance of various remote sensed indicators, in how well each indicator represents actual vegetation growth.
Scientific Problem
Such performance evaluation has previously been done in northern-latitude focused studies, with little or none having been conducted in southern... (More) - Remote sensed vegetation biophysical indicators are derived from various sensors and methods and contribute to the quantification of vegetation growth. They are used as inputs to early warning systems for crop yield estimation, thus improving crop management and food security. However, the performance of any such system is affected by the performance of the chosen remote sensed biophysical indicator. Performance based selection, requires evaluation of the performance of various remote sensed indicators, in how well each indicator represents actual vegetation growth.
Scientific Problem
Such performance evaluation has previously been done in northern-latitude focused studies, with little or none having been conducted in southern hemisphere semi-arid grasslands specifically, such as those used for sheep grazing in the Nama-Karoo biome in South Africa.
Study Aim
The aim of this study is to test the strength of the associations between remote sensed indicators of fAPAR, GDMP and MODIS GPP, with ground-based eddy covariance-derived GPP, at a semi-arid Nama-Karoo grassland site in South Africa.
Methods
Correlation between standard scores (z-scores) of time-series biophysical indicator data was conducted, to test the relative strengths of each indicator and eddy covariance reference data.
Additionally, linear regression yielded models (R2 = 0.748, 0.538 and 0.129, RMSE = 0.599, 1.34 and 1.88 gC/m2/day) that could be used as a fast and simple predictors of Nama-Karoo type semi-arid grassland biomass availability for grazing management, in similar ecozone’s such as parts of mid-latitude Australia, South America and Mexico.
Study Findings
The findings of the study indicate the strongest correlation exists between standard scores within the MODIS GPP data (r = 0.849, p < 2.2e-16), followed by Sentinel-3 GDMP (r = 0.667, p = 4.3e-12) and lastly Sentinel-3 fAPAR (r = 0.239, p = 0.045). These performance differences are likely due to temporal response differences relating to changes in temperature, vapour pressure deficit, soil moisture, light use efficiency and other variables. The importance of this is that standard scores calculated using Sentinel-3 GDMP observational data may prove more useful, for semi-arid grassland vegetation anomaly early warning systems, than fAPAR, and only slightly less well than MODIS GPP. (Less) - Popular Abstract
- Remote sensed vegetation biophysical indicators are derived from various sensors and methods and contribute to the quantification of vegetation growth. They are used as inputs to early warning systems for crop yield estimation, thus improving crop management and food security. However, the performance of any such system is affected by the performance of the chosen remote sensed biophysical indicator.
Performance based selection, requires evaluation of the performance of various remote sensed indicators, in how well each indicator represents actual vegetation growth. Such performance evaluation has previously been done in northern-latitude focused studies, with little or none having been conducted in southern hemisphere semi-arid... (More) - Remote sensed vegetation biophysical indicators are derived from various sensors and methods and contribute to the quantification of vegetation growth. They are used as inputs to early warning systems for crop yield estimation, thus improving crop management and food security. However, the performance of any such system is affected by the performance of the chosen remote sensed biophysical indicator.
Performance based selection, requires evaluation of the performance of various remote sensed indicators, in how well each indicator represents actual vegetation growth. Such performance evaluation has previously been done in northern-latitude focused studies, with little or none having been conducted in southern hemisphere semi-arid grasslands specifically, such as those used for sheep grazing in the Nama-Karoo biome in South Africa.
Standard scores of time-series biophysical indicator data were calculated, and used to test the relative strengths of the associations between the remote sensed indicators, fAPAR, GDMP and MODIS GPP, and reference data from an eddy covariance flux station at a Nama-Karoo semi-arid grassland site.
Study Findings
The findings of the study indicate the strongest correlation exists between standard scores within the MODIS GPP data, followed by Sentinel-3 GDMP and lastly Sentinel-3 fAPAR. These performance differences are likely due to temporal response differences relating to changes in temperature, vapour pressure deficit, soil moisture, light use efficiency and other variables. The importance of this is that standard scores calculated using Sentinel-3 GDMP observational data may prove more useful, for semi-arid grassland vegetation anomaly early warning systems, than fAPAR, and only slightly less well than MODIS GPP.
Additionally, linear regression yielded models that could be used as a fast and simple predictors of Nama-Karoo type semi-arid grassland biomass availability for grazing management, in similar ecozone’s such as parts of mid-latitude Australia, South America and Mexico. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9141718
- author
- Joubert, Jason Craig LU
- supervisor
-
- Jonas Ardö LU
- organization
- course
- GISM01 20232
- year
- 2023
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Geography, GIS, Anomaly Detection, Remote Sensing, fAPAR, GDMP, GPP, MODIS, Sentinel-3
- publication/series
- Master Thesis in Geographical Information Science
- report number
- 169
- language
- English
- id
- 9141718
- date added to LUP
- 2023-11-28 13:24:35
- date last changed
- 2023-11-28 13:24:35
@misc{9141718,
abstract = {{Remote sensed vegetation biophysical indicators are derived from various sensors and methods and contribute to the quantification of vegetation growth. They are used as inputs to early warning systems for crop yield estimation, thus improving crop management and food security. However, the performance of any such system is affected by the performance of the chosen remote sensed biophysical indicator. Performance based selection, requires evaluation of the performance of various remote sensed indicators, in how well each indicator represents actual vegetation growth.
Scientific Problem
Such performance evaluation has previously been done in northern-latitude focused studies, with little or none having been conducted in southern hemisphere semi-arid grasslands specifically, such as those used for sheep grazing in the Nama-Karoo biome in South Africa.
Study Aim
The aim of this study is to test the strength of the associations between remote sensed indicators of fAPAR, GDMP and MODIS GPP, with ground-based eddy covariance-derived GPP, at a semi-arid Nama-Karoo grassland site in South Africa.
Methods
Correlation between standard scores (z-scores) of time-series biophysical indicator data was conducted, to test the relative strengths of each indicator and eddy covariance reference data.
Additionally, linear regression yielded models (R2 = 0.748, 0.538 and 0.129, RMSE = 0.599, 1.34 and 1.88 gC/m2/day) that could be used as a fast and simple predictors of Nama-Karoo type semi-arid grassland biomass availability for grazing management, in similar ecozone’s such as parts of mid-latitude Australia, South America and Mexico.
Study Findings
The findings of the study indicate the strongest correlation exists between standard scores within the MODIS GPP data (r = 0.849, p < 2.2e-16), followed by Sentinel-3 GDMP (r = 0.667, p = 4.3e-12) and lastly Sentinel-3 fAPAR (r = 0.239, p = 0.045). These performance differences are likely due to temporal response differences relating to changes in temperature, vapour pressure deficit, soil moisture, light use efficiency and other variables. The importance of this is that standard scores calculated using Sentinel-3 GDMP observational data may prove more useful, for semi-arid grassland vegetation anomaly early warning systems, than fAPAR, and only slightly less well than MODIS GPP.}},
author = {{Joubert, Jason Craig}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master Thesis in Geographical Information Science}},
title = {{A comparison of remote sensed semi-arid grassland vegetation anomalies detected using MODIS and Sentinel-3, with anomalies in ground-based eddy covariance flux measurements.}},
year = {{2023}},
}