An overview of vegetation health in the North West Province, South Africa, between 2010 and 2020

The North West Province in South Africa is an important contributor to the country’s economy with agriculture and mining the main drivers. Droughts regularly affect the region and impact greatly on farming which in turn has negative socio-economic consequences. Multi-temporal satellite remote sensing data is well suited to study changes in vegetation health. Vegetation and temperature indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and rainfall data from the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) between 2010 and 2020 showed good vegetation health in 2010 and 2020, but gradually worsening drought condition in the intervening years. Although the entire North West Province is affected by drought, the central and western portions experience the worst conditions. The vegetation condition index, temperature condition index and vegetation health index show a faster recovery along the western edge of the province in 2018 than the rest of the province, a detail not easily visible in the conventional enhanced vegetation index and land surface temperature data. They also show a gradual decrease in vegetation health between 2010 and 2014. A comparison with geology shows that vegetation health is, in part, also linked to the underlying rock types.

Eucalyptus forest plantation assessment of vegetation health using satellite remote sensing techniques

Early examination of the water condition of the plants utilizing remote sensing technology can be used to assess the health of the vegetation in the Eucalyptus forest plantation. To preserve a sustainable wood supply and wooded region that is necessary to human life and vital wood supplies, the forested region should be protected from disease and environmental damage. Disease and environmental impacts are two of the most critical challenges in Eucalyptus forest management. To calculate the vegetation index and identify land cover in the research region, remote sensing with Catalyst Professional software based on Object Analyst (OBIA) tools was utilized. The NDVI (Normalized Difference Vegetation Index) is a valuable index for assessing early vegetation health. For atmospheric correction and haze removal, the image was first pre-processed with ATCOR tools. Second, the image was converted to NDVI using algorithm library tools. In addition, for land cover classification in the area, an OBIA based on Support Vector Machine (SVM) was utilized, followed by an accuracy assessment. Using ArcGIS software, zonal statistics were used to calculate the NDVI value for each land cover category. According to the method, the map produced roads, plantations, buildings, low-density vegetation, oil palm, and open area classifications. Based on accuracy assessment in OBIA, plantation, oil palm, and open area were all 100% accurate, whereas low-density vegetation and oil palm were 100% accurate according to the user. Producer accuracy was lowest on roads, whereas user accuracy was lowest in open areas. Non-vegetated land is difficult to classify at this site, according to the accuracy assessment results. The map improved accuracy since the study used a lower segmentation scale factor of 50, which produced fine vectors ascribed for classification. The average NDVI for oil palm area was 0.71, and 0.69 for plantation. Because it was difficult to classify open areas and roads, the NDVI for the class was low, at 0.37 and 0.22, respectively. From land use classification, the plantation was classified (37%), low-density vegetation area (28%), and oil palm (21%). Others make up only 2 to 7% of the site’s overall area. According to the study, NDVI is a useful indicator for assessing the health of vegetation in areas where NDVI values are larger than 0.70 and presents pf mixed landscape and non-vegetated features. A higher NDVI value implies that the plant is in good enough shape to conduct photosynthetic activities thus producing biomass for sustaining vegetation health. This type of inquiry can forecast more indices to produce higher accuracy of land use maps for the Eucalyptus plantation. At the same time, this type of research can assist forest managers in detecting large areas in their plantation for vegetation health assessment such as for early disease detection.

Agricultural Drought Detection with MODIS Based Vegetation Health Indices in Southeast Germany

Droughts during the growing season are projected to increase in frequency and severity in Central Europe in the future. Thus, area-wide monitoring of agricultural drought in this region is becoming more and more important. In this context, it is essential to know where and when vegetation growth is primarily water-limited and whether remote sensing-based drought indices can detect agricultural drought in these areas. To answer these questions, we conducted a correlation analysis between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) within the growing season from 2001 to 2020 in Bavaria (Germany) and investigated the relationship with land cover and altitude. In the second step, we applied the drought indices Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) to primarily water-limited areas and evaluated them with soil moisture and agricultural yield anomalies. We found that, especially in the summer months (July and August), on agricultural land and grassland and below 800 m, NDVI and LST are negatively correlated and thus, water is the primary limiting factor for vegetation growth here. Within these areas and periods, the TCI and VHI correlate strongly with soil moisture and agricultural yield anomalies, suggesting that both indices have the potential to detect agricultural drought in Bavaria.

PCA driven watershed prioritization based on runoff modeling and drought severity assessment in parts of Koel river basin, Jharkhand (India)

Global warming influencing regional climate is playing a significant role in triggering the recurrent drought. The current study demonstrates a PCA (Principal Component Analysis) driven watershed prioritization in a part of Koel river basin by runoff computation during monsoon season along with assessment of Vegetation Health Index (VHI) derived from MODIS satellite data during the period from 2000 to 2017. Koel river catchment area of 7,261 sq km was divided into 82 sub-watersheds based on drainage networks derived from Survey of India (SOI) topographical map on scale 1: 50,000. High resolution satellite image of Sentinel-2 was used to prepare land use land cover map. Soil conservation service curve number method (SCS CN) was used to estimate runoff. Result obtained from runoff estimation of 82 sub watersheds shows high runoff (50 to 60% of rainfall) with 290,000 m3 total runoff volume in the upper and middle parts of the catchment dominated by agricultural/fallow and barren lands whereas low runoff was estimated (20 to 30%) with 29,467 m3 in the lower catchments where a large area is covered with forests. The value of satellite based VHI ranges between 23 to 53 with major parts of the area exhibiting values less than 30 reflecting poor vegetation health. Most of the sub-watersheds in parts of Ranchi, Lohardaga, Gumla and Khunti districts experienced high total runoff with poor vegetation health index reflecting more proneness to drought. Watershed prioritization was done based on correlation among four parameters viz., rainfall, drought zones, direct runoff and total runoff through PCA. Strong correlation between total runoff volume and drought areas was used for watershed prioritization which indicated 42 sub-watersheds (4,703 sq km) in the upper catchment required high prioritization. The outcomes of study would help proper planning of water resources and soil moisture management to overcome the recurrent drought conditions at watershed level.

Medium-Term Increases in Ambient Grass Pollen Between 1994-1999 and 2016-2020 in a Subtropical Climate Zone

Grass pollen is the major outdoor trigger of allergic respiratory diseases. Climate change is influencing pollen seasonality in Northern Hemisphere temperate regions, but many aspects of the effects on grass pollen remain unclear. Carbon dioxide and temperature rises could increase the distribution of subtropical grasses, however, medium term shifts in grass pollen in subtropical climates have not yet been analysed. This study investigates changes in grass pollen aerobiology in a subtropical city of Brisbane, Australia, between the two available monitoring periods, 1994-1999 and 2016-2020. Potential drivers of pollen change were examined including weather and satellite-derived vegetation indicators. The magnitude of the seasonal pollen index for grass showed almost a three-fold increase for 2016-2020 over 1994-1999. The number and proportion of high and extreme grass pollen days in the recent period increased compared to earlier monitoring. Statistically significant changes were also identified for distributions of CO2, satellite-derived seasonal vegetation health indices, and daily maximum temperatures, but not for minimum temperatures, daily rainfall, or seasonal fraction of green groundcover. Quarterly grass pollen levels were correlated with corresponding vegetation health indices, and with green groundcover fraction, suggesting that seasonal-scale plant health was higher in the latter period. The magnitude of grass pollen exposure in the subtropical region of Brisbane has increased markedly in the recent past, posing an increased environmental health threat. This study suggests the need for continuous pollen monitoring to track and respond to the possible effects of climate change on grass pollen loads.

Casual Rerouting of AERONET Sun/Sky Photometers: Toward a New Network of Ground Measurements Dedicated to the Monitoring of Surface Properties?

This paper presents an innovative method for observing vegetation health at a very high spatial resolution (~5 × 5 cm) and low cost by upgrading an existing Aerosol RObotic NETwork (AERONET) ground station dedicated to the observation of aerosols in the atmosphere. This study evaluates the capability of a sun/sky photometer to perform additional surface reflectance observations. The ground station of Toulouse, France, which belongs to the AERONET sun/sky photometer network, is used for this feasibility study. The experiment was conducted for a 5-year period (between 2016 and 2020). The sun/sky photometer was mounted on a metallic structure at a height of 2.5 m, and the acquisition software was adapted to add a periodical (every hour) ground-observation scenario with the sun/sky photometer observing the surface instead of being inactive. Evaluation is performed by using a classical metric characterizing the vegetation health: the normalized difference vegetation index (NDVI), using as reference the satellite NDVI derived from a Sentinel-2 (S2) sensor at 10 × 10 m resolution. Comparison for the 5-year period showed good agreement between the S2 and sun/sky photometer NDVIs (i.e., bias = 0.004, RMSD = 0.082, and R = 0.882 for a mean value of S2A NDVI around 0.6). Discrepancies could have been due to spatial-representativeness issues (of the ground measurement compared to S2), the differences between spectral bands, and the quality of the atmospheric correction applied on S2 data (accuracy of the sun/sky photometer instrument was better than 0.1%). However, the accuracy of the atmospheric correction applied on S2 data in this station appeared to be of good quality, and no dependence on the presence of aerosols was observed. This first analysis of the potential of the CIMEL CE318 sun/sky photometer to monitor the surface is encouraging. Further analyses need to be carried out to estimate the potential in different AERONET stations. The occasional rerouting of AERONET stations could lead to a complementary network of surface reflectance observations. This would require an update of the software, and eventual adaptations of the measurement platforms to the station environments. The additional cost, based on the existing AERONET network, would be quite limited. These new surface measurements would be interesting for measurements of vegetation health (monitoring of NDVI, and also of other vegetation indices such as the leaf area and chlorophyll indices), for validation and calibration exercise purposes, and possibly to refine various scientific algorithms (i.e., algorithms dedicated to cloud detection or the AERONET aerosol retrieval algorithm itself). CIMEL is ready to include the ground scenario used in this study in all new sun/sky photometers.

Use of remotely sensed derived metrics to assess wetland vegetation responses to climate variability induced drought at the Soetendalsvlei wetland system in the Western Cape province of South Africa

Wetland areas are the most vital ecosystems and they provide important functions towards stabilizing the environment. Hydrological processes in these wetland systems directly affects the productivity of plants. Therefore, assessing vegetation response to climate variability induced drought is vital in wetlands. In this paper, the subtle changes in vegetation distribution were used as a proxy to examine and quantify the extent of drought impacts on wetland ecosystems within the Heuningnes catchment, South Africa. First, vegetation health information was extracted by calculating the normalized difference vegetation index (NDVI) during the wet and dry seasons for the period between 2014 and 2018. The derived NDVI results were further statistical linked to the corresponding rainfall and evapotranspiration (ET) observed during the study period. An analysis of NDVI results revealed that gradual vegetation health change occurred across the study area. The highest derived NDVI (0.5) for wetland vegetation was observed during the year 2014 but progressively declined over the years. Change in vegetation health indicated a significant (α = 0.05) and positive correlation to the amount of rainfall received over the same period. The results of this study showed that healthy vegetation deteriorated between the study periods due to the 2015–2017 Western Cape drought.