Remote Sensing and Wetland Ecology: a South African Case Study

Monitoring trophic status using in situ data and Sentinel-2 MSI algorithm: lesson from Lake Malombe, Malawi

With excessive nutrient enrichment exacerbated by anthropogenic drivers, many standing water bodies are changing from oligotrophic to mesotrophic, eutrophic, and finally hypertrophic-negatively affecting ecosystem functioning, biodiversity, and human populations. Efforts have been devoted to developing novel algorithms for estimating chlorophyll-a (chl-a), cyno-blooms, and floating vegetation. However, to this date, little research has focused on freshwater lakes in the data-scarce Sub-Saharan African countries such as Malawi. We, therefore, estimated the trophic status of Lake Malombe in Malawi-a lake likely to be affected by eutrophication and algal bloom-emerging threats to freshwater ecosystem functioning globally-especially with the onset of climatic and anthropogenic drivers. We integrated in situ data with high-resolution Sentinel-2 Multispectral Imagery Analysis (MSI). We independently assessed the remote sensing technique using in situ data and tested the model at multiple stages. The scatter plot showed that most points were in the 95% confidence interval. The validation results between the measured in situ chl-a concentrations and the Sentinel-2 MSI-based chl-a retrieval had a root mean square error (RMSE) of 2.88 µg/L. The chl-a concentrations retrieved from MSI images were consistent with in situ data, indicating that the normalized difference chlorophyll index (NDCI) algorithm estimated chl-a concentrations in Lake Malombe with acceptable accuracy. Dissolved oxygen (DO), sulfate (SO₄^2-), nitrite [Formula: see text], soluble reactive phosphorous [Formula: see text]), total dissolved solids (TDS), and chl-a, except for temperatures from the hot-dry-season, cold-dry-windy-season, and rainy-season, were significantly different (P < 0.05). The Sentinel-2 MSI imagery analysis also depicted similar results, with high chl-a concentration reported in March (rainy season) and October (hot-dry season) and the lowest from May to August (cold-dry-windy season). On the contrary, the ANOVA results for water quality parameters from all five points had P > 0.05. The correlation matrix showed coefficients of (0.798 < r < 0.930, n = 30, P < 0.005), suggesting that Lake Malombe is homogenous. Our results demonstrate that integrating remote sensing based on MSI imagery and in situ data to estimate chl-a can provide an effective tool for monitoring eutrophication in small, medium, and large standing waterbodies-crucial information required to respond to global ecological and climatic dynamics.

Analysis of the Temporal Changes of Inland Ramsar Sites in Turkey Using Google Earth Engine

Ramsar Convention (RC) is the first of modern intergovernmental agreement on the conscious use and conservation of natural resources. It provides a platform for contracting parties working together to develop the best available data, advice, and policy recommendations to increase awareness of the benefits of wetlands in nature and society. Turkey became a party of the RC in 1994, and in the years 1994 to 2013, 14 wetlands that reached the Ramsar criteria were recognized as Ramsar sites (RS). With this study, all inland RS in Turkey from 1985 to 2020 were examined, and changes in the water surface areas were evaluated on the GEE cloud computing platform using Landsat satellite images and the NDWI index. The closest meteorological station data to each RS were evaluated and associated with the surface area changes. The reasons for the changes in these areas, besides the meteorological effects, have been scrutinized using management plans and publications. As a result, inland wetlands decreased at different rates from 1985 to 2020, with a total loss of 31.38% and 21,571.0 ha for the spring months. Since the designation dates of RS, the total amount of water surface area reduction was 27.35%, constituting 17,758.90 ha.

Use of Spectral Indices to Identify the Changes in the Vegetation Community Over Time After Restoring a Palustrine Wetland: A Case Study of Spencer Island Regional Park, Everett, WA

Wetland restoration can be measured over time using community vegetation as an effectiveness indicator of restoration actions. Spencer Island Regional Park is part of the tidal freshwater wetlands along the Snohomish river basin. Those wetlands are part of a complex ecosystems, in which they are included as a salmon corridor. This research analyzes the vegetation community changes over time after restored in 1996 on Spencer Island Regional Park, Everett, Washington State, U.S. I analyzed three spectral indices using segmentation and supervised classification of land cover from 1997 to 2018. I found that in the last 21 years, the areas with emergent palustrine vegetation and forests increased, in contrast to diminishing areas of upland and scrub-shrub classes. Those finds can be interpreted that the community vegetation advanced to higher wetland successional stages as upland areas have been colonized by emergent wetland plant communities. A linear regression model predicted that by 2025, the difference between emergent and upland classes should increase. Empirical evidence is presented that support the integration of spectral indices to identify changes in community vegetation. However, it is recommended for future studies to include spectral indices and spatial information for soil and hydrology to deepen these results.

Ecosystem evaluation (1989-2012) of Ramsar wetland Deepor Beel using satellite-derived indices

The unprecedented urban growth especially in developing countries has laid immense pressure on wetlands, finally threatening their existence altogether. A long-term monitoring of wetland ecosystems is the basis of planning conservation measures for a sustainable development. Deepor Beel, a Ramsar wetland and major storm water basin of the River Brahmaputra in the northeastern region of India, needs particular attention due to its constant degradation over the past decades. A rule-based classification algorithm was developed using Landsat (2011)-derived indices, namely Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), Normalised Difference Pond Index (NDPI), Normalised Difference Vegetation Index (NDVI) and field data as ancillary information. Field data, ALOS AVNIR and Google Earth images were used for accuracy assessment. A fuzzy accuracy assessment of the classified data sets showed an overall accuracy of 82 % for MAX criteria and 90 % for RIGHT criteria. The rules were used to classify major wetland cover types during low water season (January) in 1989, 2001 and 2012. The statistical analysis of the classified wetland showed heavy manifestation in aquatic vegetation and other features indicating severe eutrophication over the past 23 years. This degradation was closely related to major contributing anthropogenic factors, such as a railway line construction, growing croplands, waste disposal and illegal human settlements in the wetland catchment. In addition, the landscape development index (LDI) indicated a rapid increase in the impact of the surrounding land use on the wetland from 1989 to 2012. The techniques and results from this study may prove useful for top-down landscape analyses of this and other freshwater wetlands.