Geostatistical Analysis of Mangrove Ecosystem Health: Mapping and Modelling of Sampling Uncertainty Using Kriging

Abstract

This study assessed the health of the mangrove ecosystem and mapped the spatial variation in selected variables sampled across the Matang Mangrove Forest Reserve (MMFR) by using a geostatistical technique. A total of 556 samples were collected from 56 sampling points representing mangrove biotic and abiotic variables. All variables were used to generate the semivariogram model. The predicted variables over the entire MMFR have an overall prediction accuracy of 85.16% (AGB), 90.78% (crab abundance), 97.3% (soil C), 99.91% (soil N), 89.23% (number of phytoplankton species), 95.62% (number of diatom species), 99.36% (DO), and 87.33% (turbidity). Via linear weight combination, the prediction map shows that mangrove ecosystem health in Kuala Trong throughout the Sungai Kerang is excellent (5: MQI > 1.5). Some landward areas of Kuala Trong were predicted to have moderate health (3: −0.5 ≤ MQI ≤ 0.5), while Kuala Sepetang was predicted to have the bad ecosystem health (2: -1.5 ≤ MQI ≤ −0.5), with active timber harvesting operations and anthropogenic activities in the landward areas. The results of this method can be utilised to carry out the preferred restoration, through appropriate management and facilities distribution, for improving the ecosystem health of mangroves.

Estimating mangrove aboveground biomass in the Colombian Pacific coast: A multisensor and machine learning approach

“…[ 69 ], is that for Colombia, there is a scarcity of field studies in mangrove forests that makes spatial estimates difficult; in addition, sampling in mangroves is complex due to the particular conditions ( Fig. 8 ) of this type of ecosystem, (e.g., tidal regime, muddy flats, root structure) and the costs associated with sampling large areas [ 70 ]. Public order problems and conflicts in the CPC [ 5 ] also limited sample collection in certain mangrove areas.…”

Long-term exposure to high perceived temperature and risk of mortality among patients with chronic kidney disease

“…Using the geocoded address of each patient and nearby AWS, we performed spatial interpolations using the Kriging method, which estimated the temperature and temperature indices of each patient based on the Gaussian process from the temperatures of each AWS [ 15 , 16 ]. Kriging interpolation estimates the optimal linear prediction at unmeasured locations with more weights in a nearby location from the measured points [ 17 ].…”

Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

“…The classification and segmentation of coastal objects, including mangrove cover and tidal marsh, allows for determining the extent of each object. Using machine learning techniques, including support vector machines (SVM), 5 , 22 , 25 – 28 support vector regression, artificial neural network, 29 random forest (RF), 22 , 30 , 31 decision tree, symbolic regression, 32 extreme gradient boosting regression, 33 , 34 light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), allows for the retrieval of data on mangrove distribution. RF has an excellent biomass modeling ability 35 and can increase the precision of land cover mapping, wetland mapping, 36 and mangrove species classification 5 , 34 , 37 …”

“…23 Furthermore, integration of artificial intelligence, mathematical algorithms, and big data analysis with high-resolution sensing imaging data has become more common. 24,25 These data can be collected on a regular basis over a wide geographic area, enabling precise, and accurate monitoring of mangrove forest ecosystems. 22 The use of remote sensing technologies is expanding, along with the demand for spatial data.…”