Mangrove land use changes of varied intensities have long been a problem in tropical mangrove forests. This has resulted in various degrees of mangrove land use modification, which catch many interests in the region for research. The research provided here is a bibliometric analysis of scholarly articles published around the world in various publication document types on changes in land use of tropical mangrove forests based on remote sensing and Geographical Information System (GIS). Scientific data analysis was undertaken by using bibliometric approaches on 6,574 papers extracted from the Scopus databases between 2010 and 2020. The findings revealed that the number of publications continuously climbed from under 400 to an average of 50-60 per year till 2019. The data showed that the mangrove forest modifications study gained traction when the highest number of citations, 9,236 in 2015, were observed. We can also notice that the overall number of citations fluctuated a lot during the first five years (2010-2015) but increased from 2013 to 2015. The findings demonstrate how remote sensing satellites have aided vegetation and land study in recent years. The findings also revealed that the analysis tools of Land Use Change, Vegetation Index, Mangrove, Tropical Country, Remote Sensing, and Tropical contributed to scientific knowledge of current issues of mangrove land use change in the tropical region. The authors' keywords, Remote Sensing in particular, supplied roughly 43%, Normalized Difference Vegetation Index (13%), Vegetation Index (9%), and other keywords contributed less than 7%. The growth pattern of the keywords "MODIS" and "Landsat" implies that both will stay important over the next five years, according to an analysis of the type of satellite used in land use assessment. Meanwhile, papers pertaining to policy on land use change, food security, and forest resources were evaluated in order to highlight policy and academic research findings on the topics. The application of the Normalized Difference Vegetation Index, which is a very relevant tool that can be used in monitoring land use changes and assessing vegetation status because it is a desirable technique in measuring plant health and vigour, can help fill the research gaps presented in this study. This review can help with the development of better mangrove land use change approaches in tropical mangroves and around the world using satellite remote sensing and GIS.
Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.