Exploring Forest Change Spatial Patterns in Papua New Guinea: A Pilot Study in the Bumbu River Basin

Abstract

Papua New Guinea is a country in Oceania that hosts unique rain forests and forest ecosystems which are crucial for sequestering atmospheric carbon, conserving biodiversity, supporting the livelihood of indigenous people, and underpinning the timber market of the country. As a result of urban sprawl, agricultural expansion, and illegal logging, there has been a tremendous increase in land-use land cover (LULC) change happening in the country in the past few decades and this has triggered massive deforestation and forest degradation. However, only a few studies have ventured into quantifying the long-term trends and their associated spatial patterns—and have often presented contrasting responses. Herein, we intended to assess the extent of deforestation and the rate of urbanization that happened in the past 33 years (1987–2020) in the Bumbu river basin in Papua New Guinea using satellite imagery—for the years 1987, 2002, 2010, and 2020—and Geographic Information System (GIS) tools. On performing image classification, land use maps were developed and later compared with Google Earth’s high-resolution satellite images for accuracy assessment purposes. For probing into the spatial aspects of the land-use change issues, the study area was divided into four urban zones and four forest zones according to the four main cardinal directions centered in the urban and forest area centers of the 1987 image; subsequently, the rate of urban area expansion in each urban zone was separately calculated. From our preliminary analysis and literature survey, we observed several hurdles regarding the classification of regenerative forests and mixed pixels and gaps in LULC studies that have happened in Papua New Guinea to date. Through this communication paper, we aim to disseminate our preliminary results, which highlight a rapid increase in urban extent from 14.39 km2 in 1987 to 23.06 km2 in 2020 accompanied by a considerable decrease in forest extent from 76.29 km2 in 1987 to 59.43 km2 in 2020; this observation favors the presumption that urban and agricultural land expansion is happening at the cost of forest cover. Moreover, strategies for addressing technical issues and for integrating land-use change with various socioeconomic and environmental variables are presented soliciting feedback.

An Assessment of Forest Cover Change and Its Driving Forces in the Syrian Coastal Region during a Period of Conflict, 2010 to 2020

“…For this purpose, Landsat-5 thematic mapper (TM) and Landsat-8 Operational Land Imager (OLI) images for the three target years 2010, 2015, and 2020 were obtained from the official website of the US Geological Survey (USGS) [61]. Landsat images, which have a spatial resolution of 30 m, have spectral properties suitable for detecting changes in LULC [22,27,47,62,63]. Details of the Landsat images used in this study are shown in Table 2.…”

“…A supervised classification method using the maximum likelihood classifier (MLC) algorithm was applied using ArcGIS 10.5 for Landsat images classification purpose in the study area. This classification method is known for its strong theoretical basis and its ability to accommodate changing spectral signatures of different land uses [27]. This classification algorithm also achieved the best results for the purposes of classifying land uses in sub-humid and humid areas as comparable environments using similar data [3,13,15,20,47,72].…”

“…Accuracy assessment is an important step during the classification because it reflects the real difference between the results of the Landsat image classification and the reference data (ground truth points) [3,13,27,73]. Accordingly, a confusion matrix was applied to the classified images to calculate and compare the user's accuracy (UA), producer's accuracy (PA) and the overall accuracy [27,74,75]. For this purpose, a stratified random sampling approach was taken by allocating sample points in each category of LULC [9,51].…”

“…To monitor and map the burned areas during the study period, the spectral signature of burned forest areas was determined on Landsat images. Using the re-classify tool available in ArcGIS 10.5, the burned areas were assigned to the values of "1", while the pixels of all other LULC categories were merged into one category and assigned to the values of "0" [3,15,27]. Manual interpretation with the help of high-resolution Google Earth imagery and the NDVI values was used to improve the accuracy of the resulting maps.…”

“…In recent years, many studies have been conducted using multitemporal satellite images to monitor and map changes in forest cover around the world. These studies used developed methodologies for this purpose, such as pixel-based classification analyses [1,3,8,13,15,[20][21][22][23][24][25][26][27], vegetation indices analysis [6,20,28,29], and object-based or object-oriented classification analyses [7,9,18,20,21,[30][31][32][33][34]. Meanwhile, many studies based on remotely sensed data and GIS techniques have been conducted to study forest degradation in different regions around the world, such as those studies that used Landsat images and different vegetation indices to estimate the impact of selective logging, forest fires and the intense production of coal on the loss of forest cover as well as to map degraded forests [35][36][37][38][39][40][41].…”