Dynamics and Determinants of Forest Changes Across Mainland Vietnam in the Recent Three Decades

South and Southeast Asia (SSEA) has been a hotspot for land use and land cover change (LULCC) in the past few decades. The identification and quantification of the drivers of LULCC are crucial for improving our understanding of LULCC trends. So far, the biophysical and socioeconomic drivers of forest change have not been quantified at the regional scale, particularly for SSEA. In this study, we quantify the biophysical and socioeconomic drivers of forest change on a country-by-country basis in SSEA using an integrated quantitative methodology, which systematically accounts for previously published driver information and regional datasets. We synthesize more than 200 publications to identify the drivers of the forest change at different spatial scales in SSEA. Subsequently, we collect spatially explicit proxy data to represent the identified drivers. We quantify the dynamics of forest and agricultural land from 1992 to 2015 using the Climate Change Initiative (CCI) land cover data developed by the European Space Agency (ESA). A geographically weighted regression method is employed to quantify the spatially heterogeneous drivers of forest change. Our results show that socioeconomic drivers are more important than biophysical drivers for the conversion of forest to agricultural land in South Asia and maritime Southeast Asia. In contrast, biophysical drivers are more important than socioeconomic drivers for the conversion of agricultural land to forest in maritime Southeast Asia and less important in South Asia. Both biophysical and socioeconomic drivers contribute approximately equally to both changes in the mainland Southeast Asia region. By quantifying the dynamics of forest and agricultural land and the spatially explicit drivers of their changes in SSEA, this study provides a solid foundation for LULCC modeling and projection.

Yom river basin is one of the largest basins in Thailand. This basin does not have any large reservoirs to collect a large amount of water in the rainy season. Therefore, the basin typically experienced floods in the rainy season (May–October) and drought in the dry season (November - April). Land use change may affect surface and groundwater hydrology associated with hydrological factors such as interception, infiltration and evaporation, and thus consequently causes significant changes in especially total runoff in the river. The objective of this research project was to determine the hydrological impacts of land use changes in the Yom river over a 22-year period using an integration of remote sensing, geographic information system (GIS), and SWAT hydrological modeling to quantify contributions of such changes. Through the interpretation of satellite images between 1981 and 2009, the forest land changes to agriculture areas about 1359.088 km2 (5.675%), the forest land changes to water body about 40.208 km2 (0.168%), the forest land changes to urban and build-up land 26 km2 (0.10 %) and the agriculture areas changes to urban and build-up land about 309.965 (1.29%) km2. For the contributions of land use changes on hydrological components, the average simulated yearly runoff at station Y14 increases 1,835.95, 1,648.65 and 1,620.70 MCM/Year and at station Y 20, the average simulated yearly runoff increases 1,176.25, 1,090.27 and 671.42 MCM/Year, when the forest land was decreased 1,979.67, 1,141.69 and 1,741.171 km2 as well as urban and build-up land was increased 66.48, 125.6 and 104.22 km2 in year 1995, 2003 and 2009, respectively. Increased runoff occurred in the long term discharge, especially in the upper part of the basin, which may cause more floods in the lower part of the basin. The approach used in this study could be applied further to other watersheds, which have been highly changed and would essential for land-use planning and sustainable water resources management.

The functions of terrestrial ecosystems are various, and recent study suggests the major three functions which are carbon, water, and energy cycling. They are all originated from land, the fundamental components of terrestrial ecosystems. Land consists of major five land cover: cropland, grassland, built-up area, wetland, and forest land. Forest land is described as high potential to remove Greenhouse gases under climate change era and thus the forest carbon management has been raised for effective land management in terms of carbon removal. Korean peninsula, South Korea and North Korea, has undergone the severe war between them and it damaged the whole territory, which consists of more than 60% of forest land. Therefore, two countries tried to revegetate and implemented forestation plans for recover the forest land over 50 years. Therefore, this study assessed the forest carbon management on the Korean Peninsula using Net Primary Productivity(NPP) from the 1980s to 2010s. To estimate NPP, Carnegie-Ames-Stanford Approach(CASA) model was applied. The study adopted the carbon demand and supply method for assessment. We defined carbon demand as amount of carbon loss from forest land in previous year due to forest land changes, and carbon supply as amount of newly updated carbon sink from forest land due to afforestation. According to research findings, even though South Korea achieved successful forest expansion, it only focused on the amount of forest area rather the quality of carbon management. However, the situation in North Korea described not only the failure of increasing forest area but also forest carbon management. Further research would be analyzed the outcomes with forest plans in South Korea and North Korea.

Forest and agricultural land change in the Carpathian region—A meta-analysis of long-term patterns and drivers of change

На прикладі гірського агроландшафтного середовища показано застосування технології дистанційного зондування Землі (ДЗЗ) до контролю зміни площ лісових угідь через їх вирубування. На підставі супутникового знімка QuickBird здійснено моделювання вирубок лісових угідь і отримано зображення територій з різними зонами вирубок. Зміни у структурі площ лісових угідь відбувається способом переміщення лінії розмежування "лісовий покрив – вирубка", за якого площі з лісового покриву переходять до зони вирубок. Доведено, що гістограмні характеристики RGB супутникового знімка QuickBird мають інформативність про зміни площ у структурі "лісовий покрив – вирубка". Показано, що величини медіан усіх RGB-каналів однозначно реагують на зміни площ лісового покрову. Найбільш чутливим до динаміки зміни площ з усіх RGB-каналів є канал Green. Фізичне пояснення цього процесу здійснено на підставі тестових фрагментів "лісовий покрив" і "вирубка". Встановлено, що завдяки різній текстурі "лісового покрову" і "вирубки" їхні гістограмні характеристики відрізняються зонами розміщення пікселів за яскравістю. Для фрагменту "лісовий покрив" основна частина пікселів у гістограмі зосереджена в діапазоні з кодом 0–100, тоді як для фрагменту "вирубка" вона зміщується вправо до діапазону 100–200. Більш однорідна текстура вирубок дає СКВ 24, що створює графік із чітко виразним екстремумом, який стає дедалі помітнішим із поширенням площі вирубок. Отримано аналітичну залежність між динамікою зміни площ ΔS на границі "лісовий покрив – вирубка" і кількості пікселів Nекстр, що припадає на екстремум за кількістю пікселів у діапазоні гістограми 100–200. Показано її достатньо високу чутливість, що дає змогу вирішувати задачі обліку зміни площ лісових угідь у структурі земельного фонду за характеристиками супутникового знімка.

Quantifying the amount of forest and change in the amount of forest are key to ensure that appropriate management practices and policies are in place to maintain the array of ecosystem services provided by forests. There are a range of analytical techniques and data available to estimate these forest parameters, however, not all ‘forest’ is the same and various components of change have been presented. Forest as defined by use and forest as defined by cover are different, although it is common for scientists and policy makers to infer one from the other. We compare and contrast estimates of forest land cover, forest land use, extent and change at policy-relevant scales in the southeastern US. We found that estimates of forest land use extent and forest land cover extent were not significantly correlated. Estimates of net change based on forest land cover and forest land use were only moderately correlated and net change estimates were independent of gross forest cover loss estimates.

Land use/land cover (LULC) changes are among the most significant human-caused global variations affecting the natural environment and ecosystems. Pakistan’s LULC patterns have undergone huge changes since the 1900s, with no clear mitigation plan. This paper aims to determine LULC and normalized difference vegetation index (NDVI) changes as well as their causes in Pakistan’s Southern Punjab province over four different periods (2000, 2007, 2014, and 2021). Landsat-based images of 30 m × 30 m spatial resolution were used to detect LULC changes, while NDVI dynamics were calculated using Modis Product MOD13Q1 (Tiles: h24 v5, h24 v6) at a resolution of 250 m. The iterative self-organizing (ISO) cluster method (object meta-clustering using the minimal distance center approach) was used to quantify the LULC changes in this research because of its straightforward approach that requires minimal human intervention. The accuracy assessment and the Kappa coefficient were calculated to assess the efficacy of results derived from LULC changes. Our findings revealed considerable changes in settlements, forests, and barren land in Southern Punjab. Compared to 2000, while forest cover had reduced by 31.03%, settlement had increased by 14.52% in 2021. Similarly, forest land had rapidly been converted into barren land. For example, barren land had increased by 12.87% in 2021 compared to 2000. The analysis showed that forests were reduced by 31.03%, while settlements and barren land increased by 14.52% and 12.87%, respectively, over the twenty year period in Southern Punjab. The forest area had decreased to 4.36% by 2021. It shows that 31.03% of forest land had been converted to urban land, barren ground, and farmland. Land that was formerly utilized for vegetation had been converted into urban land due to the expansion of infrastructure and the commercial sector in Southern Punjab. Consequently, proper monitoring of LULC changes is required. Furthermore, relevant agencies, governments, and policymakers must focus on land management development. Finally, the current study provides an overall scenario of how LULC trends are evolving over the study region, which aids in land use planning and management.

Change of land use land cover (LULC) has been known globally as an essential driver of environmental change. Assessment of LULC change is the most precise method to comprehend the past land use, types of changes to be estimated, the forces and developments behind the changes. The aim of the study was to assess the temporal and spatial LULC dynamics of the past and to predict the future using Landsat images and LCM (Land Change Modeler) by considering the drivers of LULC dynamics. The research was conducted in Nashe watershed (Ethiopia) which is the main tributary of the Upper Blue Nile basin. The total watershed area is 94,578 ha. The Landsat imagery from 2019, 2005, and 1990 was used for evaluating and predicting the spatiotemporal distributions of LULC changes. The future LULC image prediction has been generated depending on the historical trends of LULC changes for the years 2035 and 2050. LCM integrated in TerrSet Geospatial Monitoring and Modeling System assimilated with MLP and CA-Markov chain have been used for monitoring, assessment of change, and future projections. Markov chain was used to generate transition probability matrices between LULC classes and cellular automata were used to predict the LULC map. Validation of the predicted LULC map of 2019 was conducted successfully with the actual LULC map. The validation accuracy was determined using the Kappa statistics and agreement/disagreement marks. The results of the historical LULC depicted that forest land, grass land, and range land are the most affected types of land use. The agricultural land in 1990 was 41,587.21 ha which increased to 57,868.95 ha in 2019 with an average growth rate of 39.15%. The forest land, range land, and grass land declined annually with rates of 48.38%, 19.58%, and 26.23%, respectively. The predicted LULC map shows that the forest cover will further degrade from 16.94% in 2019 to 8.07% in 2050, while agricultural land would be expanded to 69,021.20 ha and 69,264.44 ha in 2035 and 2050 from 57,868.95 ha in 2019. The findings of this investigation indicate an expected rapid change in LULC for the coming years. Converting the forest area, range land, and grass land into other land uses, especially to agricultural land, is the main LULC change in the future. Measures should be implemented to achieve rational use of agricultural land and the forest conversion needs to be well managed.

This study employs a transfer matrix, dynamic degree, stability index, and the PLUS model to analyze the spatiotemporal changes in forest land and their driving factors in Yibin City from 2000 to 2022. The results reveal the following: (1) The land use in Yibin City is predominantly characterized by cultivated land and forest land (accounting for over 95% of the total area). The area of cultivated land initially increased and then decreased, while forest land continued to decline and construction land expanded significantly. The rate of forest land loss has slowed (with the dynamic degree decreasing from −0.62% to −0.04%), and ecosystem stability has improved (the F-value increased from 2.27 to 2.9). The conversion of cultivated land to forest land is the primary driver of forest recovery, whereas the conversion of forest land to cultivated land is the main cause of reduction; (2) cultivated land is concentrated in the central and northeastern regions, while forest land is distributed in the western and southern mountainous areas. Construction land is predominantly located in urban areas and along transportation routes. Areas of forest land reduction are mainly found in the central and southern regions with rapid economic development, while areas of forest land increase are concentrated in high-altitude zones or key ecological protection areas. Stable forest land is distributed in the western and southern ecological conservation zones; (3) changes in forest land are primarily influenced by annual precipitation, elevation, and distance to rivers. Road accessibility and GDP have significant impacts, while slope, annual average temperature, and population density exert moderate influences. Distance to railways, aspect, and soil type have relatively minor effects. The findings of this study provide a scientific basis for the sustainable management of forest resources and ecological conservation in Yibin City.

Introduction Although numerous land cover datasets can act as references for understanding land cover change in China, the inconsistencies between the datasets can also provide understanding. Previous studies on the consistency between land cover datasets have mostly focused on land cover type consistencies and have ignored data consistencies in land cover change. Outcomes Therefore, we aim to analyse the consistencies in land cover changes through likelihood assessment methods. We compared the spatiotemporal changes in forest, grassland, cropland, and bare land in the Climate Change Initiative land cover dataset (CCI-LC), Moderate-resolution Resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), China’s National Land Use and Cover Change (CNLUCC), Globeland30 and Global Land Cover Fine Surface Covering 30 (GLC-FCS30) datasets in 2010. The results showed that the percentages and changes in each land cover type in MCD12Q1 were different from those in the other datasets. Discussion For example, the proportion of grassland in MCD12Q1 was the highest, reaching 48.04%. The places with high consistency were the places where the land cover types were concentrated, and the bare land had the highest consistency. However, the consistency of China’s land cover change was quite low, and the percentage of low consistency was more than 87% from 2000-2018. Comparison of the data with the global artificial impervious area (GAIA) and Hansen-Global Forest Change (Hansen-GFC) datasets showed that the percentage of high construction gain consistency (38.83%) was higher than the forest change consistency, and the percentage forest loss high consistency (8.85%) was lower than the forest gain high consistency (12.76%). Conclusion The results not only provide a basis for the use of land cover datasets but also give a clearer understanding of the pattern of land cover changes.

Land usage is a concrete manifestation of the influence of human activities on some parts of earth surface. Forest area has a dynamic land usage condition; hence it needs to check the development, because forest usage often is not met the requirements and allocation. This study intends to acquire the certain level of land-cover changes used Landsat +7/ETM year 2000 dan 2008 for identification. The information provided by aspect of land area and type of land usage. Data analysis was performed to determine the land-cover classification with supervise, unsupervised method, and image interpretation, meanwhile the method used in area change is a visual method. Determination of unit mapping conducted by land usage units. Changing in land cover and forest plantations is significant from the period 2000 and 2008 in Jambi Province. Based on visual methods of forest cover decreased from 7839 km 2 to 5771 km 2 and plantation cover grew from 2262 km 2 to 4241 km 2 . Reduced forest in Jambi caused by changes in forest land into plantations, the frequent occurrence of illegal logging and fires. Keywords: Perubahan Lahan, Supervised classification, and Unsupervised Classification

Changes in land cover (LC) are the major factors influencing the hydrological processes within a watershed. Understanding the impacts of LC on watershed hydrology is crucial for planning and predicting land resource utilization, water resources, and sustaining hydrological balance. This study assesses the hydrological responses of LC changes in the Muger watershed located in the Upper Blue Nile River Basin (UBNRB) from 1986 to 2020. We used the Soil and Water Assessment Tool (SWAT) hydrological model to investigate the effects of LC on the hydrological process. The simulations were driven by several datasets, such as watershed elevations, mean climatology, hydrology and soil datasets, and LC satellite maps for three time periods (i.e., satellite imagery taken in 1986, 2003, and 2020). We found that the key LC changes that affected hydrological parameters in the Muger watershed are changes in cultivation land, forest land, and settlement. The expansion of cultivation land and shrinkage of forest and shrub lands triggered surface runoff and a reduction in groundwater between 1986 and 2003. Additionally, settlement was identified as the primary factor contributing to increases in evapotranspiration (ET) and surface runoff. The LC changes that occurred between 1986 and 2020 reduced the average annual, wet season, and dry season streamflow. Between 2003 and 2020, surface runoff decreased by 3.71% due to the effect of land landscape restoration interventions. The outcome of the study can assist decision-makers and planners in preparing adaptable strategies under changing LC conditions within a watershed.

Understanding drivers of changes in land use/land cover (LULC) is essential for modeling future dynamics or development of management strategies to ameliorate or prevent further decline of natural resources. In this study, an attempt has been made to identify the main drivers behind the LULC changes that had occurred in the past four decades in Munessa-Shashemene landscape of the south-central highlands of Ethiopia. The datasets required for the study were generated through both primary and secondary sources. Combination of techniques, including descriptive statistics, GIS-based processing, and regression analyses were employed for data analyses. Changes triggered by the interplay of more than 12 drivers were identified related to social, economic, environmental, policy/institutional, and technological factors. Specifically, population growth, expansion of cultivated lands and settlements, livestock ranching, cutting of woody species for fuelwood, and charcoal making were the top six important drivers of LULC change as viewed by the local people and confirmed by quantitative analyses. Differences in respondents' perceptions related to environmental (i.e., location specific) and socioeconomic determinants (e.g., age and literacy) about drivers were statically significant (P=0.001). LULC changes were also determined by distances to major drivers (e.g., the further a pixel is from the road, the less likelihood of changes) as shown by the landscape level analyses. Further studies are suggested targeting these drivers to explore the consequences and future options and formulate intervention strategies for sustainable development in the studied landscape and elsewhere with similar geographic settings.

Land use change is the main cause of carbon stock changes in terrestrial ecosystems. Studying the impact mechanisms of carbon stock changes in different land use types in the arid zone and simulating future changes in land use and carbon stock under different scenarios will help to formulate a scientific land use policy for the arid zone to promote high-quality and sustainable development in the region. Based on the Xinjiang land use data from 2000 to 2020, the coupled PLUS-InVEST model analyzed the spatial and temporal characteristics of land use and carbon stock in Xinjiang from 2000 to 2020 and predicted the changes in land use and carbon stock in Xinjiang in 2030 under the scenarios of natural development （Z1）, economic development （Z2）, sustainable development （Z3）, arable land preservation development （Z4）, and ecological protection development （Z5）. The results showed that ① From 2000 to 2020, the total value of carbon stock in Xinjiang showed an overall decreasing trend of first decreasing and then increasing, with a total decrease of 4.268 2×108 t. The large amount of grassland degraded into unutilized land was the main reason for the decrease in carbon stock in Xinjiang. ② Analysis of the contribution rate of carbon stock changes in different land use types showed that the main influencing factors for carbon stock changes in cropland, forest land, grassland, watersheds, and unutilized land were natural factors, and the main influencing factors for carbon stock changes in construction land were humanistic and economic. Additionally, the reasons for the changes in carbon stock in Xinjiang were the result of the joint influence of natural factors and humanistic and economic factors. ③ Compared with that in 2020, the five development scenarios of Xinjiang in 2030 kept the trend of increasing carbon stock, among which the sustainable development （Z3） scenario increased 66.723 6×106 t the most. This was the optimal development mode to increase the carbon stock of Xinjiang in the future and consider economic development. The results of the above study can provide a theoretical basis for the future spatial planning of Xinjiang and the realization of the goal of "carbon neutrality".

Oxygen is an important requirement for human life. Oxygen is produced by forests. As population growth increases, the need for land use also increases, so that forest land is deforested. This study aims to determine the dynamics of forest land change and the profile of deforestation in the Maros River Basin. This study uses quantitative analysis with a descriptive approach. The data used in this study is land cover data in the form of shapefiles obtained from the Makassar Region VII Forest Area Consolidation Agency, which is then processed using a Geographic Information System (GIS). The results of the analysis show that for 30 years (1990-2020) the forest area has decreased by 1,057.90 hectares. Changes in forest land to shrubs, settlements/built-up land, savanna/grasslands, bodies of water, mixed gardens, and rice fields. The most preferred residential land use. The results for the deforestation profile in the Maros watershed obtained 4 different types of deforestation profiles, where overall the majority of the Maros watershed are not vulnerable to deforestation AbstrakOksigen merupakan kebutuhan penting bagi kehidupan manusia. Oksigen dihasilkan oleh hutan. Dengan meningkatnya pertumbuhan penduduk, kebutuhan akan penggunaan lahan juga meningkat, sehingga lahan hutan mengalami deforestasi. Penelitian ini bertujuan untuk mengetahui dinamika perubahan lahan hutan dan profil deforestasi di DAS Maros. Penelitian ini menggunakan analisis kuantitatif dengan pendekatan deskriptif. Data yang digunakan dalam penelitian ini adalah data tutupan lahan berupa shapefile yang diperoleh dari Badan Pemantapan Kawasan Hutan Wilayah VII Makassar, yang kemudian diolah menggunakan Sistem Informasi Geografis (SIG). Hasil analisis menunjukkan bahwa selama 30 tahun (1990-2020) luas hutan mengalami penurunan sebesar 1.057,90 hektar. Perubahan lahan hutan menjadi semak belukar, pemukiman/lahan terbangun, sabana/padang rumput, badan air, kebun campur, dan persawahan. Penggunaan lahan perumahan yang paling disukai. Hasil profil deforestasi di DAS Maros diperoleh 4 jenis profil deforestasi yang berbeda, dimana secara keseluruhan sebagian besar DAS Maros tidak rentan terhadap deforestasi.