Forest Gain Research Articles

Land covers associated with forest expansion hot spots in the Nepal Himalaya

Abstract Many regions of the Himalaya are experiencing forest expansion, signifying a widespread forest transition across this fragile mountain system. In the Nepal Himalaya, forest expansion is increasingly attributed to the narrative of cropland abandonment driven by rural outmigration. Understanding forest change dynamics as consequences of land use changes is critical in the context of rural mountain societies such as Nepal, which exhibit complex interactions between forest, agriculture, and livelihoods. We analysed forest gain in Nepal from 2000–2019 to inquire where and when forests have expanded and what land covers transitioned into forests. We integrated spatiotemporal analysis of national land cover maps and photointerpretation of very high resolution (VHR) imagery to improve analytical estimates. The Middle Mountain physiographic region experienced the strongest hot spots of forest gains relative to the high mountains or lowlands. Visual interpretation of VHR showed that 68.8% of forest gains since 2000 occurred on shrubland, and 26.5% on cropland. The dominance of shrubland-forest transitions implied that shrubland is an intermediate stage in a multi-decadal succession process, or a semi-permanent state such as arrested succession. Notably however, we also detected cropland-forest transitions occurring as fast as 6–10 years, indicating the potential for rapid forest succession if biophysical conditions permit. Thus, variations in land cover transitions to forests in the hills implicated multiple pathways of forest regeneration, reflecting a variety of spatiotemporally explicit drivers. Our results differ from previous studies because VHR image analysis accounted for land cover classification errors and improved land cover quantification. Further research on spatially explicit mechanisms and drivers of forest gain are needed to understand the synergies of forest, agriculture, and livelihoods to inform land use land cover policies that could be leveraged to enhance rural mountain livelihoods.

Forest Management Assessment Using LASSO and the Critical List of Variables for Sustaining the Commons: Four Cases in the Monarch Reserve

In the Monarch Reserve, four cases were selected for exploratory analysis using the critical list of variables for sustaining the commons and LASSO regression to determine which of these are most influential in the successful maintenance of forest cover over a decade. Forest recovery from degraded to dense forest is found to be inversely related to resource size, consistent with theory. However, in four other categories of forest change, the size of the resource is less influential, though it is still a significant, variable with a positive relationship to forest cover change which is inconsistent with theory. The analysis also showed that less overlap, greater poverty and lower dependence on the resource are associated both with forest gain and with mitigation of forest loss, which is contrary to theory and most research. Also associated with poverty in the LASSO regression are homogeneity of identity and ease of enforcement of rules. These show a negative relation to transitions from non-forest to dense forest, with low coefficients. This is inconsistent with theory. Possible reasons for the divergence from theory are discussed in detail, as is the utility of this approach for assessment and monitoring.

Evaluating mangrove community forestry outcomes: a mixed synthetic control and field-based approach

Abstract Contemporary evaluations of outcomes in human-managed systems have been constrained by a lack of counterfactual analysis. Community forestry (CF), a widely adopted strategy to achieve both conservation and management in coupled human-environment systems, is no exception, and counterfactual analysis would greatly enhance CF research. We used a mixed method approach incorporating quantification of deforestation and forest regrowth rates, synthetic control analysis, and focus groups discussions to evaluate mangrove CF outcomes in the Ayeyarwady Delta, Myanmar, from to 1990–2021. CF resulted in an overall increase in net forest gain and reduction in net forest loss across sites. More than two-thirds of CF sites had superior outcomes relative to synthetic controls for at least one metric (deforestation or forest regrowth); however, CF tended to perform better for only one outcome while avoiding underperformance in the other. The annual rate of forest regrowth in CFs accelerated beginning three years prior to certification and peaked two years after certification, likely related to pre-certification engagement with the Forest Department or non-government organizations. Moreover, control sites near CFs experienced more rapid forest regrowth than controls further from CFs, suggesting spillover effects. The predominant challenge facing successful CF management was illegal extraction and overharvesting, and poor performing CFs experienced a complex array of challenges facing forest regrowth, likely related to the private nature of individual land claims within the CFs. Most supporting factors for CF were related to community management capacity, strongly indicating a need for extended close engagement with competent government and non-government actors to develop long-term management and governance capacities, which are sustainably funded. Our mixed-method approach can be replicated in other human-managed systems to evaluate the biophysical impacts of policies and gain insights into the underlying drivers of outcomes.

A global annual fractional tree cover dataset during 2000–2021 generated from realigned MODIS seasonal data

Fractional tree cover facilitates the depiction of forest density and its changes. However, it remains challenging to estimate tree cover from satellite data, leading to substantial uncertainties in forest cover changes analysis. This paper generated a global annual fractional tree cover dataset from 2000 to 2021 with 250 m resolution (GLOBMAP FTC). MODIS annual observations were realigned at the pixel level to a common phenology and used to extract twelve features that can differentiate between trees and herbaceous vegetation, which greatly reduced feature dimensionality. A massive training data, consisting of 465.88 million sample points from four high-resolution global forest cover products, was collected to train a feedforward neural network model to predict tree cover. Compared with the validation datasets derived from the USGS circa 2010 global land cover reference dataset, the R2 value, MAE, and RMSE were 0.73, 10.55%, and 17.98%, respectively. This dataset can be applied for assessment of forest cover changes, including both abrupt forest loss and gradual forest gain.

Detecting Vegetation Cover Change in Reforestation Sites from 2013 to 2019 in Central Visayas, Philippines Using Remotely Sensed Data

Assessment of reforestation success is indispensable to document the impact of the project and whether it has achieved the intended objectives. Globally, the use of remotely sensed data is a widely accepted method to track changes in reforestation sites. Hence, this study investigated the changes in Normalized Difference in Vegetation Index (NDVI) using LandSat 8 images to assess vegetation cover in National Greening Program reforestation sites in Central Visayas from 2013 to 2019. Results showed that there was a minimal increase of NDVI (0.002 to 0.07) and a 9% (286 ha) gain of dense vegetation cover in the sampling sites which may be attributed to the reforestation activity. Forest gain was highest in Negros Oriental (262 ha) while Cebu lost 29 ha of dense vegetation. Field data confirmed that tree biomass density (245 Mg/ha) was highest in Negros Oriental and lowest in Cebu (78 Mg/ha). Analysis of variance revealed that there was no significant change in forest cover between 2013 and 2019. The program reported a total of 42,000 ha as their accomplishment in the region which was planted with 500 fast-growing tree species per hectare. If the seedlings successfully survived, this could have increased considerably forest cover in the region by up to 60%. Thus, it is important to revisit the reforestation program’s policies and processes to build on the strengths and avoid mistakes for future projects. It is also vital to integrate cost-effective, accurate, and real-time monitoring schemes that ensure the attainment of the key objectives.

Land-cover change in Cuba and implications for the area of distribution of a specialist's host-plant.

Changes in land cover directly affect biodiversity. Here, we assessed land-cover change in Cuba in the past 35 years and analyzed how this change may affect the distribution of Omphalea plants and Urania boisduvalii moths. We analyzed the vegetation cover of the Cuban archipelago for 1985 and 2020. We used Google Earth Engine to classify two satellite image compositions into seven cover types: forest and shrubs, mangrove, soil without vegetation cover, wetlands, pine forest, agriculture, and water bodies. We considered four different areas for quantifications of land-cover change: (1) Cuban archipelago, (2) protected areas, (3) areas of potential distribution of Omphalea, and (4) areas of potential distribution of the plant within the protected areas. We found that "forest and shrubs", which is cover type in which Omphalea populations have been reported, has increased significantly in Cuba in the past 35 years, and that most of the gained forest and shrub areas were agricultural land in the past. This same pattern was observed in the areas of potential distribution of Omphalea; whereas almost all cover types were mostly stable inside the protected areas. The transformation of agricultural areas into forest and shrubs could represent an interesting opportunity for biodiversity conservation in Cuba. Other detailed studies about biodiversity composition in areas of forest and shrubs gain would greatly benefit our understanding of the value of such areas for conservation.

Siberian Pine and Larch Response to Warming-Drying Climate in the Southern Boundary of Their Range

Trees’ growth and areal responses to changing climate are primarily expected within the edges of the species range. Here, we compared the responses of Siberian pine (Pinus sibirica Du Tour), a moisture-sensitive species, and drought-resistant larch (Larix sibirica Ledeb.) at the southern part of their ranges in the Siberian Mountains (the Tannu-Ola Ridge). We study the species’ growth and proportion in the forests from forest-steppe to treeline ecotone along the elevation gradient. These studies are based on radial growth index (GI) analysis and GI dependence on the climate variables. We used satellite time series to detect the land cover changes (areas of larch and Siberian pine, as well as shrubs and birch). We compared trees’ GI before and after warming “restart” in the late 1990s. Generally, GI dependence on the air temperature was negative at elevations below c. 1600 m a.s.l., whereas GI dependence on the moisture variables (precipitation, vapor pressure deficit, and soil moisture) was positive for both species. Above 1600 m, increasing air temperatures stimulated species growth, whereas the influence of moisture variables was negative (for larch) or neutral (for Siberian pine). After the warming restart, the GI of both conifers increased in moisture-sufficient high elevations and treeline ecotone, whereas within low elevations (<1300 m), the GI was stagnant or suppressed. Both species’, especially Siberian pine, negative growth dependence on air temperature and positive dependence on the moisture variables strongly increased since the warming restart. We found a risen growth dependence of both species on the soil-stored water during the previous year (September–October), which smoothed moisture stress at the beginning of the growing season. Yet both species’ growth also suffered as a result of early spring warms. We found that larch is migrating in both uphill and downhill directions, while Siberian pine is migrating uphill only. Forests loss occurred at low elevations (<1300 m), whereas forest and shrub gain occurred at high (>2000 m) ones. The upper boundary of the forests and shrubs is migrating uphill at rates of about 0.8 and 0.3 m/y, respectively. We observed a decrease in Siberian pine proportion in the forests, whereas areas of larch and birch strongly increased (by 150% and 100%, respectively), which indicates the retreat of Siberian pine from its southern habitat. We suggested afforestation of the areas of Siberian pine mortality by the drought-tolerant larch species.

Monitoring the impact of forest changes on carbon uptake with solar-induced fluorescence measurements from GOME-2A and TROPOMI for an Australian and Chinese case study

Abstract. Reliable and robust monitoring tools are crucial to assess the effectiveness of land mitigation techniques (LMTs) in enhancing carbon uptake, enabling informed decision making by policymakers. This study, addressing one of the scientific goals of the EU Horizon 2020 Land Use Based Mitigation for Resilient Climate Pathways (LANDMARC) project, examines the feasibility of using satellite solar-induced fluorescence (SIF) in combination with other satellite data as a monitoring proxy to evaluate the effects of LMTs on carbon uptake. Two distinct cases are explored: (1) instantaneous vegetation destruction caused by a 2019 eucalyptus wildfire in southeast Australia and (2) gradual forest gain resulting from reforestation efforts in northern China in 2007–2012. The cases are monitored using SIF from the TROPOspheric Monitoring Instrument (TROPOMI) and Global Ozone Monitoring Experiment-2A (GOME-2A), respectively. Comparing the temporal variability in SIF across the affected areas and nearby reference areas reveals that vegetation dynamics changed as a consequence of the land-use changes in both cases. Specifically, in the Australia case, TROPOMI demonstrated an immediate reduction in the SIF signal of 0.6 mW m−2 sr−1 nm−1 (−72 %) over the eucalypt forest right after the fire. Exploiting the strong correspondence between TROPOMI SIF and gross primary productivity (GPP) at the nearby representative eddy covariance Tumbarumba site and through the FluxSat product, we estimate that the Australian fire led to a loss in GPP of 130–200 GgC in the first 8 months after the fire. Over the northern Chinese provinces of Gansu, Shaanxi, Sichuan, Chongqing, and Shanxi, we report an increase in GOME-2A summertime SIF of 0.1–0.2 mW m−2 sr−1 nm−1, coinciding with reforestation efforts between 2007 and 2012. This increase in the SIF signal is likely driven by a combination of increasingly favourable natural conditions and by the reforestation effort itself. A multivariate model that takes into account growth factors such as water availability and maximum temperature as well as satellite-derived forest-cover data explains the observed variability in GOME-2A SIF in the Chinese case reasonably well (R2=0.72). The model suggests that increases in both forest cover and soil moisture have led, in step, to the observed increase in vegetation activity over northern China. In that region, for every 100 km2 of additional forest cover, SIF increases by 0.1 mW m−2 sr−1 nm−1 between 2007 and 2012. Our study highlights that the use of satellite-based SIF, together with supporting in situ, modelled, and satellite data, allows us to monitor the impact of LMT implementation on regional carbon uptake as long as the scale of the LMT is of sufficient spatial extent.

Tropical forest loss alters land surface temperature more than tropical forest gain

Tropical forest loss alters land surface temperature more than tropical forest gain

Asymmetric impacts of forest gain and loss on tropical land surface temperature

Asymmetric impacts of forest gain and loss on tropical land surface temperature

Insight into the imbalance of forest cover change at county level in mainland China during 2000–2020: From the perspective of subdividing forest cover change into forest gain and loss

The significant growth of China's forest resources presents a remarkable case in global vegetation dynamics. However, there is spatial imbalance in forest cover changes, which is not only reflected in regional imbalances, but also between total and net changes. There is still a lack of knowledge in this imbalance. In our comprehensive study, we subdivided forest cover change into forest gain and loss, and calculated the total and net forest change across 2850 county-level administrative units in mainland China, utilizing the Global Forest Change Dataset. The univariate linear regression and the Sen + Mann-Kendall test is utilized to explore the temporal trends in forest loss. Additionally, spatial autocorrelation models were employed to examine the spatial differentiation of forest cover change. Our findings unveiled a wide spectrum of forest loss rates ranging from 0% to 27%, and gain rates ranging from 0% to 10%, with the highest rates observed in the southern counties. We observed that the forest loss trends in southeastern counties showed significant and slight increases, while the northwestern counties experienced significant and slight decreases, with the Huhuanyong Line serving as the demarcation. In terms of spatial patterning, quite a number of the southern counties displayed a “large net change, large total change” state, which contrasted with diverse patterns observed in certain western regions. Spatial autocorrelation analysis showed that the High-high clusters of both forest gain and loss were primarily observed in southern counties, with the Low-low clusters in the northwest. This detailed analysis provides valuable insights into the dynamics of forest cover changes, offering a robust scientific foundation for developing informed and region-specific conservation policies.

MONITORING FOREST DEGRADATION OVER FOUR DECADES USING REMOTE SENSING AND MACHINE LEARNING CLASSIFICATION ALGORITHMS IN BOUSKOURA, MOROCCO

Abstract. A large portion of Morocco’s forest ecosystem is being damaged by human activity and climate change, which makes it crucial for the country to monitor forest dynamics and develop measures to counter these effects. The objective of this study was to determine how the forest cover has changed over the past four decades in Bouskoura forest, Morocco. Based on Remote Sensing (RS), Geographic Information System (GIS) and machine learning algorithms. Throughout the process, Spatial data such as Landsat 5 TM, Sentinel-2B, and spectral indices, including NDVI, NDWI, NDBI, and MSAVI2 were used to train/validate Random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers. By comparing the performance of the three classifiers for all four periods, the RF method was the most effective with an overall accuracy of 0.99 and kappa coefficient of 0.99 for 1991, 2001 and 2011, and an overall accuracy of 0.99 and kappa coefficient of 0.98 for 2021 . Therefore, the RF was selected as a method of examining time variations. The results indicated that forests covered an area of 20,41 km2 in 1991 which has decreased to 18,96 km2 in 2021, a loss of 1,45 km2 (7.10%) in four decades. The highest forest loss was 2,69 km2 during 1991–2001, 2,12 km2 during 2001–2011, 1,40 km2 during 2011–2021. And the highest forest gain was found to be 3,75 km2 during 2011–2021, 0,61 km2 during 2001–2011, 0,43 km2 during 1991-2001. Recent declines in forest degradation attest to the benefits of initiatives to conserve the environment taken by the country.

Application of Eucalyptus Agroforestry to reduce poverty in rural areas in Jember Regency, East Java, Indonesia

State forest management on the island of Java is monopolized by Perhutani Public Company as a state company with the community through a social forestry partnership. Various agroforestry patterns are applied to utilize forest land and gain benefits. The application of eucalyptus agroforestry in a village in Jember district, East Java is expected to reduce erosion and poverty problems in rural areas. The application of eucalyptus agroforestry which is a new pattern provides various perspectives for village communities. therefore, this study aims to (1) determine the application of various eucalyptus agroforestry patterns, (2) determine the income level of farmers in various eucalyptus agroforestry patterns and their contribution to household incomes and (3) analyse livelihood strategy of eucalyptus agroforestry farmer. The data analysis method using descriptive and quantitative analysis of eucalyptus agroforestry pattern, farmer’s income and contribution analysis to 36 forest farmers and also livelihood strategy. The research results show: (1) Application of the agroforestry system on social forestry partnership using a simple agroforestry system with eucalyptus agroforestry pattern of trees along border, random mixture, and alternate rows; (2) eucalyptus agroforestry income on land area <0.5ha is Rp. 7.955.013/year, land area 0.5-1ha Rp. 20,777,388/year, and land area >1 ha Rp. 23,853,189/year with the contribution of agroforestry to household income is moderate (between 41% until 61% of forest farmer household income). (3) most livelihood strategy of eucalyptus agroforestry farmer is most (81%) is multiple/diversification work most in agriculture.

30 m Resolution Global Maps of Forest Soil Respiration and Its Changes From 2000 to 2020

AbstractThe soil respiration (Rs) of forests is a major component of global Rs, yet few studies have focused on it. This study aimed to estimate global forest Rs and its changes at a resolution of 30 m via an artificial neural network (ANN) model. Five input candidates representing forest type, climatic, soil, and geographical information, as well as 1472 satisfactory forest Rs records, were used to build the ANN model and evaluate the model performance via a 10‐fold cross‐validation scheme. Global forest change data sets were used to accurately define the extent of forests and their changes, which was achievable because of the dynamic information and high resolution (30 m) of the data sets. The results indicate that the average annual global forest Rs from 2000 to 2020, as estimated by the optimal ANN model with an r2 value of 0.67 and a root‐mean‐square error of 252.6 g C m−2 yr−1, was 46.24 ± 5.86 Pg C yr−1. From 2001 to 2019, the average theoretical annual global forest Rs loss was 0.22 ± 0.06 Pg C yr−1 due to an average forest loss area of 23.4 million ha yr−1. In addition, the annual Rs theoretically increased by 0.75 Pg C in 2012 due to a global forest gain area of 80.5 million from 2001 to 2012. The presented data sets of global forest Rs and its changes can provide an accurate benchmark for discussing the carbon cycle and climate change at global to regional scales, even when operating over a small forest area (i.e., dozens of ha), which is a scale that has been ignored in other global Rs studies.

Forest-Cover Changes in European Natura 2000 Sites in the Period 2012–2018

Protected areas have a key role in preserving biodiversity at different scales, as well as in providing ecosystem services to rural communities. Natura 2000 is the primary conservation network at the EU level, with the aim of protecting the most valuable species and habitats; it covers around 18.6% of the EU’s land area. The aim of this study is to assess the evolution of forest cover in EU Natura 2000 sites in the period 2012–2018 through GIS-based spatial analyses of the High-Resolution Layers produced in the framework of the Copernicus initiative. In 2018, fifteen EU countries had more than 50% of their surface covered by forests, with the top three countries being Slovenia (71.9%), the Czech Republic (70.5%), and Slovakia (69.3%). In 2012–2018, the net forest cover increase in EU Natura 2000 areas was equal to 105,750 ha/year (+1.7%). France, Bulgaria, and Germany recorded the greater net forest cover increase: 303,000 ha, 267,000 ha, and 150,000, respectively. France also recorded the highest yearly rate of forest gain (+51,491 ha/year). Most of the forest gain in EU Natura 2000 areas was found to be located between 0 and 200 m a.s.l. The study demonstrated that forest cover in EU Natura 2000 areas is increasing, with a consequent reduction of open spaces, homogenization of rural landscapes, and loss of landscape-scale biodiversity. The management and design of EU protected areas should consider the importance of preserving biodiversity-friendly land uses and practices, instead of promoting a diffuse “rewilding” with negative consequences for the landscape complexity and heterogeneity, as well as for biodiversity.

Hybrid feature selection based ScC and forward selection methods

Operational data is always huge. A preprocessing step is needed to prepare such data for the analytical process so the process will be fast. One way is by choosing the most effective features and removing the others. Feature selection algorithms (FSAs) can do that with a variety of accuracy depending on both the nature of the data and the algorithm itself. This inspires researchers to keep on developing new FSAs to give higher accuracies than the existing ones. Moreover, FSAs are essential for reducing the cost and effort of developing information system applications. Merging multiple methodologies may improve the dimensionality reduction rate retaining sensible accuracy. This research proposed a hybrid feature selection algorithm based on ScC and forward selection methods (ScCFS). ScC is based on stability and correlation while forward selection is based on Random Forest (RF) and Information Gain (IG). A lowered subset generated by ScC is fed to the forward selection method which uses the IG as a decision criterion for selecting the attribute to split the node of the RF to generate the optimal reduct. ScCFS was compared to other known FSAs in terms of accuracy, AUC, and F-score using several classification algorithms and several datasets. Results showed that the ScCFS excels other FSAs employed for all classifiers in terms of accuracy except FLM where it comes in second place. This proves that ScCFS is the pioneer in generating the reduced dataset with remaining high accuracies for the classifiers used.

Spatiotemporal Vegetation Variability and Linkage with Snow-Hydroclimatic Factors in Western Himalaya Using Remote Sensing and Google Earth Engine (GEE)

The mountain systems of the Himalayan regions are changing rapidly due to climatic change at a local and global scale. The Indian Western Himalaya ecosystem (between the tree line and the snow line) is an underappreciated component. Yet, knowledge of vegetation distribution, rates of change, and vegetation interactions with snow-hydroclimatic elements is lacking. The purpose of this study is to investigate the linkage between the spatiotemporal variability of vegetation (i.e., greenness and forest) and related snow-hydroclimatic parameters (i.e., snow cover, land surface temperature, Tropical Rainfall Measuring Mission (TRMM), and Evapotranspiration (ET)) in Himachal Pradesh (HP) Basins (i.e., Beas, Chandra, and Bhaga). Spatiotemporal variability in forest and grassland has been estimated from MODIS land cover product (MCD12Q1) using Google Earth Engine (GEE) for the last 19 years (2001–2019). A significant inter- and intra-annual variation in the forest, grassland, and snow-hydroclimatic factors have been observed during the data period in HP basins (i.e., Beas, Chandra, and Bhaga basin). The analysis demonstrates a significant decrease in the forest cover (214 ha/yr.) at the Beas basin; however, a significant increase in grassland cover is noted at the Beas basin (459 ha/yr.), Chandra (176.9 ha/yr.), and Bhaga basin (9.1 ha/yr.) during the data period. Spatiotemporal forest cover loss and gain in the Beas basin have been observed at ~7504 ha (6.6%) and 1819 ha (1.6%), respectively, from 2001 to 2019. However, loss and gain in grassland cover were observed in 3297 ha (2.9%) and 10,688 ha (9.4%) in the Beas basin, 1453 ha (0.59%) and 3941 ha (1.6%) in the Chandra basin, and 1185 ha (0.92%) and 773 ha (0.60%) in the Bhaga basin, respectively. Further, a strong negative correlation (r = −0.65) has been observed between forest cover and evapotranspiration (ET). However, a strong positive correlation (r = 0.99) has been recorded between grassland cover and ET as compared to other factors. The main outcome of this study in terms of spatiotemporal loss and gain in forest and grassland shows that in the Bhaga basin, very little gain and loss have been observed as compared to the Chandra and Beas basins. The present study findings may provide important aid in the protection and advancement of the knowledge gap of the natural environment and the management of water resources in the HP Basin and other high-mountain regions of the Himalayas. For the first time, this study provides a thorough examination of the spatiotemporal variability of forest and grassland and their interactions with snow-hydroclimatic factors using GEE for Western Himalaya.

Machine Learning-Based examination of recent mangrove forest changes in the western Irrawaddy River Delta, Southeast Asia

Mangrove forests serve as a significant carbon sink and provide effective shoreline protection against devastating winds. However, these forests are facing an unprecedented decline in tropical and subtropical large deltas due to human activities and climate change. This study focuses on quantifying the dynamic changes of mangrove forests in the Western Irrawaddy River Delta (WIDR), which is part of Myanmar’s largest delta in Southeast Asia, Remote sensing images from 1988 to 2022 were utilized alongside a machine learning approach to analyze these changes. The findings demonstrate a significant decline of mangrove forests within the WIRD, with a reduction in area by 45.35% over the past 35 years. Additionally, the patches of mangrove forests have become increasingly fragmented. Losses predominantly occurred in the inland regions, while gains were observed along the seaward edges, suggesting progradation towards the sea, which compensated for a net loss of 2,812.32 ha during the study period. The intensification of human activities, specifically deforestation and aquaculture pond utilization, appears to be the leading cause of catastrophic internal degradation within the WIRD’s mangrove forests. Contrary to the influence of local sea level rise or variations in suspended sediment discharge into the WIRD, this study suggests that the persistent retreat of mangrove forest fringes is controlled by large waves generated by the southwest monsoon. Estuarine barriers situated in the WIRD act as buffers, dissipating wave energy and facilitating seaward growth. of mangrove forests. Our study reveals a significant pattern in the WIRD, where landward mangrove forest loss coincided with seaward mangrove forest gain. This work underscores the importance of addressing landward mangrove forest loss and recognizing the emergence of seaward gains in the WIRD, providing valuable insights for fostering successful and sustainable mangrove management and protection initiatives.

Monitoring forest gain and loss based on LandTrendr algorithm and Landsat images in KTH Pati social forestry area, Indonesia

Social forestry schemes are now being implemented in numerous state forest areas in Indonesia, aiming to reduce deforestation and improve the community’s livelihood. However, spatial monitoring in the social forestry area is still limited to see how the implementation progresses. The present study aimed to identify the change of forest taking a case in Pati Forest Farmer Communities (KTH Pati) social forestry area from 1996 to 2022 using the LandTrendr algorithm based on Normalized Burn Ratio (NBR) value of Landsat image series. The results detected forest loss and gain covering an area of 453.97 ha and 494.18 ha, respectively. Two main reasons causing the forest loss are the country’s financial and political situation from 1997 to 2003 and the harvest of forest plantations in 2017–2018. However, it was found that the study area had a positive forest gain with the current continuous growth of 292.32 ha (20.16% of the total area). Even though the social forestry policy has not significantly shown a positive impact on forest growth, spatial monitoring through remote sensing can be a great tool for observing the progress.

USING REMOTE SENSING TO DETECT FOREST COVER CHANGE IN SAM HOUSTON NATIONAL FOREST, TEXAS

Abstract. The Sam Houston National Forest is a large, forested area in Texas that has experienced significant land-use changes over the past few decades. The study area replicates plentiful climatic, physiographic, and edaphic differences in the country and this forest faces a serious problem of degradation and disturbance of different nature. In this study, we utilized remote sensing technology specifically Landsat 4 ETM and Landsat 8 from USGS Earth Explorer with spatial resolution 30 m, to analyze forest cover change in Sam Houston National Forest from 2001 to 2020. We also employed the Hansen Global Forest Cover Data from the Google Earth Engine Catalogue to assess the forest cover loss and gain within the study period. Also, the i-Tree software was used to estimate carbon sequestration in the forest and assess the potential benefits of forest management practices. Results of the study showed that the Sam Houston National Forest has experienced a net loss of forest cover over the past few decades, primarily due to agricultural expansion and urbanization. However, the forest has also shown signs of regrowth and recovery in certain areas, highlighting the potential for effective forest management practices to promote carbon sequestration and conservation. Overall, our study highlights the importance of remote sensing technology for understanding forest cover change and its implications for carbon sequestration and climate change mitigation.