Cover Change Research Articles

The effects of land cover transition and its patch mosaics on soil erosion using geospatial technology in South Wollo, Ethiopia

Soil erosion has been a significant threat to agricultural productivity, dam sustainability, and ecosystem services in the highlands of Ethiopia. Despite some watershed-specific research on this problem, the spatiotemporal distribution of soil erosion risks is still rarely assessed in South Wollo. This study aims to monitor land cover changes, analyze landscape mosaics, and estimate soil loss in South Wollo from 1990 to 2020. The Revised Universal Soil Loss Equation (RUSLE) was utilized to estimate soil loss. Key informant interviews and field observations were conducted to gain further insights into land cover change and soil erosion. The results showed that agricultural land use was dominant, covering 50.75 % of the study area in 1990 and it continued to expand at an annual rate of 1.73 %, primarily on shrubland and grassland. The landscape structure was more heterogeneous in the north compared to the central and southern areas. In 1990, the mean soil loss was 21.13 t ha-1yr−1, totaling 41.53 million tons. By 2020, These figures had increased to 28.97 t ha-1yr−1 and 49.86 million tons, respectively. Areas experiencing very severe soil erosion (>50 t ha-1yr−1) expanded from 10.81 % (1990) to 13.57 % (2020). The magnitude of soil loss also differed among land cover types, with the highest rates observed in bare land (>85 t ha-1yr−1), followed by agricultural land (>30 t ha-1yr−1). Soil loss factors and landscape metrics exhibited a significant correlation with soil loss, though the strength and direction of these interactions varied. Land cover change and its associated soil erosion were primarily exacerbated by policy factors, including tenure insecurity and disregard for farmers’ indigenous conservation knowledge. This study offers valuable insights into the trends of land cover transitions, landscape structure, and soil loss. It will help guide sustainable land management, aimed at increasing green legacy and reducing soil loss.

Surat’s sustainable future: analyzing compact urban development and population density trends

Rapid urbanization is a challenge for the cities of developing countries like India. The share of an urban area is rapidly increasing in dynamically growing cities. Due to this, the land use is altered, causing sprawl and unplanned development. The consumption of resources is greater, and the impact on the surroundings is negative. The compact city concept is always more efficient and cost effective compared to unplanned organic growth. The compact city optimizes available land resources with its maximum possible capacity in accordance with other available natural resources. The primary study objective is to assess the growth trend of Surat city using satellite images, geographic information system techniques, and census data for the last few years. The analysis considers population density for different city wards from 1991 to 2020. Based on the land use land cover change carried out using the maximum likelihood method from the satellite images, the rate of spreading out, its direction, and causes are also found. In this study, population density is a crucial attribute of the compact city concept. The study shows that wardwise density is increasing in a specific direction and making that portion of the city a dense, compact development. This is observed in ward numbers 48, 42, and 35 in the east, north, and southeast directions from Central Business District, respectively. The average population density in persons per sq. km is continuously increasing from 5006 to 20329 at city level which also shows the compact city development for Surat. The governing authorities must incorporate the compact city concept into the planning policy for sustainable urban development. By this, policymakers can understand housing needs to promote affordable housing, invest in infrastructure development and public services, and identify areas where sustainable transportation options, such as public transit, walking, or cycling, need improvement.

Cloud removal in landsat images based on spatial-spectral-temporal random forests

Abstract The Landsat-8 Operational Land Imager (OLI), together with the Landsat-9 OLI2 sensors, provides important optical data for various applications. Due to frequent cloud cover, the obtained Landsat images always contain different degrees of spatial information loss. Although great efforts have been made to remove clouds in remote sensing images in recent decades, there still exists a large challenge, that is, the land cover changes between the target cloudy and the auxiliary cloud-free images, which limits greatly the accuracy of cloud removal. To cope with this issue, a spatial-spectral-temporal random forest (SSTRF) algorithm was proposed in this paper. The SSTRF model aims at removing clouds in Landsat images with the assistance of a remote sensing image with spatially complete cover from another satellite, that is, the 8-day MODIS surface reflectance product (MOD09A1). By compositing the valid data from 8 separate days, the MOD09A1 product has a great possibility to provide cloud-free land cover change information for the cloud removal of Landsat images. Through experiments with simulated and real clouds, SSTRF was demonstrated to produce greater accuracy than two benchmark cloud removal methods and has great potential to be applied to practical use.

Identification of wetland cover change in the upstream of Negara and Barito river in 1988 – 2014

Identification of wetland cover change in the upstream of Negara and Barito river in 1988 – 2014

Separation of the Urbanization Effects on Thermal Environment

Rapid urbanization leads to changes in the thermal characteristics of urban areas. Despite cities serving as natural laboratories to investigate responses to global change, it remains unclear how urbanization affects the thermal environment during global warming. Urbanization effects can be categorized as either the effect of urban climate (UCE) or land cover change (LCCE). This study proposes a conceptual framework for separating the urbanization effects on the thermal environment in 29 major Chinese cities. Results show that land surface temperature increases with impervious surface fractions in the urban area and decreases with vegetation fractions in rural areas, resulting in significant UCE and LCCE across cities, with LCCE approximately 1.3 to 5.7 times greater than UCE. These findings provide a comprehensive understanding of the thermal environment change caused by urbanization. To mitigate the negative effects of urbanization on the thermal environment, reducing land cover change would be more effective.

Spatio-temporal tendencies of urban land surface temperature on the Andean piedmont under climate change: A case study of Metropolitan Lima, Peru (1986–2024)

The increase in land surface temperature (LST) in megacities has contributed to global warming due to poor urban planning and high population concentration. This study analyzes spatio-temporal trend patterns of LST in the city of Metropolitan Lima, Peru, during the summers of 1986–2024. The Mann-Kendall tests and the Theil-Sen Slope method were used to analyze the spatiotemporal trends of LST, relating R2 and the Mann-Kendall p-value of the annual average LST in each district. A hierarchical cluster analysis was performed to group districts according to their average yearly LST. Urban heat islands were identified based on basin configuration and distance to the sea at 1 km intervals. The results reveal a significant increase in LST (p<0.001) related to El Niño-Southern Oscillation phenomena, in addition to urban growth and land cover change. The significant positive trend of LST showed a heterogeneous distribution in all districts. The districts were grouped into three clusters with statistically significant differences in LST (p<0.001), spatially configured radially from the sea to the foot of the Andes, up to 1179 m a.s.l. Urban heat islands did not correlate with significant positive trends in basins. Still, they showed a considerable increase in LST in areas of high economic activity, including dense commercial, industrial, and residential areas. This information is crucial to managing climate change adaptation and mitigation measures and contributing to sustainable urban planning focused on the population’s well-being.

Peatlands changes analysis in Banjar District using three decades of Landsat imagery

Abstract Peatlands are a multifunctional wetland ecosystem, so it is essential to know the changes in their presence. Observation of the changes in peatlands can be done in two ways, i.e., indirect and direct observation. Indirect observation can be done with remote sensing technology. This study aimed to analyze the distribution and peat land cover change from 1990 to 2020 and calculated the land’s rate changes. This research uses the maximum likelihood classification with seven classes of land cover input of agriculture, plantations, forests, bare lands, settlements, shrubs, and water bodies. The classification result covers a map of Banjar Regency peatlands covering 1990, 2000, 2009, and 2020, and then analyzes to know the land’s rate changes. The results of this research inform Banjar’s peatland area is 58,048.76 ha, which is spread over eight districts, i.e., Astambul, Beruntung Baru, Gambut, Martapura, Martapura Barat, Mataraman, Simpang Empat dan Sungai Tabuk. The land’s rate changes in 1990 and 2000; almost all of the land cover has the addition of wide each year, most of it is shrubs around 870.47 ha per year, only covering the reduction of forest land with an area of approximately 1,293.80 hectares per year. In 2000 to 2009 shows that four land covers experience and increase in area per year, namely settlements 11.71 ha, agriculture 30.69 ha, water bodies 6.76 ha and the largest oil palm plantation 1,098.5 ha. While those that experienced a reduction in area per year were bare land around 335.61 ha and shrubs around 812.11 ha. While the rate of change from 2009 to 2020 which experienced an increase in area each year was 15.19 ha of water bodies, 422,9 ha of bare land, 1,073.07 ha of oil palm plantations and around 1,508.2 ha of agriculture. While those that experienced a reduction in area per year were shrubs 3,184.2 ha, and settlement 32.76 ha. The data from these findings is crucial to the national wetlands inventory database for upcoming assessments, even though they offer valuable insights into using conservation measures in the context of wetland sustainability.

Exploring the dynamics and future projections of land use land cover changes by exploiting geospatial techniques; A case study of the Kabul River Basin

Global land cover change has caused significant environmental degradation and biodiversity loss. It affects ecosystem functions, livelihoods, and climate variation and has drawn substantial attention in recent decades. In the Kabul River Basin (KRB), there are limited studies on the historical Land Use/Land Cover (LULC) pattern, transition, intensity and future perspective. Therefore, this study aims to investigate long-term LULC changes and major drivers of LULC in the KRB over the past thirty years (1990–2020) and then to project the future LULC pattern for the years 2030, 2040 and 2050. Landsat Imageries of (1990–2020) were used as input data by utilizing the Random Forest Classifier algorithm (RF) in the Google Earth Engine (GEE) to classify the LULC. The LULC was then projected for the future, using the Cellular Automata Markov Chain Model (CA-MCM). The results demonstrated drastic LULC changes, controlled primarily by urbanization and agriculture expansion, which expanded from 467 Km2 (0.7 %) to 2312 km2 (3.4 %) and 6528 km2 (9.6 %) to 10812 (15.9 %), between 1990 and 2020. In contrast, bare land decreased from 70606 km2 (82.1 %) to 48212 km2 (70.9 %) between 1990 and 2020. In addition, the study depicts that the expansion in built-up and vegetation areas in the KRB during the study period were at the utilization of bare land. Future LULC predictions indicated that between 2020 and 2050, bare land would trend downward from 48212 km2 (70.9 %) to 46172 km2 (67.9 %), while vegetation and built-up areas would trend upward from 2312 km2 (3.4 %) to 3640 km2 (5.3 %), 10812 km2 (15.9 %) to 11622 km2 (17.1 %), and water bodies and snowcover would slightly vary from 1.2 % to 0.9 % and 7.9 %–9.0 %. In addition, the results of LULC dynamics reveal a significant strong positive correlation between population and built, as well as population and vegetation. Conversely, there is a strong negative correlation between population and bare land. Our results provide precise insights on LULC patterns and trends in the KRB, which could be employed to design a sustainable framework for land use and ecosystem protection.

Examining the relationship between land-use cover change and sociodemographic characteristics: A case study of Madison County, Tennessee

Examining the relationship between land-use cover change and sociodemographic characteristics: A case study of Madison County, Tennessee

Influence of urban functional zone change on land surface temperature using multi-source geospatial data: A case study in Nanjing City, China

The urban heat island effect is a prevalent urban concern, and studies on its influencing factors are essential to mitigate its negative effects and promote sustainable urban development. Although many studies have revealed the impact of land cover change on land surface temperature (LST) during urbanization, most have focused only on built-up areas without considering the relationship between urban internal functional structure and LST. To clarify the influence of changes in urban functional zones (UFZs) on LST, focusing on the downtown area of Nanjing City, China, this study proposed an effective framework using multi-source geospatial data for qualitative and quantitative analysis. Specifically, very high spatial resolution images and point of interest data were jointly used to generate UFZ maps using an advanced deep learning approach, and LST distribution maps were retrieved from Landsat 8 images using the radiative transfer equation. The UFZ and LST maps were then overlaid and analyzed to reveal the impact of UFZ change on LST. The results showed that in 2013, 2018, and 2022, the residential region constituted the largest proportion of area, comprising 23.52%, 24.52%, and 27.28%, respectively. The areas of unused region and transport region experienced the most rapid decrease and increase of 6.08% and 4.05% from 2013 to 2022, respectively. Furthermore, the area of sub-high temperature zone decreased the most, by 3.47%, whereas that of the high-temperature zone increased the most, by 6.05%. In the study area, water and green space maintained relatively low LSTs, whereas residential region and unused region exhibited higher LSTs. From 2013 to 2022, UFZ changes contributed to a 0.71°C increase in LST. Transfers from green space, water, commercial region, transport region, and industrial region mostly resulted in a higher LST, which was associated with a decrease in fractional vegetation cover (FVC). Transfers from unused region, residential region, and public service region mostly contributed to reducing LST, which was caused by the increase in FVC. The findings and the proposed framework can provide reliable references for urban managers to optimize urban spatial layout and promote sustainable urban development.

Reviews on the Land Use and Land Cover Changes in Bangkok

Remote sensing has been a highly effective and efficient method for spatial and socio-political analyses for several decades. It utilizes satellite images to map land use and land cover (LULC) and detect changes in this regard. One of Southeast Asia’s megacities, Bangkok has undergone significant land use dynamics and related socioeconomic activity changes. Recently, the new Land and Buildings Tax Act B.E. 2562 (A.D. 2019) has been implemented, which could impact land-use changes, particularly in vacant lands. This study aims to review the literature on LULC changes in Bangkok since the implementation of the land tax and to provide a summary of the main findings. The reviews indicate that previous literature on LULC in Bangkok has primarily focused on environmental or spatial factors, rather than socio-economic ones. This highlights a gap in our understanding of the socio-economic and LULC impacts of land taxation.

Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru

Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru

Temporal dynamics of land use land cover (LULC) changes and assessment of environmental and climatic parameters using remote sensing and GIS: A case study in Limboti Reservoir, Loha Taluka, Maharashtra, India

In this study, multispectral satellite data from Landsat 4-5 TM and Landsat 8 OLI/TIRS was used to analyse Land Use Land Cover (LULC) change detection and to retrieve environmental parameters. The LULC change detection was done between 2007 and 2021. This study used the supervised classification-maximum likelihood approach in ArcGIS 10.3 software to detect LULC in Loha Taluka, Maharashtra. The taluka was classified into four major LULC classes, viz. water, vegetation, settlement and barren-land. Changes in LULC directly or indirectly will also induce a change in the environmental parameters. Environmental parameters (water surface temperature, chlorophyll-a concentration and total suspended solids) were analysed for Limboti Reservoir. Data were predicted for next two years (2022 and 2023) using multiplicative seasonal decomposition and Holt-Winter’s multiplicative method and trends of each parameter were analysed using Mann-Kendall trend method. For both years, vegetative land was the most extensive LC in Loha Taluka, accounting for more than 60% of the total land area. However, throughout these years, urbanisation was rampant and vegetative land was converted to settlement. In these years, open water resources such as reservoirs, lakes and rivers covered a very small percentage of the total area, which is a serious threat to the ecosystem. As a result, proper water body management is essential; otherwise, these resources will be destroyed and will no longer be able to contribute to the area’s socioeconomic growth. Keywords: Change detection, Chlorophyll content, Land use land cover, Water quality, Water surface temperature

THE ROLE AND IMPACT OF VEGETATION ON THE URBAN FABRIC. CASE OF GUELMA CITY

In cities, land use changes caused by various human activities can affect how natural ecosystems function. In this context, it is increasingly important for cities to consider the role of vegetation in preserving a sustainable environment. A diachronic analysis of landscape changes was applied to assess the presence and distribution of vegetation to determine whether it was evolving or regressing. This phenomenon was studied within the current administrative boundaries of the city of Guelma (Algeria). For this purpose, as an approach based on landscape metrics, several Landsat TM and ETM+ remote sensing satellite images were used throughout the period 1987-2019. A set of landscape indices, including NP, AREA_MN, LPI, PLAND, AI and LSI, were calculated to map land cover, the mechanism of land cover and vegetation change, and their impact on the urban ecosystem. The geo-statistical procedure was carried out using a geographic information system Qgis combined by statistical software using Fragstats to calculate various landscape metrics at class level for the analysis of fragmentation . The results of the landscape metric analysis show that the decrease in average area and the increase in the number of green patches are important indicators of land degradation, meaning that the mechanism of landscape degradation and transformation is progressive. This underlines the need to give particular attention to land use and land cover in the region to ensure the sustainable allocation of natural resources.

Forest Restoration through Village Common Forests in the Chittagong Hill Tracts of Bangladesh: The Role of NGO Interventions

Non-Governmental Organizations (NGOs) are widely recognized for their support of biodiversity conservation and forest restoration in tropical developing countries. In the Chittagong Hill Tracts (CHTs) of Bangladesh, ethnic forest-dependent communities have long maintained the Village Common Forest (VCF), an ancestral forest managed for community well-being. In recent years, some VCFs in the CHT have received support from local and international NGOs due to their perceived role in supporting biodiversity, ecosystem services, and improving local livelihoods. Although such support remains limited, its impacts on forest restoration and vegetation are not yet fully investigated. In the present study, we used remote sensing techniques and field surveys to measure vegetation cover changes and tree diversity in selected VCFs in the area. Our findings indicate that vegetation condition and tree diversity were better in NGO-supported VCFs compared with those without NGO intervention. Based on our study, we recommend increasing NGO involvement in VCF management with a more holistic and ecosystem-based approach, fostering institutional cooperation addressing land ownership issues, developing a market for forest ecosystem services, and improving local capacity for science-based forest management and ecosystem restoration in the CHT.

Spatio-temporal analysis of urbanization effects: unravelling land use and land cover dynamics and their influence on land surface temperature in Aligarh City

ABSTRACT Rapid urbanization causes significant changes in land use and land cover (LULC), contributing to Urban Heat Islands (UHIs) and environmental challenges. This study analyzes two decades of Landsat satellite imagery to examine LULC changes and their impact on Land Surface Temperature (LST) in Aligarh City, India. Supervised classification using maximum likelihood techniques produced LULC maps with 85% accuracy. The results show a 21.10% increase in built-up areas, from 2338.83 hectares in 2001 to 2832.66 hectares in 2021, alongside a 24.55% decline in vegetation, from 620.28 hectares to 467.91 hectares. LST increased by 11.35°C, with a total maximum rise of 10.70°C between 2001 and 2021. Regression analysis revealed strong correlations between LST and spectral indices, with NDVI showing a negative correlation (R² = 0.89, 0.83, 0.78) and NDBI a positive one (R² = 0.87, 0.89, 0.79). The Aligarh main city area consistently recorded higher LST values than the AMU campus, with 88.60% of the city area in the 40–45°C range by 2021, compared to 52.87% for the campus. This study underscores the need for sustainable urban strategies to mitigate UHI effects and promote climate resilience.

Revolutionizing Land Cover Analysis: A Systematic Review of Geospatial Intelligence with Classification and Segmentation

In today’s fast-paced technological landscape, artificial intelligence (AI) is revolutionizing industries, with geospatial analysis standing at the forefront of this transformation. The process of land cover classification, which involves categorizing different types of land surfaces—such as forests, urban areas, water bodies, and agricultural fields—has traditionally been plagued by inconsistencies and inaccuracies. These shortcomings have led to a variety of pressing real-world issues. Misclassified land cover data can result in the inefficient allocation of resources, where critical areas may be overlooked while less urgent regions receive attention. Additionally, the failure to accurately monitor land cover changes can allow illegal activities, such as deforestation, to go unnoticed, resulting in severe environmental degradation. Similarly, unmonitored topographical changes, like unauthorized construction projects, can significantly alter landscapes without regulatory oversight, posing risks to both the environment and public safety. Unchecked forest fires, exacerbated by delayed detection due to poor land cover classification, can spread rapidly, causing widespread damage. Furthermore, inaccurate monitoring of border fences can lead to security vulnerabilities and geopolitical tensions. Collectively, these issues contribute to the escalating challenges of climate change and urbanization, highlighting the critical need for more precise and reliable land cover classification methods. In response to these challenges, our study seeks to explore the potential of advanced machine learning (ML) techniques to revolutionize land cover classification. We leverage publicly available geospatial datasets, specifically EuroSAT and DeepGlobe, which provide comprehensive satellite imagery data across various regions. The focus of our research is on two primary tasks: Image Classification and Semantic Segmentation. Image Classification involves categorizing entire satellite images into specific land cover classes, providing a broad overview of the landscape. In contrast, Semantic Segmentation is a more granular approach that labels each pixel in an image according to its land cover class, offering detailed insights into the spatial distribution of different land types. To conduct a thorough analysis, we evaluate the performance of several cutting-edge models in these tasks. For Semantic Segmentation, we employ Meta AI’s Segment Anything Model (SAM), which is recognized for its ability to segment objects within images with high precision, and the U-Net architecture, a model that has been widely used in medical image analysis and has proven effective in various segmentation tasks. For Image Classification, we use the VGG and ResNet models, both of which are highly regarded in the field of computer vision for their capacity to extract detailed features from images and classify them with high accuracy. The primary objective of our research is to assess how these models perform when applied to land cover classification tasks and to identify their strengths and weaknesses in this specific context. By analyzing their performance, we aim to provide valuable insights that can guide future research efforts and help improve the accuracy and reliability of land cover classification methods. Additionally, our study seeks to highlight new opportunities for enhancing the monitoring and management of land resources, which is crucial for addressing the environmental and urbanization challenges that the world faces today. Our research aspires to contribute to the geospatial field by offering practical recommendations for utilizing machine learning techniques to improve land cover classification. By addressing the limitations of current methods and proposing more effective solutions, we hope to support the development of tools that are capable of accurately monitoring land cover changes and responding to the complex environmental and societal challenges of our time.

Examining the impact of land use and land cover changes on land surface temperature in Herat city using machine learning algorithms

Examining the impact of land use and land cover changes on land surface temperature in Herat city using machine learning algorithms

Attribution of climate change and human activities to spatiotemporal changes of ecological service value in Yunnan Province of China

Quantifying the influence of climate change and human activities on spatiotemporal changes of regional ecological service value (ESV) can provide key information for regional ecosystem protection. However, few studies have distinguished the contributions of climate change and human activities to the total ESV. In this study, Yunnan Province in Southwest China with high biodiversity and ESV was selected as a representative region for the attribution analysis of ESV changes. The Carnegie-Ames Stanford Approach (CASA) was used to calculate the vegetation net primary productivity (NPP), and improved equivalent factor method was used to calculate ESVs of different ecosystem service functions directly from NPP. Then, the multi-factor attribution analysis method was used to quantify the contribution rates of climate change and human activities to changes in ESV. Results show that the ESV in Yunnan Province increased by 12.9 % in 2000–2019 compared to 1980–1999 without considering economic development. Land use and cover change (LUCC) is the main cause for the increase in ESV with the contribution rate of 92.3 %, while that of climate change is only 7.7 %. Major components of ESV change are ESV changes in forests, grasslands, and farmland, which are all mainly caused by LUCC with the contribute rates of over 80 %. ESVs in different cities differ greatly, where the ESV and ESV per unit area in Pu'er City are 5.3 and 2.6 times as those in Zhaotong City, respectively. LUCC is also the main cause of ESV changes in all cities, with contributions rates generally exceeding 80 %. When considering economic development, the ESV in Yunnan has increased by 336.2 % in these two periods. Contribution rate of economic development to ESV increase in Yunnan is 80.3 %, while that of climate change and LUCC is only 19.7 %. The speed of ecological construction in Yunnan Province is far slower than that of economic development, and appropriate measures should be taken to improve the ecological services in the future.

Effects of Land Cover Changes on Shallow Landslide Susceptibility Using SlideforMAP Software (Mt. Nerone, Italy)

Land cover changes in mountainous areas due to silvo-pastoral abandonment can affect soil stability, especially on steep slopes. In addition, the increase in rainfall intensity in recent decades requires re-assessing landslide susceptibility and vegetation management for soil protection. This study was carried out using the software SlideforMAP in the Mt. Nerone massif (central Italy) to assess (i) the effects of land cover changes on slope stability over the past 70 years (1954–2021) and (ii) the role of actual vegetation cover during intense rainfall events. The study area has undergone a significant change in vegetation cover over the years, with a reduction in mainly pastures (−80%) and croplands (−22%) land cover classes in favor of broadleaf forests (+64%). We simulated twelve scenarios, combining land cover conditions and rainfall intensities, and analyzed the landslide failure probability results. Vegetation cover significantly increased the slope stability, up to three to four times compared to the unvegetated areas (29%, 68%, and 89%, respectively, in the no cover, 1954, and 2021 scenarios). The current land cover provided protection against landslide susceptibility, even during extreme rainfall events, for different return periods. The 30-year return period was a critical condition for a significant stability reduction. In addition, forest species provide different mitigation effects due to their root system features. The results showed that species with deep root systems, such as oaks, provide more effective slope stability than other species, such as pines. This study helps to quantify the mitigation effects of vegetation cover and suggests that physically based probabilistic models can be used at the regional scale to detect the areas prone to failure and the triggering of rainfall-induced shallow landslides. This approach can be important in land planning and management to mitigate risks in mountainous regions.