Land Cover Analysis Research Articles

Geospatial Assessment of Land Cover Dynamics in the Guinea Savannah Ecological Zone

An attempt is made in this study to examine the geospatial analysis of land cover/ land use changes between 2000- 2016 in the North Central region including the Federal Capital Territory Abuja. Multispectral satellite images of Landsat 7 ETM and 8 OLI/TIRS of 2000, 2008 and 2016 with three land-use types were identified and analysed using Geographic Information System ArcGIS 10.5 software. The land-use types are Urban representing the built-up area, Vegetation and water bodies. The area of each land use type per year was calculated and the result was used to compute land-use change and percentage change in square kilometres. The outcome of the analysis reveals that built-up area (Urban) land use increase from 10.64% in 2000 to 22.44% in 2008 and later 29.01% in 2016 while Vegetation reduced from 84.99% in 2000 to 73.24.% in 2008 and 62 .74% in 2016. In the same vein, water bodies which stood at 4.37% in 2000 reduced to 4.32% in 2008 before declining again in 2016 to 4.27% respectively. The results have therefore highlighted the nexus between climate change and resource depletion and further provided valuable information for a robust assessment of the changes in vegetal resources of the study area over time. The study recommends sustainable forest resource management, vegetation monitoring and promulgation of vegetation control laws to improve the vegetation cover of the study area.

Spatiotemporal analysis of land use land cover and future simulation for agricultural sustainability in a sub-tropical region of India

Spatiotemporal analysis of land use land cover and future simulation for agricultural sustainability in a sub-tropical region of India

Changes in Land cover using the NDVI (Normalized Difference Vegetation Index) Method in Kedamaian Subdistrict, Bandar Lampung City as Urban City

Land cover change occurs naturally, but in urban areas, Land cover change usually occurs more rapidly than in rural areas. Therefore, it is necessary to analyse Land cover changes using spatial data to determine Land cover changes that occurred in urban areas such as Bandar Lampung City in 2000 and 2020. This study uses Landsat 7 and 8 imagery processed by the NVDI (Normalized Difference Vegetation Index) analysis method with an arithmetic formula to determine the vegetation index NDVI = (NIR-RED) / (NIR + RED). After obtaining the land cover map from the results of the NDVI analysis, a ground check was carried out at 30 location points to determine the actual condition of the field and calibrate the data from NDVI analysis and actual condition. In this study, it was found that the results of Land cover from 2000 to 2020 experienced changes, namely in open land/settlements increased by 74% or 217 ha, shrub land decreased by 27% or 32 ha, low vegetation land decreased by 4.8% or 7 ha, and vegetation land is decreasing by 72.5% or 100 ha. Based on the results of ground checks, changes in Land cover are caused by the construction of settlements, housing, and industrial estates due to an increase in population and urban growth.

Variation in land use cover and surface temperature of Kubwa, Federal Capital Territory, Nigeria

Increasing human activity within the urban area brings massive changes in Land/Land Cover patterns and has an obvious effect of changes to urban ecosystems that make urban areas fragile. These conversions result in the appearance of numerous micro and mesoscale climates, warmer than the original climate and that of surrounding areas. This paper analyzed urban surface temperature variation on vegetal cover loss in Kubwa, FCT, Abuja. The study employed the used Landsat imageries of (1990, 2000, 2010 and 2020) to analyse land cover changes and land surface temperature. The study revealed that built-up area increase from 1990-2000 with 166% and 80% in 2010 and the year 2020 recorded an average of 23%. Bare surface decreased by -25% between (1990 -2000); further decreased from 3028.5 to 1979.6 hectares and -14% between 2010-2020. The study established that the LST value in 1990 Kubwa was 15℃ and 32.4℃ with a mean value of 23.7℃. The minimum and maximum LST of Kubwa in 2000 was 25℃ and 35℃ with mean of 30℃. The trend further continues, in the year 2010, minimum and maximum LST of 25℃ and 35℃ with a mean of 30℃ and in 2020 with a minimum and maximum LST of 25℃ and 40℃ with mean of 32.5℃. The analysis revealed that temperature variation increases between 1990, 2000, and 2010 were 0.4°C. There was 2°C in LST between the 2010 and 2020 due to urban expansion, infrastructure development, population growth and anthropogenic activities. The study recommends improvements of urban green infrastructure; and the FCT administration should embark into afforestation in order to restore the depleted vegetal land cover of the study area.

Modeling the effects of future climate and land-use changes on streamflow in a headwater basin in the Brazilian Caatinga biome

Projected climate changes integrated into future land-use changes are extremely complex and hard to estimate, especially for basins located in semiarid environments. This work aims to estimate streamflow and the future water yield using climate prediction and land-use changes in the Salgado River basin for the 2030 − 2060 period. In this study, the applicability of the Soil and Water Assessment Tool (SWAT) and Land Change Modeler models was verified to estimate flows and land use and cover for the studied basin. Calibration and validation of the SWAT model are used on a monthly basis for 1986 − 2017, with a calibration period from 1986 to 2006 and a validation period from 2007 − 2017. Land use and cover analysis is performed for the 1985 − 2000 period, and future land use and cover is predicted using the land change model for 2050. In addition, future hydrological modeling of the geographical distribution of the water yield is performed for each subbasin of the Salgado River basin from the 2030 to 2060 period. The results of land use and cover changes show that the pasture class was the one that showed the most significant increase in the basin due to recent changes in land use and cover. The results showed that the HadGEM2-ES model presented a minor difference in the representative concentration pathways (RCP) 2.6, which indicates that this model is the most suitable for rainfall-runoff simulations in the region.

Application of normalized difference vegetation index in classifying land cover change over Bangli regency by using Landsat 8 imagery

The information on land cover changes is very important in regional spatial planning. Remote sensing technology can minimize the cost and time in analyzing land cover changes. Normalized Difference Vegetation Index (NDVI) is a vegetation index that combines red and near-infrared channels so that it can provide approximate information about land cover in an area. The objective of this study is to extract land cover change information from Landsat 8 images based on NDVI values in Bangli Regency in 2015 and 2021. The classification method used to estimate the type of land cover is supervised classification. The results reveal the decrease of the land cover in the category of water body, sand, dry land/soil, rice fields, and vegetation, which are 1.62%, 14.14%, 7.93%, 8.63%, and 2.45%, respectively, while an increase in the settlement category by 30.12%. The overall accuracy of land cover classification result based on NDVI value is 86.54%.

Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil

The simulation and analysis of future land use and land cover—LULC scenarios using artificial neural networks (ANN)—has been applied in the last 25 years, producing information for environmental and territorial policy making and implementation. LULC changes have impacts on many levels, e.g., climate change, biodiversity and ecosystem services, soil quality, which, in turn, have implications for the landscape. Therefore, it is fundamental that planning is informed by scientific evidence. The objective of this work was to develop a geographic model to identify the main patterns of LULC transitions between the years 2000 and 2018, to simulate a baseline scenario for the year 2036, and to assess the effectiveness of the Chapecó River ecological corridor (an area created by State Decree No. 2.957/2010), regarding the recovery and conservation of forest remnants and natural fields. The results indicate that the forest remnants have tended to recover their area, systematically replacing silviculture areas. However, natural fields (grassland) are expected to disappear in the near future if proper measures are not taken to protect this ecosystem. If the current agricultural advance pattern is maintained, only 0.5% of natural fields will remain in the ecological corridor by 2036. This LULC trend exposes the low effectiveness of the ecological corridor (EC) in protecting and restoring this vital ecosystem.

Analysis of land cover and SUHII pattern using local climate zone framework—A case study of Chang-Zhu-Tan main urban area

The study examines the changes in the local climate zone (LCZ) and the surface urban heat island intensity (SUHII) of Chang-zhu-tan main urban area from 2000 to 2017. Firstly, it analyzed the land cover changes in Chang-zhu-tan from 2000 to 2017 by exploring the distribution of LCZ types. Secondly, mean LST of LCZs were obtained to evaluate the SUHII layout. Thirdly, it deals with critical LCZs in SUHII and seeks to understand how to approach mitigation strategies for Chang-zhu-tan. The results showed that, firstly, the land cover LCZs of the suburban areas adjacent to the three cities were most likely to be replaced with built-up LCZs from 2000 to 2017. Secondly, SUHII varies among different LCZs. LCZ-10 contributed to the strongest SUHII (around 5–6 °C) in 2000 and 2005, while LCZ-5 had the highest SUHII (around 6–7 °C) from 2011 to 2017, which tended to occur around the urban centers. By contrast, LCZ-A-D and LCZ-G contribute to cool areas (around 0–2 °C) throughout the study period.The findings help better understand the relationship between SUHII and LCZs and provide a more scientific basis for urban environmental planning.

MLNet: multichannel feature fusion lozenge network for land segmentation

The use of remote sensing images for land cover analysis has broad prospects. At present, the resolution of aerial remote sensing images is getting higher and higher, and the span of time and space is getting larger and larger, therefore segmenting target objects enconter great difficulties. Convolutional neural networks are widely used in many image semantic segmentation tasks, but existing models often use simple accumulation of various convolutional layers or the direct stacking of interfeature reuse of up- and downsampling, the network very heavy. To improve the accuracy of land cover segmentation, we propose a multichannel feature fusion lozenge network. The multichannel feature fusion lozenge network (MLNet) is a three-sided network composed of three branches: one branch uses different levels of feature indexes to sample to maintain the integrity of high-frequency information; one branch focuses on contextual information and strengthens the compatibility of information within and between classes; and the last branch uses feature integration to filter redundant information based on multiresolution segmentation to extract key features. Compared with FCN, UNet, PSP, and other serial single road computing models, the MLNet, which performs feature fusion after three-way parallelism structure, can significantly improve the accuracy with only small increase in complexity. Experimental results show that the average accuracy of 85.30% is obtained on the land cover data set, which is much higher than that of 82.98% of FCN, 81.87% of UNet, 77.52% of SegNet, and 83.09% of EspNet, which proves the effectiveness of the model.

Climate change impact on fungi in the atmospheric microbiome

The atmospheric microbiome is one of the least studied microbiomes of our planet. One of the most abundant, diverse and impactful parts of this microbiome is arguably fungal spores. They can be very potent outdoor aeroallergens and pathogens, causing an enormous socio-economic burden on health services and annual damages to crops costing billions of Euros. We find through hypothesis testing that an expected warmer and drier climate has a dramatic impact on the atmospheric microbiome, conceivably through alteration of the hydrological cycle impacting agricultural systems, with significant differences in leaf wetness between years (p-value <0.05). The data were measured via high-throughput sequencing analysis using the DNA barcode marker, ITS2. This was complemented by remote sensing analysis of land cover and dry matter productivity based on the Sentinel satellites, on-site detection of atmospheric and vegetation variables, GIS analysis, harvesting analysis and footprint modelling on trajectory clusters using the atmospheric transport model HYSPLIT. We find the seasonal spore composition varies between rural and urban zones reflecting both human activities (e.g. harvest), type and status of the vegetation and the prevailing climate rather than mesoscale atmospheric transport. We find that crop harvesting governs the composition of the atmospheric microbiome through a clear distinction between harvest and post-harvest beta-diversity by PERMANOVA on Bray-Curtis dissimilarity (p-value <0.05). Land cover impacted significantly by two-way ANOVA (p-value <0.05), while there was minimal impact from air mass transport over the 3 years. The hypothesis suggests that the fungal spore composition will change dramatically due to climate change, an until now unforeseen effect affecting both food security, human health and the atmospheric hydrological cycle. Consequently the management of crop diseases and impact on human health through aeroallergen exposure need to consider the timing of crop treatments and land management, including post harvest, to minimize exposure of aeroallergens and pathogens.

Analysis of land cover changes due to forest fires in Gunung Leuser National Park, North Sumatra Province, Indonesia

Abstract. Thoha AS, Saraswita N, Sulistiyono N, Wiranata D, Sirait SM, Inaldi R. 2022. Analysis of land cover changes due to forest fires in Gunung Leuser National Park, North Sumatra Province, Indonesia. Biodiversitas 23: 1420-1426. Forest and land fires occur almost every year with significant impacts and losses on the environment and various sectors including conservation areas. Gunung Leuser National Park (GLNP) is a conservation area in danger of losing its forest cover due to slash-and-burn land clearing. This study aimed to analyze land cover changes due to forest and land fires in GLNP of North Sumatera Province. Data of hotspots, satellite images, land cover maps, and field verification were used to see the transition of land cover changes before and after forest and land fires. Temporal and spatial analyses were carried out to see the trend of these changes. Field surveys were also conducted to verify hotspots as locations for forest fires, burned land, and human activities causing the fires. This study found that 2010 had the highest hotspots reported from 2001 to 2019. There were indications of a fairly massive forest fire in 2010, as evidenced by the identification of burned land on satellite images. The burned land was near the outer boundary of the GLNP area and expanded into the area in the following period. Before 2010, the area of the non-forest cover was not too large. After 2010, there was an increase in non-forest land cover, presumably due to increased access to enter the area after the forest was cleared after the fire. During almost 20 years, the primary dryland forest in the study area remained only 6% of the initial area. At least more than 200 ha of forest area is reduced in the study area every year. The burned lands in the following years tended to become plantation land. Plantation land cover increased by around 90% during 2000-2019 in the research sites. This study expects that GLNP management and various parties need to prevent the expansion of forest clearing because the strategic role of GLNP as a world heritage is currently in danger.

Spatial based mapping of londerang peatprotected revegetation

The Londerang Peat Protected Forest is the largest protected peat forest in Jambi Province with 12, 500 hectares. In 2015 there was a peat forest fire that destroyed the existing vegetation. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, thousands of hotspots were observed from May to October. The condition of peat damage reaches 90%. Peat plays an important role in absorbing 75%of carbon so it should not be burned, dried, and used as plantation land. The purpose of the study was to determine changes in land cover, to determine the extent of revegetation, and to determine the area of unvegetated forest in the Londerang Peat Protected Forest area. The remote sensing method used is Image Classification assisted by ArcGIS Version 10.8 software with Landsat 8 satellite imagery. The analysis is carried out by making 20 color classifications on satellite image data, then the majority or pulling the minority pixel classes into the majority of pixel colors. From each code grid in the attribute table, the data are combined according to the landcover classification.The results of the spatial analysis of land cover In 2013-2018 there was very high forest degradation. In 2013 it was 2940.1 ha, in 2018 it was 191.3ha from the initial area of 12500 ha so that the Gonderang HLG was categorized as critical. Revegeted land is 354 ha with 164, 000 planted vegetation and the type of planted vegetation is endemic vegetation (Jelutung, swamp island, Gelam). The revegetation of the Londerang Peat Protection Forest has only been carried out for only 2.8% of the total land area.

Commodity crops in biodiversity-rich production landscapes: Friends or foes? The example of cotton in the Mid Zambezi Valley, Zimbabwe

The production and trade of agricultural commodities is a major driver of the loss of tropical biodiversity. In the Mid Zambezi Valley, Zimbabwe, which is home to many emblematic African mammals, cotton production has historically been a major driver of land cover change. The collapse of cotton production in Zimbabwe over the last decade provides a unique opportunity to understand the linkages between the profitability of cotton and land-use changes in this multifunctional landscape. By re-visiting 141 households that had been surveyed in 2007 and combining this panel survey data with a land cover analysis and secondary data, we demonstrate that the decreasing profitability of cotton led to a shift from cotton farming (mean area per farm decreasing from 1.79 ± 2.05 ha in 2007 to 0.72 ± 0.90 ha in 2020) to livestock farming (mean number of cattle and goats per farm increasing several-fold between 2007 and 2020), resulting in drastic land cover changes. Indeed, open vegetation (including crops, fallows and grazing areas) expanded from 10 to 20% of the total land cover area between 2007 and 2020. Populations of wildlife species have declined drastically during this period, although this cannot be attributed solely to the observed changes in land cover. However, increasing human-wildlife conflicts are likely to threaten the long-term coexistence of people and wildlife in the area. We argue that commodity crops can be an opportunity for nature conservation, not only a threat, and that conservation needs to support a ‘living income’ for people coexisting with wildlife.

Physicochemical and Bacteriological Analysis of Groundwater Quality in Lower Kubanni River Basin Zaria, Nigeria

Groundwater is the most common source of drinking water in most developing countries including Nigeria, the diverse nature of human activities within lower Kubanni may result in a negative impact on most water resources including groundwater. This research was conducted to examine the effect of different activities/land uses within lower Kubanni and relate them with groundwater quality from the borehole and shallow wells. A purposive sampling technique was adopted for the collection of water samples. The samples were obtained from the closest sources of groundwater to the lower course of Kubanni River. A total of eight water samples were used for this study, four from boreholes and four from shallow wells. Seventeen physicochemical and bacteriological parameters were analyzed which include: odour, taste, colour, Total Dissolved Solids (TDS), pH, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), hardness, nitrate, phosphate, chloride, calcium (Ca), Mg, Fe, Pb, Cr and total coliform. Land use Land Cover (LULC) analysis was carried out using the GIS technique to classify the different activities taking place within the study area. Findings from this study revealed that groundwater from both borehole and shallow wells across the study area had all the physical parameters alongside total coliform within the WHO and NSDWQ threshold. However, some physicochemical parameters such as TDS (470-1650 mg/L), hardness (434-1050 mg/L), Ca (231-405 mg/L), Fe (0.8 mg/L), Pb (0.6-1.02 mg/L) and Cr (0.2-0.6 mg/L) were above the WHO and NSDWQ limits. The LULC analysis shows the built-up area covers the largest portion of 42% followed by cultivation with 36%, bare land makes up 20% while water body covered only 2%. The study recommends that groundwater within lower Kubanni should not be taken without proper treatment as any of the samples fully complied with the WHO/NSDWQ limits.

An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques

Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, the Resourcesat-2 LISS-IV image was used, and the entire research region was classified into five LULC groups. The performance of the two approaches was examined by comparing the classification results. For accuracy evaluation, the ground truth data was used, and confusion matrixes were generated. The overall accuracy of the object-based methodology was 84.66%, which was significantly greater than the overall accuracy of the pixel-based categorization methodology, which was 72.66%. The findings of this study show that object-based classification is more appropriate for high-resolution Resourcesat-2 satellite data than MLC of pixel-based classification.

IDENTIFIKASI PERUBAHAN TUTUPAN LAHAN DI KEBUN RAYA MEGAWATI SOEKARNOPUTRI MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS

This study aims to identify land cover and analyze land cover changes in the Megawati Soekarnoputri Botanical Gardens area in 2010-2019. This research was conducted in December 2021-January 2022 at the Megawati Soekarnoputri Botanical Gardens, Southeast Minahasa and at the Forestry Science Study Program, Faculty of Agriculture, Sam Ratulangi University, Manado. This study uses satellite image/ Digital Globe Imagery with visual interpretation methods or on-screen digitization. The results showed that there were 6 land cover classes: forest, developed, open land, agriculture, shrub, and water bodies. The land cover area resulting from the interpretation of satellite image in 2010 is 189,51 Ha of forest, 0,34 Ha of developed, 6,78 Ha of open land, 9,71 Ha of agriculture, 8,96 Ha of shrub and water bodies of 6,11 Ha. The land cover area from the interpretation of satellite photos in 2019 is 185,79 Ha of forest, 0,57 Ha of developed, 6,02 Ha of open land, 12,40 Ha of agriculture, 8,71 Ha of shrub and water body 4,62 Ha. The results of the analysis of land cover changes showed that forested land cover from 2010 to 2019 in Megawati Soekarnoputri Botanical Gardens decreased by (0.22%), namely 0,22 Ha, so that forested land cover in 2019 had an area of 189,10 Ha and non-forest land cover 32,31 Ha.

IDENTIFIKASI PERUBAHAN TUTUPAN LAHAN DI KEBUN RAYA MEGAWATI SOEKARNOPUTRI MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS

This study aims to identify land cover and analyze land cover changes in the Megawati Soekarnoputri Botanical Gardens area in 2010-2019. This research was conducted in December 2021-January 2022 at the Megawati Soekarnoputri Botanical Gardens, Southeast Minahasa and at the Forestry Science Study Program, Faculty of Agriculture, Sam Ratulangi University, Manado. This study uses satellite image/ Digital Globe Imagery with visual interpretation methods or on-screen digitization. The results showed that there were 6 land cover classes: forest, developed, open land, agriculture, shrub, and water bodies. The land cover area resulting from the interpretation of satellite image in 2010 is 189,51 Ha of forest, 0,34 Ha of developed, 6,78 Ha of open land, 9,71 Ha of agriculture, 8,96 Ha of shrub and water bodies of 6,11 Ha. The land cover area from the interpretation of satellite photos in 2019 is 185,79 Ha of forest, 0,57 Ha of developed, 6,02 Ha of open land, 12,40 Ha of agriculture, 8,71 Ha of shrub and water body 4,62 Ha. The results of the analysis of land cover changes showed that forested land cover from 2010 to 2019 in Megawati Soekarnoputri Botanical Gardens decreased by (0.22%), namely 0,22 Ha, so that forested land cover in 2019 had an area of 189,10 Ha and non-forest land cover 32,31 Ha.

Spatial - Temporal variability of Land Use Land Cover at Mount Merapi, Indonesia

One of the most active volcanoes in the island of Java is Merapi mount which was experienced the last major eruption peak on October 26th, 2010. This volcanic eruption was effusive eruption type where magmatic gas pressure in the crater was not too strong and magma eruption was just flown out past the slopes of the Merapi mount area. However, magmatic gas pressure and magma volume still result in deformation changes that have a direct impact on residential areas throughout the Merapi mount area. Residential areas were obtained through supervised classification process from Landsat 7 and 8-satellite imagery in the 2009, 2011 and 2019 acquisition year. The reason of observation year selection was based on pre and post eruption concept to get pattern of Merapi’s mountain body change through deformation analysis. The work focuses on spatial-temporal variability of land use land cover analysis at Mount Merapi pre and post 2010 eruption event considered here. The technique is based on NDVI (Normalized Difference Vegetation Index), Maximum Likelihood Classification (MLC) and False Colour Composite methodology. Based on change in number of pixels it was analysed. Actually, some portion of land was covered with clouds and its shadows. From the results it was observed that, water body, barren and built up features were miss classified. So finally False Colour Composite (FCC) images are used to identify the misclassified classes.

THE SENSITIVITY OF EVAPOTRANSPIRATION FOR LAND USES AND TOPOGRAPHY IN AREA OF CERRADO BIOME – BRAZIL

Evapotranspiration (ET) is a crucial element in the spatiotemporal dynamics of moisture, energy, and heat, and is related to climatic, pedogeomorphological, and phytophysiognomic aspects of the landscape. Therefore, estimating ET requires dynamic and integrated temporal analysis with biophysical landscape factors. The study aimed to analyze the behavior of ET through the analysis of land use and land cover and topographic in time series. The Simple Algorithm for Evapotranspiration Recovery (SAFER) model was used to obtain the variable ET in the periods of 01/21, 02/22, 05/13 and 06/30/2019 (variables). A database of explanatory covariates was constructed, including land use and land cover, satellite image data (Landsat-8) and digital elevation model (SRTM). The values of variables and covariates were extracted into a grid of points and separated for three altimetric conditions, and the linear Gaussian Models (GLM) were applied to the point data. The most explanatory spectral covariates for the ET variation were Albedo and Surface Temperature. And the covariates related to topography were Digital Elevation Model and Topographic Moisture Index. In general, the wet period presents a higher ET rate (2.06 mm d-¹). Forests generated the highest ET regardless of period (1.62 mm d-¹ to 4.03 mm d-¹). Elevated topography also influences the increase in ET in relation to the same lower altimetry classes (A2 and A3). This influence is associated with the altitude dynamics and intrinsic elements of the region, such as the marshy environment in the A3 region, where the highest ET values occurred. This work stands out from its peers for addressing the influence of landscape aspects on the knowledge of evapotranspiration variation, a vanguard theme in the scope of spatial analysis.

Farmers’ Strategies to Climate Change and Urbanization: Potential of Ecosystem-Based Adaptation in Rural Chengdu, Southwest China

Ecosystem-based adaptation (EbA) is emerging as a cost-effective approach for helping people adapt to climate and non-climate changes. Nowadays, climate change and urbanization have affected agricultural systems, but it is not clear how rural communities have responded or adapted to those changes. Here, we chose two typical villages in the Chengdu Plain, southwest China, through sociological surveys on 90 local farmers with a semi-structured questionnaire, participatory observation, geospatial analysis of land use and land cover, and a literature review, to explore the local people’s perception of changes or disturbances and their adaptation strategies from the perspective of EbA. The results showed that climate change and urbanization had impacted agricultural systems dramatically in the last 40 years. In two case-study sites, climate change and urbanization were perceived by most local farmers as the main drivers impacting on agricultural production, but various resource-use models containing abundant traditional knowledge or practices as well as modern tools, such as information communication technology (ICT), were applied to adapt to these changes. Moreover, culture service through the adaptive decoration of rural landscapes is becoming a new perspective for implementing an EbA strategy. Finally, our findings highlighted the potential value of an EbA strategy for sustaining urban-rural integrated development and enhancing the resilience of agricultural systems.