High Land Research Articles

Identifikasi Urban Heat Island di Kawasan Tepian Selokan Mataram Yogyakarta

Title: Identification of Urban Heat Island in Yogyakarta's Selokan Mataram Riverbank Area
 This research aims to identify the Urban Heat Island (UHI) phenomenon in the Selokan Mataram riverbank area, which manifests traces of human life in the Anthropocene era. Selokan Mataram is ten kilometers long and has various regional characteristics formed spontaneously from several functions and typologies of areas that continue to develop. An area with low population and low-density built-up land, or rural, has a low surface temperature compared to an area with high population and high-density built-up land, or urban. This difference in air temperature is theoretically supposed to occur. Even research in several similar articles in other countries has been proven and has been a concern. In the context of the study object in Mataram Sewer, some results follow the theory and similarities to similar studies. Specifically, the difference in air temperature recorded in the early morning reached 2,35°C in the early morning and 4,55°C in the afternoon.

Analysis of Land Value Change in Genuk District

Genuk District is a district in Semarang City which is designated as an industrial area. The development of industrial estates can lead to increased urbanization. According to data from Central Agency on Statistics in 2020, the population in Genuk District has always increased from year to year. The increasing population causes the need for land to also increase. Land prices can also rise considering the need for land and factors from industrial development. On the other hand, Genuk District is prone to tidal flooding. The worst tidal floods in Genuk District occurred in Gebangsari, Genuk and Banjardowo Sub-District in the 2000s. Based on the problem, this research aims to map land prices temporally in 2014, 2019 and 2022 and analyze the distribution pattern of land value changes in Genuk District with the Local Moran’s I method. That method is used for analyze the pattern of land values in 2022 and the pattern of land price changes between 2019 and 2022. The method used in mapping land prices is based on a fair market land price approach and land valuation techniques using a mass valuation cost approach. The average land value zone in 2014 has a value of IDR 711,000 in 2019 has an average value of IDR 986,000 and in 2022 is IDR 1,599,000. According to the results of Local Moran’s I, the pattern land value change in 2019-2022 is different from the results of the land value pattern in 2022 for each category class. This shows that the high land value changes between 2019 to 2022 are not in the same location as the high land value in 2022.

Landslide susceptibility mapping in the Northern part of Los Glaciares National Park, Southern Patagonia, Argentina using remote sensing, GIS and frequency ratio model

The valleys in the northern part of Los Glaciares National Park, Southern Patagonia, Argentina, are highly dynamic due to glacier shrinkage. To improve our understanding of slope instability in deglaciated environments, we generated and analyzed a Landslide Susceptibility Map (LSM). The proposed methodology includes remote sensing, interpretation of geological maps, both performed in a Geographic Information System (GIS), and finally a Frequency Ratio Model (FRM). Factors included in the slope instability analysis were lithology, geomorphology, land cover/land use, slope, aspect, elevation, curvature, distance to geologic fault, and distance to roads and trails. Slope instability events identified in the inventory include moraine landslides, debris flows, and rockfalls. The area is modeled by glacial action and has numerous geographic features related to erosional and depositional processes of glaciofluvial, cryogenic, and paraglacial origin. According to their high frequency ratio values, geomorphology, land cover and land use, lithology, and elevation are the most influential predisposing factors. The three best performing classes are Quaternary deposits, ice-contact topography, and the innermost lateral moraines on the western side of the study area, which surround glaciers and proglacial lakes. The performance and accuracy of the LSM was evaluated and verified using the Area Under Curve (AUC) with an accuracy of 0.804. The study provides knowledge of the deglaciated environment and landslide-prone areas in the study area. In this way it will help to provide tools for action to prevent and manage potential hazards to infrastructure, residents and/or tourists.

Evaluation of land roughness and weather effects on paddy field using cameras mounted on drone: A comprehensive analysis from early to mid-growth stages

This paper proposes a potential solution to the traditional method of rice field preparation and management by utilizing unmanned aerial vehicles (UAVs) equipped with RGB and spectral cameras. Farmers typically rely on their experience to deal with bad weather and prepare rice fields in the beginning of the season, resulting in time-consuming and inefficient processes. The proposed method uses UAVs to evaluate the rice fields from the start to the middle stages of the season. The UAV flight follows a pre-determined trajectory autonomously, capturing successive images using the attached cameras. In the soil preparation stage, Harralick texture segmentation is used to analyze the roughness of paddy fields’ surfaces (low land, normal land and high land). In this approach, image is segmented first, then areas belonging the same clusters are extracted, manually verified and labelled. These labelled images are used as training data for the proposed model. The result shows that roughness of field surface can be observed clearly with high accuracy at over 90%, with random forest method is at over 97%. In addition to this, the effects of rain and flooding are also considered during the reproductive stages. The results show that the proposed method achieves high accuracy in evaluating rice fields, which can significantly increase efficiency and productivity compared to traditional methods. The study highlights the potential of UAV-based monitoring to provide a cost-effective and efficient way to manage rice fields and improve crop yields, leading to higher food security and sustainable agriculture.

Which land cover product provides the most accurate land use land cover map of the Yellow River Basin?

Precise land use land cover (LULC) data are essential for understanding the landscape structure and spatial pattern of land use/cover in the Yellow River Basin (YRB) to regulate scientific and rational territorial spatial planning and support sustainable development. However, differences in the multiple sets of LULC products in portraying the land composition of the YRB limit our understanding of the land cover composition in this region. To address this issue, this study chose five sets of open and high spatiotemporal LULC data in 2020, namely, CLCD, LSV10, ESRI10, CLC_FCS30, and Globeland30, to evaluate the accuracy and consistency of classification in the YRB. Our results show that: (1) The LULC composition of the YRB in 2020 was mapped consistently by the five datasets. Grasslands, croplands, and woodlands constitute the major LULC types, accounting for 96% of the total area of the study area. (2) The correlation coefficients of the LULC types of any two of the five datasets ranged from 0.926 to 0.998, showing high land compositional consistency. However, among the five datasets, there were considerable differences in the areas of a single LULC type. (3) The classification consistencies of croplands, woodlands, grasslands, and water bodies were higher than 60% in any two datasets. The spatial consistencies of grasslands, croplands, and woodlands were higher than those of other LULC types. An area with better consistency can reach more than 50% of the average area of the corresponding land types, but grasslands were mixed with other LULC types in ESRI10 and GLC_FCS30. (4) According to the accuracy assessments, LSV10 data have the highest overall classification accuracy, 79.32%, and the classification accuracy of major land types is also higher than 70%; GLC_FCS30 data have the lowest overall accuracy, 70.14%. Based on these results, LSV10 can more accurately demonstrate LULC than the other four datasets. This study can be used as a reference for selecting land cover data, and it also highlights that the necessary assessments of consistency and accuracy are essential when conducting land use/cover change studies in a specific region.

Effects of Transfer of Land Development Rights on Urban–Rural Integration: Theoretical Framework and Evidence from Chongqing, China

The transfer of land development rights (TDR) is a significant policy tool for advancing urban–rural integration. This study establishes an analytical framework to examine the influence mechanism of TDR on urban–rural integration, considering the flow of land, capital, and population factors. Furthermore, an indicator system is developed to evaluate urban–rural integration across economic, social, and population dimensions. Using panel data from Chongqing, China (2013 to 2019), this article adopts the global principal component analysis (GPCA) method and time-varying difference-in-difference (TV-DID) model to analyze the effects of the land quota trading project, known as the ‘Dipiao’ policy. The results show that TDR can effectively promote urban–rural integration, though with a four-year time lag. Heterogeneous effects of TDR on urban–rural integration are observed across different districts and counties, with the more pronounced promotion in areas characterized by low agricultural land value or high industrial land value. This study further analyzes the influence mechanism of TDR on urban–rural integration. It concludes with policy implications on improving TDR to promote urban–rural integration.

Does rural-urban migration matter in the marketization of land rentals in China?

ABSTRACT Drawing on data from the 2015 China Household Finance Survey, this paper assess the links between rural-urban migration and the marketization of land rentals. The estimated results indicate that farm households with more migrant labour are less likely to participate in land rentals, and lessees with more agricultural labour tend to use the rented land for non-grain production. Meanwhile, lessees with less migrated labour are more likely to rent land for profit and pay high land rent, even when land rentals are conducted between acquaintances. Further evidence shows that lessors with more migrated labour have a higher probability of leasing farmland for for-profit purposes and obtain high land rent. Our analysis reveals that with rural-urban migration, the marketization of land rentals between acquaintances in rural China increases if there emerge large numbers of lessees who are specialized in agricultural production.

Structural indicator synergy for mitigating extreme urban heat island effects in industrial city: Simulation and verification based on machine learning

Industrial areas emit large amounts of extremely high temperatures and aggravate the extreme urban heat island (E-UHI) effect, which has become a non-negligible environmental issue worldwide. Due to the lack of in-depth understanding of the relationship between E-UHI and surface structural synergy, few studies explored the causes and mitigation measures of E-UHI. Therefore, this study accordingly defined the E-UHI effect at a finer scale based on the thermal characteristics and human thermal perceptions, and a multi-indicator’ method based on machine learning was proposed to quantify internal causes and collaborative mitigation measures. The results revealed that the average E-UHI scale shows a quadratic growth in Wu’an. Artificial heat from production space (PS) is the main cause of E-UHI formation. The synergy between green space (GS), PS and shape of industrial land determines the development of E-UHI. Although both the decrease of PS and the increase of GS are ways to mitigate high land surface temperature, when PS is higher than 50%, the cooling effect of GS is not obvious. When GS is less than 2.5%, the adjustment of PS is invalid for improving the thermal environment. For mitigating E-UHI, a factory with high PS needs to increase its shape index. The synergy between the structural indicators is a novel model to solve the shortcoming of adjusting a single factor in mitigating complex E-UHI. The results provide theoretical guidance for mitigating E-UHIs.

An Economic Analysis of Paddy Cultivation and Constraits in Surguja District of Chhattisgarh, India

For the study, 150 farmers were selected at randomly from the Surguja district that covers the majority of Chhattisgarh's expanded area. In order to gather information about farmers from the previous year, primary data was gathered through direct farmer contact for questionnaires and interviews. Secondary data for the previous ten years was provided by many government agencies. Objective is to calculate the costs and return from the paddy crop in the study area. According to estimates, the total cost of paddy cultivation per hectare was Rs. 43584.81; this cost ranged from Rs. 41502.65 at marginal farms to Rs. 44466.62 at large farms. The cost-per-hectare for the sampled farms have been estimated to be Rs. 23430.04, Rs. 23430.04, Rs. 40829.91, Rs. 26584.81, Rs. 43584.80, and Rs. 47943.28 for Cost-A1, Cost-A2, Cost-B1, Cost-B2, Cost-C1, Cost-C2, and Cost-C3 overall. On an overall basis, the net income over Cost-A1, Cost-A2, Cost-B1, Cost-B2, Cost-C1, Cost-2, and Cost-C3 were calculated to be Rs. 95631.96, Rs. 95232.09, Rs. 78232.09, Rs. 92477.20, Rs. 75477.20, and Rs. 71118.72, respectively. The overall gross return was Rs. 141373.30 per hectare, the overall net income was Rs. 97788.49 per hectare, and the overall input-output ratio was 1:2.24 calculated in the study area. Because of the high land value, labor costs, high hybrid seed costs, chemical fertilizer, and other factors, rice cultivation in this region is expensive. Thus, it's critical to maintain the ecology by changing to an improved local variety of paddy and applying FYM and wormi compost in place of chemical fertilizers. Labour costs can be reduced through the use of machinery and technology.

High land surface temperatures (LSTs) disproportionately affect vulnerable socioeconomic groups in Barranquilla, Colombia

Temperatures in urban areas usually exhibit distinct spatial patterns and disproportionately affect lower-income communities as they are frequently located in warmer neighborhoods. Our study focuses on the urban area of the metropolitan area of Barranquilla on the Caribbean Coast of Colombia. We examined heat disparities at the block level and used, 2018 census data to test whether population groups with high socioeconomic vulnerability were also exposed to higher land surface temperatures (LST). Specifically, we defined populations with high and low socioeconomic vulnerability based on their socioeconomic level, educational attainment, age, population density and percentage of migrant population. We tested whether those groups had differences in LST and other temperature-related variables, including NDVI (normalized difference vegetation index), NDBI (normalized difference built-up index) and distance to water. Finally, we generated a heat risk map based on socioeconomic vulnerability and heat exposure. Our results showed that high and low-vulnerability groups had significant differences in LST and distance to water compared to marginal differences in NDVI and NDBI. Spatially, due to historical urban development patterns, areas at higher heat risk were found to the south of the study region, where there is a combination of low socioeconomic level, higher population density and high LST.

Vegetation dynamics over 18 years in response to seasonal climatic conditions and land use in the Kamiesberg mountains of the succulent Karoo, South Africa

Projections of vegetation change in response to future global change conditions are challenged by the lack of long-term, empirical data on how vegetation responds to the variability in climatic conditions. This is particularly true for biodiverse systems that are threatened by climate change such as the winter-rainfall Succulent Karoo biome of southern Africa. In this study we analyse long-term data (2002–2019) of two biodiversity observatories from the Kamiesberg Mountains in the Succulent Karoo. We employed the R package GLMMtmb to analyse counts of abundance and estimated cover values of vegetation as response variables and seasonal Standardised Precipitation-Evapotranspiration Index (SPEI), annual livestock density, topography and soil surface structure as predictors for the interannual change of life forms under low and high land use intensity. We compared the life form composition at the two contrasting land use types and found the cover and abundance of leaf succulents to be higher under low grazing intensity and that of stem succulents to be higher under high grazing intensity. The cover and abundance of herbaceous life forms varied over the years depending on the seasonal weather conditions. Our GLMM results reveal counts of abundance to be a sensitive predictor for population dynamics in various herbaceous and shrub life form types. Estimated cover values showed less of a response to interannual differences in the environmental drivers. All life forms responded strongly to high humid and cool conditions in the second half of the winter rainfall period, when the temperatures increase again. The abundance of leaf-succulent and non-succulent shrubs also responded positively to moderate conditions towards the end of the dry season. The extensive and hot dry summer season in turn had a significant negative effect on the survival of particularly young plants with low cover. Cover values showed no response to the conditions of the late dry season and were generally less sensitive to the variance in seasonal climatic conditions. Our results suggest that future changes in interannual patterns in climatic conditions will affect life form composition of the vegetation.

Design of online combinatorial auction mechanism for urban land transfer.

With the wide application of the "Tender, Auction, and Listing" system for land in China, it is of paramount importance to establish a sound land auction mechanism so as to avoid unreasonable land allocation. However, existing studies only focus on developers who are merely concerned about land combinations and offline auctions with low efficiency and high land unsold rate. To further improve land auction mechanism, we modeled the problem of land allocation as an online combinatorial auction. On this basis, we defined a land correction coefficient and designed an optimal online combinatorial auction mechanism that enabled developers to submit multiple combinations of land. Our designed mechanism proved to satisfy both incentive compatibility (IC) and individual rationality (IR), which can prevent developers from lying in the auction while winning higher revenue, reducing land unsold rate, increasing developer turnover rate and promoting the stability of land resource allocation rate. Therefore, online combinatorial auction mechanism, both applicable and efficient, is a practical solution for improving land auctions in China.

Green Manuring and Nitrogen Management for Sustainable Productivity of Niger [Guizotia abyssinica (L.f.) Cass.] in Odisha, India

Niger (Guizotia abyssinica (L.f.) Cass), a minor oil seed crop is generally cultivated in marginal and sub marginal lands under poor agronomic practices without application of manures and fertilizers which causes great losses in its yield. However, the crop has high yield potential if recommended agronomic package of practices are followed. Also, the farmers always grow niger without application of any organic or inorganic fertilizers. In view of this, a field experiment was carried outunder All India Coordinated Research Project on Niger at Regional Research and Technology Transfer Station (OUAT), Semiliguda of Koraput district under Eastern Ghat High Land zone of Odisha in acidic soil during two consecutive kharif seasons of 2019 to 2020 to study the response of green manuring and nitrogen management on growth and yield of niger. The experiment consisted of four crop sequences and four nitrogen levels with split plot design. The data revealed that growing of niger crop after incorporation of cowpea as green manure along with application of recommended dose of fertilizer resulted in significantly highest seed yield of 645.6 kg/ha and 684.1 kg/ha with a net monetary return of Rs.17939/ha and Rs.30853/ha and benefit cost ratio of 1.7 and 2.9 during the year 2019 and 2020 respectively.

Insights into heat islands at the regional scale using a data-driven approach

Urban heat island (UHI) phenomenon is crucial in the context of climate change. However, while substantial attention has been given to studying UHIs within cities, our understanding at the regional level still needs to be improved. This study delves into the intricate dynamics of the regional heat island (RHI) by examining its relationship with land use/land cover (LULC), vegetation, and elevation. The objective is to enhance our knowledge of RHI to inform effective mitigation strategies. The research employs a data-driven approach, leveraging satellite data and spatial modeling, examining surface and canopy-layer regional heat islands, and considering daytime and nighttime variations. To assess the impact of LULC, the study evaluates three main categories: anthropized (urbanized), agricultural, and wooded/semi-natural environments. Furthermore, it delves into the influence of vegetation on RHI and incorporates elevation data to understand its role in RHI intensity. The findings reveal meaningful variations in heat islands across different LULCs, providing essential insights. Although urbanized areas exhibit the highest RHI intensity, agricultural regions contribute notably to RHI due to land use changes and reduced vegetation cover. This emphasizes the significant impact of human activities. In contrast, wooded and semi-natural environments demonstrate potential for mitigating RHI, owing to their dense vegetation and shading effects. Elevation, while generally associated with reduced heat island, shows variations based on local conditions. Ultimately, this research underscores the complexity of the RHI phenomenon and the importance of considering factors such as different temperatures and their daily variation, landscape heterogeneity, and elevation. Additionally, the study emphasizes the significance of sustainable spatial planning and land management. Targeted efforts to increase vegetation in high daytime land surface temperature areas can reduce heat storage and mitigate RHI. Similarly, planning for agroforestry and green infrastructure in agricultural areas can significantly increase resilience to climate.

COVID-19 and Public Transport in Auckland, New Zealand: Investigating Vulnerable Population Groups’ Ridership Behavior

Public transit ridership was severely affected during the COVID-19 pandemic in 2020 and the effects have continued since. The present study examines changes to ridership immediately post-pandemic in 2021. Research investigating the effects of COVID-19 on disadvantaged population groups is limited and the present study addresses this knowledge gap. Ridership of socially-disadvantaged groups such as low-income, female, and ethnic minority people is examined using order logit regression models. The study uses data from an online travel survey conducted in Auckland, New Zealand, immediately after all COVID-19-related restrictions were lifted. This allowed the collection of revealed preference data for the post-pandemic period. The regression models included the effects of socio-demographic characteristics of individual riders, travel attributes, and built environment factors. Findings suggest that those with lower income and from an ethnic minority group are likely to continue using transit frequently post-pandemic. Younger riders from the ethnic minority group are less likely to use transit frequently, while pre-COVID-19 they were more likely. Access to transit stops near home and work are significant factors for the ethnic minority group. Higher land use mix near the residence and work locations are found to induce more transit trips for all. It is critical for transit agencies to understand how the usage has evolved post-pandemic. These findings highlight the importance of considering the effects of the pandemic on different disadvantaged groups. Public transport service providers are encouraged to consider equity as they develop strategies to improve transit ridership.

Optical and electrical performance of an agrivoltaic field with spectral beam splitting

Solar photovoltaic power generation is a mature and competitive technology, but its high land area requirements conflict with other uses of available land. Agrivoltaics (APV) allows dual use of land, with photovoltaic panels installed over agricultural crops. However, the panels block a significant portion of the solar radiation, leading in many cases to a severe reduction in growth and productivity of the agricultural crop. Here, we present a novel type of collector to resolve this competition between electricity and crop yields by spectral splitting of the incident direct radiation using simple planar components. Photosynthetically active radiation (PAR) is transmitted to the crop while near-infrared radiation (NIR) is directed to the PV panels. The annual optical and electrical performance of a large-scale agrivoltaic field with these spectrum-splitting collectors is investigated in detail. The study is conducted using a computational model that includes optics, heat transfer, and electrical conversion. The reliability of the model was validated using experimental data obtained from a small-scale experimental setup. Results show that the electricity production per unit land area of the spectrum-splitting collectors can be similar to that of conventional APV fields and may reach even higher value by about 20% with optimized spectral splitters. According to the simulations, the loss of PAR on the ground due to the collectors may be significantly reduced, reaching only 10% compared to 21% in a conventional APV field.

Effects of vegetation distribution and landscape pattern on water conservation in the Dongjiang River basin

Water conservation is important for maintaining a well-functioning ecosystem. Using the Soil & Water Assessment Tool (SWAT) model, we simulated the hydrological cycle and evaluated the spatial and temporal variability of water conservation in the Dongjiang River. Spatial autocorrelation analysis and geographically weighted regression (GWR) were used to investigate the effects of landscape patterns and normalized difference vegetation index (NDVI) on water conservation. Four hydrological stations with regular rate, validation period deterministic coefficient R2, and efficiency coefficient ENS (Nash–Sutcliffe) met the adaptation requirements, and the model accurately simulated the hydrological process of the Dongjiang River. The interannual variation of water conservation showed a non-significant upward trend (0.12 mm/10a), and the spatial distribution is given as follows: midstream > downstream > upstream. Regression coefficients of NDVI of high, medium, and low-density forest land in the sub-basin with water conservation decreased from the upper to lower reaches. The negative correlation of LPI, CONTAG, and IJI was concentrated in the upper reaches and southeast of the basin, and that of SHDI was concentrated in the middle and lower reaches; the regression coefficients of LPI and PLAND of forest land decreased from middle reaches to north and south, and ED decreased from southwest to northeast. The positive correlation of grassland was concentrated in the watershed’s upper reaches; the regression coefficient of construction land showed an increasingly negative correlation from the lower to upper reaches. This study provides scientific support for promoting the hydrological ecosystem and comprehensive utilization of water resources in the Dongjiang River basin.

Identifying spatial heterogeneity of COVID-19 transmission clusters and their built-environment features at the neighbourhood scale

The identification of high-risk areas for infectious disease transmission and its built-environment features are crucial for targeted surveillance and early prevention efforts. While previous research has explored the association between infectious disease incidence and urban built environment, the investigation of spatial heterogeneity of built-environment features in high-risk areas has been insufficient. This paper aims to address this gap by analysing the spatial heterogeneity of COVID-19 clusters in Shanghai at the neighbourhood scale and examining associated built-environment features. Using a spatiotemporal clustering algorithm, the study analysed 1395 reported cases in Shanghai from March 6 to March 17, 2022. Both global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models were applied to examine the association between built-environment variables and the size of COVID-19 clusters. Our findings suggest that larger COVID-19 clusters emerging in the suburbs compared with the downtown and multiple built-environment features are significantly associated with this pattern. Specifically, neighbourhoods with a higher proportion of commercial, public service and industrial land, higher centrality of metro stations, and proximity to hospitals are positively associated with larger COVID-19 clusters, while neighbourhoods with higher land use mix and green/open spaces density are associated with smaller COVID-19 clusters. Moreover, we identified that metro stations with high centrality present the highest risk in the downtown, while commercial and public service places exhibit the highest risk in the suburbs. By highlighting the overlooked spatial heterogeneity of built-environment features for high-risk areas, this study aims to provide valuable guidance for public health departments in implementing place-based interventions to effectively prevent the spread of potential epidemics.

Local climate variability associated to sustainable agriculture at rain shadow areas of mount Rinjani and Tambora in West Nusa Tanggara

Climate is driven by solar energy received on the earth surface and local factor, such as mountains. The solar energy affects climate variables namely, temperature, atmospheric pressure, relative humidity, wind and rain. In addition, mountains perform rain shadow areas and affect variability of local climate. A descriptive research was conducted to study variability of those variables associated to direct normal solar irradiation (DNI), and its possible impact on sustainable agriculture at rain shadow areas of Mt. Rinjani in Lombok and Tambora in Sumbawa, West Nusa Tenggara. The results show that the DNI was relative similar at both sides of the Mt. Rinjani which was 13.15±2.6 MJ m−2day−1 on average. While at Mt. Tambora, the DNI at rain shadow area (southern part) was higher than northern part which were 15.6±2.07 MJ.m−2.day−1 and 13.6±3.2 MJ m−2.day−1 respectively. The DNI was relatively high in low lands and it successively decreased through medium and high lands. ETo, air and soil surface temperature were relatively high at rain shadow areas. In conclusion, the intensity of DNI was not significantly affected by aspect factor of incoming light relative to the Mt Rinjani and Mt. Tambora. The mountains had affected the variability of local climate and performed rain shadow areas to the north from Mt Rinjani and to the south from Mt. Tambora, where there had established typical dry land agriculture of semi arid tropics.

Changes in land use impacted air temperature in Bekasi Regency

Indonesia is experiencing dynamic land change. One area in Indonesia that has experienced high land use change is Central Cikarang District. Landsat 9 image sensing and land cover data from the Ministry ofEnvironment and Forestry show the increased temperature in the Central Cikarang District area since 2004. Based on the NDVI analysis, the residential area has increased, and the area of green vegetation hasdecreased. Analysis of temperature changes using satellite imagery also shows a wider distribution of hot temperatures in the 2004–2022 range. From the two data analyses that were carried out, a regression test was carried out to determine the correlation between air temperature and vegetation density. The results of the regression test obtained an R2 value of 7.2%. It is also known that the value of the regression coefficient is negative, which means that the correlation of the two data is inversely proportional. So this research analyzes changes in land cover to changes in temperature.