Land Mapping Research Articles

Evaluation of Aggregate Stability Using the Slaking Index Method with Soil Physical Approach in Keduang Sub-Watershed, Indonesia

The Keduang Sub-Watershed area has faced multiple natural disasters like landslides, erosion, and flooding because of the poorly managed terrain in the area. This study examines the distribution of the slaking index on agricultural land in the Keduang Sub-Watershed, analyzes the impact of soil type on it, and identifies the soil physical elements that have the most significant influence on it. The study took place in the Keduang Sub-Watershed, Indonesia, utilizing agricultural land from woods, plantations, drylands, and paddy fields with Andisols, Alfisols, Inceptisols, and Entisols soil types. This survey research was supported by laboratory analysis of the soil's physical and chemical properties and used GIS for data interpretation. Soil samples were collected from 22 Land Map Units (LMUs) with 3 replications each, resulting in 66 samples. The SLAKES software assesses the primary parameter, the slaking index. The supporting parameters analyzed were aggregate stability, bulk density, texture, structure, pH, organic C, and Cation Exchange Capacity (CEC). The research showed that soil types in the Keduang Sub-Watershed significantly affect the slaking index value. The slaking index ranged from 0.13-11.63, with the highest values for Andisols in a forest, while the lowest values were Inceptisols in a plantation. The allophane mineral in Andisols was causing the high slaking index. The soil factors determining the slaking index were bulk density and exchangeable K. The lower the bulk density, the higher the slaking index. Meanwhile, the lower the exchangeable K, the lower the slaking index. The land management recommendations based on determinant factors are adding organic material and reducing soil cultivation practices.

SSA-LHCD: A Singular Spectrum Analysis-Driven Lightweight Network with 2-D Self-Attention for Hyperspectral Change Detection

As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth’s surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial–spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models.

Evaluation of land suitability for sorghum crops in Lampasio District, Tolitoli Regency

Sorghum is an alternative food ingredient that contains high levels of carbohydrates and calories so it can be a solution to food security problems. This research aims to evaluate the suitability of land for sorghum (Sorghum bicolor L.) in Tolitoli Regency, Indonesia. This research was conducted in Lampasio District, Tolitoli Regency using purposive sampling and survey methods on five land map units (SPL). The results of the research show that the area is classified as quite suitable land class (S2) with limiting factors for drainage and nutrient retention (oa, nr) with a land area of 3,652.7 ha spread over 5 SPL, namely SPL 1,2,3 and 5 while class The land (S3) is SPL 4. However, this can be overcome by adding fertilizer and adding organic material. So that it can improve the status of each land to be very suitable (S1) and quite suitable (S2).

Study on mapping method of irrigated cultivated land–taking Nebraska as an example

The distribution information of irrigated cultivated land is important to many studies, including grain yield estimation, water resources management, drought risk assessment, and so on. This study developed a feature variable, called the irrigation probability index (IPI), based on the principle that irrigation can slow down or inhibit the evolution from meteorological drought to agricultural drought. Nebraska, which has a good irrigation information database, was selected as the study area. Combining IPI with the irrigation statistical data and other selected feature variables, irrigated cultivated land in Nebraska was mapped in 2017 using both the spatialization method and remote sensing classification methods (random forest and support vector machine). We verified the effectiveness of IPI in identifying irrigated cultivated land, analyzed the importance of different feature variables for irrigated cultivated land identification, and compared the accuracy of different irrigation mapping methods. The results show that the IPI is significantly correlated with the actual irrigation area, the area of irrigation facilities, and the number of active irrigation wells. It has a better indicative ability of irrigation in areas with a suitable climate or arid areas, than in areas with a humid climate. The crop water stress index (CWSI), IPI, vegetation index (EVI and NDVI), and land surface temperature difference between day and night are the most important feature variables that are used to distinguish irrigated cultivated land from rain-fed cultivated land. There are differences between the recognition accuracies of the three mapping methods in different climatic regions. Overall, the spatialization method had the highest accuracy (overall accuracy = 86.92 %, kappa = 0.74) and the highest similarity with the existing high-resolution irrigation maps. This study provides a direct and useful reference for other researchers to select feature variables and mapping methods in irrigated cultivated land mapping research.

Remote Sensing Scene Classification using ConvNeXt-Tiny Model with Attention Mechanism and Label Smoothing

Remote Sensing Scene Classification (RSSC) is the discrete categorization of remote sensing images into various classes of scene categories based on their image content. RSSC plays an important role in many fields, such as agriculture, land mapping, and identification of disaster-prone areas. Therefore, a reliable and accurate RSSC algorithm is required to ensure the accuracy of land identification. Many existing studies in recent years have used deep learning methods, especially CNN combined with attention modules to solve this problem. This study focuses on solving the RSSC problem by proposing a deep learning-based method (CNN) with the ConvNeXt-Tiny model integrated with Efficient Channel Attention Module (ECANet) and label smoothing regularization (LSR). The ConvNeXt-Tiny model shows that a persistent superior outperforms the ‘large’ model in convinced metrics. The ConvNeXt-Tiny model also has a huge advantage in high-precision positioning and higher classification accuracy and localization precision in a variety of complicated scenarios of remote sensing scene recognition. The experiments in this study also aim to prove that the integration of the attention module and LSR into the basic CNN network can improve precision, because the attention module can strengthen important features and weaken features that are less useful for classification. The experimental results proved that the integration of ECANet and LSR in the ConvNeXt-Tiny base network obtained a higher precision of 0.38% in the UC-Merced dataset, 0.7% in the AID, and 0.4% in the WHU-RS19 dataset than the ConvNeXt-Tiny model without ECANet and LSR. The ConvNeXt-Tiny model with ECANet integration and LSR obtained an accuracy of 99.00±0.41% in the UC-Merced dataset, 95.08±0.20% in AID, and 99.50±0.31% in the WHU-RS19 dataset.

Spatio-Temporal Mapping of Land Use/Cover and Population Change in a Biosphere Reserve: The Case of Lake Bosomtwe Basin in Ashanti Region, Ghana

Spatio-Temporal Mapping of Land Use/Cover and Population Change in a Biosphere Reserve: The Case of Lake Bosomtwe Basin in Ashanti Region, Ghana

Land cover fraction mapping across global biomes with Landsat data, spatially generalized regression models and spectral-temporal metrics

Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium-resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients.

Analysis of soil fertility status on rice (Oryza sativa L.) planted land in Polewali District, Polewali Mandar Regency

Rice is the main food commodity in Indonesia. The main abiotic factor affecting rice productivity is soil fertility. This study aimed to determine the soil fertility status (SFS) of paddy fields in Polewali District, Polewali Mandar Regency, West Sulawesi. The research was conducted by surveying soil characteristics supported by soil chemical analysis at the Laboratory of Soil Science and Environmental Conservation, Faculty of Agriculture, Universitas Muslim Indonesia. The research was conducted in Polewali District, with the survey area consisting of 4 land map units (LMU). Soil fertility status was determined based on the criteria of each soil fertility parameter, namely: soil pH, organic C-content, cation exchange capacity, available P, Potassium content, and base saturation. The results showed that the SFS in Polewali District was classified as low. Only the base saturation parameters in LMU-2 and LMU-3 were classified as high. In addition, all parameters were only classified as very low to medium at all LMUs. Low C-organic content and very low P and K content are the main limiting factors for SFS. Recommended land management to improve soil fertility is the addition of organic matter that can increase total nitrogen and C-organic in the soil and the application of manure. Keywords: Soil fertility status, paddy fields, rice plants

Implementation Effectiveness Law Number 4 Of 1996 Concerning Rights Of Dependents Case Study: Registration Of Rights Of Dependents In Pangkep Regency

This study is intended to analyze the implementation of registration of dependent rights in Pangkep Regency, for this reason, several variables have been determined as factors causing the effectiveness of the implementation of registration of dependent rights. This research is a research of Normative Law (Normative Juridical) and sociological (empirical) legal research. Empirical research was conducted on 50 respondents consisting of dependent rights holders or creditors in Pangkep Regency and also taken from the population which is the source in the implementation of registration of dependent rights, namely the Head of the Pangkep Regency Defense Office and PPAT Notaries who entered the research area. The empirical data is analyzed using frequency distribution. In addition to respondents as a source of data as mentioned above, the author also obtained data from the land map office of Pangkep Regency. The results showed that the implementation of registration of dependent rights has not been carried out in accordance with applicable regulations. It can be proven that there are still dependent rights that are submitted for more than 7 (seven) working days, and there are even credits that are not tied to dependent rights. In the implementation of registration of dependent rights in the future, it is recommended to improve the quality and quantity of human resources with training and mental education to be honest, fair and devoted to God Almighty.

Difficulties in land regulation in Greater Lomé and Atakpamé. Contexts and explanatory factors

In Togo and particularly in greater Lomé and Atakpamé, we see that the State has difficulty regulating or controlling land. The objective of this article is to examine the factors that make land regulation difficult in Togo in general and in greater Lomé and Atakpamé in particular. To achieve this, the research used quantitative and qualitative approaches based respectively on questionnaire administration and individual interview techniques. For the quantitative survey, 208 landowners were interviewed in greater Lomé compared to 122 in Atakpamé. Concerning the qualitative approach, 24 resource people were interviewed in greater Lomé compared to 21 in Atakpamé. The results of the investigations showed that the non-registration of plots purchased in the cadastral service, the absence of land mapping, unconventional subdivisions, land speculation and the purchase of family land constitute factors which make regulation and management difficult. land security. To compensate for illegal urban practices and sustainably control land, it is necessary, in addition to existing measures, to adopt an inclusive and participatory approach which would bring populations, local elected officials and the state into a chain of collaboration.

Urban explosion and hotspots of forest loss in western Himalaya: Mapping land use/cover change trends since 1975

The Western Himalayan Region (WHR) is under constant threat from Land Use and Land Cover Change (LULCC) brought on by various socioeconomic activities such as urbanization, agricultural expansion, and population growth. Detailed research into the scope of these changes is necessary to comprehend the challenges they represent. This study aimed at (1) examining and quantifying spatiotemporal patterns of LULCC from 1975 to 2015, (2) mapping the hotspots of forest loss, and (3) modelling future LULCC for the year 2055 using medium spatial resolution multispectral satellite imagery (MSS, TM, LISS-I, LISS-III, AWiFS), field data, visual interpretation and logistic regression model in the WHR. Logistic regression integrated various drivers of LULCC, including climatic, topographic, and socio-economic factors and past LULCC trends to forecast potential LULC in the WHR by 2055. The results highlight an outrageous 184% increase in regional urbanization. Expansion of urban and cropland (12%) led to deforestation which reduced the area of natural habitats, specifically forests (−11%), water bodies (−8%) and scrubland (−6%). A significant rise in barren land by 30% was noted, while there was a decrease of 20% in glacier/snow melt. The future projection for 2055 anticipates continuing current trends, emphasizing an intensified urban expansion of 63% and an approximate 9% reduction in overall forest coverage. The accuracy of the predicted 2015 LULC map was assessed using ROC/AUC (Receiver Operating Characteristic/Area under Curve) which was found to be 0.79.

Application of GIS and Multi-Criteria Analysis (MCA) in Land Evaluation at Ninh Phuoc District, Ninh Thuan Province, Vietnam

This study was conducted to assess land suitability (land evaluation) in the Ninh Phuoc districts in order to identify evidence for recommending main crops effectively and sustainably using agricultural land under drought conditions. Results indicated that Ninh Phuoc district had 83 land mapping units (LMUs), with 10 main land use types (LUTs) of rice, annual cash crops, and perennial plants. About 99.2% of agricultural land was classified as high suitability (S1), moderate suitability (S2), and marginal suitability (S3) for most LUTs, while non-suitability (N) lands were very small (0.8%). This study’s land suitability classification analysis has offered valuable insights into the current land use situation in Ninh Phuoc district and proposes sustainable land management recommendations. It has taken into account various land use criteria and assumptions. The study has proposed the following actions for sustainable land use: (i) Maintain highly suitable (S1) and moderately suitable (S2) land areas, (ii) Renovate or convert marginally suitable land (S3) to suitable tree planting/forestation, (iii) Convert unsuitable land (N) to forestry, grass, or plants that enhance soil quality, and (iv) Preserve existing forest areas without encroachment. Specific results in Ninh Phuoc district when applying land suitability classification for future orientation are as follows: maintain 14,859.31 ha, which has high suitability (S1) and moderate suitability (S2) levels; maintain 30.78 hectares of grass and convert 335 hectares to other suitable trees/forest plantations for the suitable marginal area (S3); Convert 122 ha of Not Suitable (N) to afforestation/grass/apply land soil improvement measure; For more than 3700 hectares of unused land, put into use 343.53 ha for growing spice plants, 8.67 ha for rice, 677.18 ha for planting forest trees, 2,691.50 ha for development of pasture and grazing.

LAND UTILIZATION AND DEVELOPMENT OF VANAME SHRIMP CULTIVATION IN COASTAL AREA, KUBU SUB-DISTRICT, KARANGASEM REGENCY, BALI

The pond land suitability is the most important things in shrimp farming to maintain production and ensure the development of aquaculture businesses. This research aims to determine the outland suitability of the coastal area of Karangasem Regency, evaluate the distribution pattern of cultivation activities and identify regional development factors. This research was a descriptive qualitative study where the analysis results related to mapping of land uses studies are presented descriptively. The data used in this study consisted of primary data: (i) physical-geographic of coastal areas; (ii) public aspirations in coastal areas, and secondary data traced from BPS, Ministry of ATR/BPN Office of Karangasem and PUPR Service Karangasem Regency. The results of this research showed that the spatial analysis of the land suitability in Kubu sub-district showed the land potential with high suitability is 1,009.46 ha; moderate suitability land level is 1,894.01 ha; for coastal areas that do not have potential or are not feasible for developing shrimp water ponds is 799.67 ha. The Kubu sub-district is a center of the shrimp water ponds development in Karangasem Regency, Bali. The existing condition of shrimp water ponds in Kubu sub-district are spread over 3 villages: Sukadana with 20.36 ha; Tianyar in 1,32 ha and West Tianyar with 0.47 ha in area. Through the analysis of internal and external factors of shrimp pond development activities in Kubu Subdistrict, showed that the coastal area of Kubu Subdistrict is very feasible to develop Vannamei shrimp.

BLEI: Research on a Novel Remote Sensing Bare Land Extraction Index

Bare land, as a significant land cover type on the Earth’s surface, plays a crucial role in supporting land-use planning, urban management, and ecological environmental research through the investigation of its spatial distribution. However, due to the diversity of land-cover types on the Earth’s surface and the spectral complexity exhibited by bare land under the influence of environmental factors, it is prone to confusion with urban and other land features. In order to extract bare land rapidly and efficiently, this study introduces a novel bare land extraction index called the Bare Land Extraction Index (BLEI). Then, considering both Ganzi Tibetan Autonomous Prefecture and Urumqi, China as the study areas, we compared BLEI with three presented indices: the Bare-soil Index (BI), Dry Bare Soil Index (DBSI), and Bare Soil Index (BSI). The results show that BLEI exhibits excellent efficacy in distinguishing bare land and urban areas. It gets the most outstanding accuracy in bare land identification and mapping, with overall accuracy (OA), kappa coefficient, and F1-score of 98.91%, 0.97, and 97.89%, respectively. Furthermore, BLEI is also effective in distinguishing bare land from sandy soil, which can not only improve the mapping accuracy of bare land in soil-deserted areas but also provide technological support for soil research and land-use planning.

Assessment of Land Quality for Siamese Orange (Citrus nobilis var. microcarpa) Development in Pacitan Regency, Indonesia

Siamese oranges are one of the most widely developed horticultural crops in Indonesia. Arjosari and Ngadirojo are some of the sub-districts that produce Siamese orange in Pacitan Regency. One of the factors that can affect the development of citrus is land quality. This study aims to obtain land quality index data and improvement efforts for developing Siamese orange. Soil samples were taken from 25 land mapping units (LMUs) with 87 sample points. The soil sampling points were determined based on area or sampling density. Data analysis used for soil quality assessment includes correlation, principal component analysis (PCA), minimum data set (MDS), and calculation of climate quality index. The parameters used in this study are bulk density, porosity, moisture content, texture, pH, electrical conductivity (EC), cation exchange capacity (CEC), base saturation, total N, available P, available K, soil organic carbon (SOC), microbial biomass carbon (MBC), evapotranspiration, temperature, and solar radiation. Based on the results of soil quality analysis in the study area, it has a value range of 0.28 to 0.37, including low class, while the climate quality index is 1.39 with moderate class. The land quality index has a very low to low class with a value of 0.39 to 0.51. Intrinsic, extrinsic, and anthropogenic factors can influence soil quality. The study area’s limiting factors for developing Siamese oranges are EC, total N, and available P, which can be improved by making furrow ponds, adding organic fertilizer, using urea fertilizer, and SP36 according to the dosage.

Integrating RUSLE Model with Cloud-Based Geospatial Analysis: A Google Earth Engine Approach for Soil Erosion Assessment in the Satluj Watershed

This study employed an advanced geospatial methodology using the Google Earth Engine (GEE) platform to assess soil erosion in the Satluj Watershed thoroughly. To achieve this, the Revised Universal Soil Loss Equation (RUSLE) model was integrated into the study, which was revealed through several analytical tiers, each with a unique function. The study commenced with estimating the R factor, which was carried out using annual precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS). The erodibility of the soil, which the K factor describes, was then calculated using the USDA soil texture classifications taken from the Open Land Map. The third layer emphasizes the LS factor, which analyzes slope data and how they affect soil erosion rates, using digital elevation models. To understand the impact of vegetation on soil conservation, the fourth layer presents the C factor, which evaluates changes in land cover, and the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The P factor incorporates MODIS data to assess the types of land cover and slope conditions. Combining these layers with the RUSLE model produces a thorough soil loss map, revealing different levels of soil erosion throughout the Satluj Watershed. The preliminary findings indicate that 3.3% of the watershed had slight soil loss, 0.2% had moderate loss, and 1.2% had high soil erosion rates. And 92% had severe rates of soil erosion. After a thorough investigation, the detected regions were divided into risk classifications, providing vital information for the watershed’s land management and conservation plans. The mean soil loss throughout the watershed was determined to be 10,740 tons/ha/year. This novel method creates a strong foundation for evaluating soil erosion, while also highlighting the value of the cloud-based geospatial analysis and the RUSLE model in comprehending intricate environmental processes.

Precision agriculture: exploration of deep learning models for farmland mapping

Precision is required for agricultural advancements to be sustainable. Traditional farming lacks effective monitoring, resulting in resource waste and environmental problems. Farmland mapping is important for agricultural management and land-use planning. The use of deep learning techniques in farmland mapping is increasing rapidly. Excellent results have been generated from deep learning approaches in a number of applications, such as image processing and prediction. Agricultural agencies are now considering different applications of deep learning including land mapping, crop classification, and monitoring of paddy fields. This paper shall explore different deep learning models that are commonly used for image processing specifically in land mapping. The three deep learning models convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were evaluated to find out which among the deep learning models is best for land mapping. It compares the classification accuracy of the models on image processing and it can be concluded that CNN algorithm normally makes better results when compared to other deep learning models. This study offers guideline and suggestions to researchers who are interested in contributing to the field of precision agriculture with the used of deep learning techniques.

Soil classification and prediction model for critical land on the slopes of Mount Raung in Indonesia

Indonesia is in the ring of fire, which has an impact on the characteristics of volcanoes, one of which is Mount Raung. Mount Raung, part of the Iyang-Argopura mountains, has a role in influencing the development of soil types and the environment; it was recorded in 1586, 1987, 1597, and 1638. Soil types and environmental parameters have an influence on critical land conditions. Remote sensing technology has been used in various fields, one of which is land evaluation. This study examined the distribution of soil characteristics and the mapping of critical lands through remote sensing approaches. The method used to identify the morphological characteristics of soil classification and the potential for critical land was a descriptive-exploratory method. The results of the research on soil types are divided into three orders, namely Andisols, Inceptisols, and Alfisols. The three land orders are divided into nine great groups, with Lithic Eutrudepts having the highest area of 38.02%, followed by Typic Hapludalfs (21.70%), Typic Eutrudepts (9.79%), Typic Epiaquepts (7.84%), Aquic Eutrudepts (7.71%), Aquic Eutrudepts (5.64%), Fluventic Epiaquepts (5.30%), Typic Udivitrands (2.16%), and Vitric Hapludands (1.83%). Critical land based on the analysis of five factors of erosion, land use, slope, rainfall, and soil canopy density, is divided into four criteria i.e., critical area of 895.88 ha, medium critical 9,027.69 ha, and lightly critical of 14,096.89 ha. Land use, slope, and plant canopy density play a major role in the potential for critical land with a strong-very-strong level of closeness (0.350-0.610).

A Comparative Study of Soil Fertility in Organic, Semi-Organic, and Conventional Rice Field Farming Systems (Case Study: Nguntoronadi District, Wonogiri, Indonesia)

The soil fertility of rice fields is closely related to rice crop production. The research aims to identify soil fertility under different rice field farming systems, find the key factor of soil fertility, and recommend strategies to improve soil fertility based on the key factor. The research was conducted in Nguntoronadi District, Indonesia, on conventional, semi-organic, and organic rice fields. The research was an exploratory descriptive survey through a field survey approach and soil chemistry and physics analysis. Soil sampling was conducted in 12 Land Map Units (LMUs) with three replicates using purposive sampling methods. Observation indicators include soil pH, organic C, total N, C/N ratio, available P, available K, exchangeable Ca, exchangeable Mg, Cation Exchangeable Capacity (CEC), Base Saturation (BS), Aluminum saturation, soil texture, and worm population density representing soil chemical, physical, and biological properties. Soil fertility is determined using Principal Component Analysis (PCA) and scoring based on the category. The research results show that the level of soil fertility under various rice field farming systems was included in the moderate with ranges of 0.53-0.70, and organic farming has the highest soil fertility. The key factors of soil fertility include pH, organic C, available P, available K, Ca-dd, CEC, and Aluminum saturation. The appropriate management direction is the addition of organic fertilizer in the planting period.

The Influence of Amplitude Stability Index Values in Mapping Land Movements on Sipora Island

Sipora Island in the Mentawai Islands Regency is prone to land movement disasters that can damage and cause losses to the people who live on Sipora Island, plus very little data base related to land movement maps on the island. To reduce the risk that will arise, it is necessary to map land movement using the PS-InSAR method, using 48 scenes of Sentinel-1A images and comparing the amplitude stability index parameters 0.65, 0.75, and 0.85 to get differences in the distribution of land movement points and the number of land movement points on the island of Sipora, to see the relationship between the value of the amplitude of the stability index is done fisher test, amplitude stability index parameters 0.65 and 0.75 can map points with subsidence value up to -99mm / year while the amplitude stability index parameter 0.85 can map points with subsidence value -87mm / year all parameters have a maximum uplift value of 5mm / year. The number of points obtained with an amplitude stability index of 0.65 is 27561, 0.75 is 961 and 0.85 is 255. The distribution of land movement points parameter value amplitude stability index 0.65 covers the entire surface of the island of Sipora, the distribution of land movement points amplitude stability index 0.75 focuses on the north and south of the island and the distribution of land movement points amplitude stability index 0.85 in the northern part of the island. The difference parameters of amplitude stability index 0.65, 0.75 and 0.85.