Yudi Setiawan

Assessing the Seasonal Dynamics of the Java’s Paddy Field Using MODIS Satellite Images

Accurate information of paddy fields over wide areas is essential to support sustainable agricultural and a food security program. Monitoring of these lands continuously, using remote sensing technology, will provide information related to the cropping intensity in the field, as well as its dynamics change. We characterized seasonal vegetation dynamics from long-term multi-temporal MODIS satellite datasets in order to determine cropping intensity and to analyze the dynamics change in paddy field of Java. The results indicate that the methodology employed in this research distinguished many specific uses in paddy fields as means of their cropping intensity. Moreover, the seasons were the most important factor affected the dynamics change in the agricultural system. Extreme climate variability caused many paddy fields, especially in non-irrigated land, to remain barren as well the planting time was postponed. Indeed, characterizing the long-term vegetation dynamics of paddy field provides information about the characteristics and trends in these land use types, either caused by natural factors or human activities.

Assessing the Suitability and Availability of Land for Agriculture in Tuban Regency, East Java, Indonesia

Indonesian food production depends highly on Java Island, which holds the most fertile soils in the country but had limited area. The objective of the research was to analyse the availability of suitable land for agriculture in Tuban Regency, an agricultural regency in Java Island. Land suitability was evaluated with spatial multicriteria analysis, integrating soil order, land capability, elevation, slope, slope direction, land use/land cover, accessibility, and climate. Land availability was analysed, integrating the forest area status designation and the spatial pattern of regional official land use plan. The results indicated that suitable land for agriculture corresponds to 91% of the total study area, confirming the high soil fertility. Analysis of land availability then indicated that 18% of the area was both suitable and available for agriculture. Considering the actual land utilization, the future development of agriculture in the region has less than 7% of the land area left for agricultural expansion. The overall results showed the importance of looking for land allocated for agriculture outside Java Island to anticipate the need for food of a country with a high population growth rate and also developing planning for food production.

Spectral indices for remote sensing of phytomass, deciduous shrubs, and productivity in Alaskan Arctic tundra

The relationships among in situ spectral indices, phytomass, plant functional types, and productivity were determined through field observations of moist acidic tundra (MAT), moist non-acidic tundra (MNT), heath tundra (HT), and sedge-shrub tundra (SST) in the Arctic coastal tundra, Alaska, USA. The two-band enhanced vegetation index (EVI2) was found more useful for estimating vascular plant green phytomass, leaf carbon and nitrogen, leaf carbon and nitrogen turnover, and vascular plant net primary productivity (NPP) without root production than the normalized difference vegetation index (NDVI). Deciduous shrub green phytomass was strongly correlated with deciduous shrub index (DSI), defined as EVI2 × (Rblue + Rgreen – Rred)/(Rblue + Rgreen + Rred) (with a coefficient of determination (R2) of 0.63). Rblue, Rgreen, and Rred denote the blue, green, and red bands, respectively. This is because Rblue and Rgreen values were higher than the Rred values for green leaves, deciduous shrub stems, lichens, and rocks compared with other ecosystem components, and EVI2 values of lichens and rocks were very low. The vascular plant NPP without root production was estimated with an R2 of 0.67 using DSI and EVI2. Our results offer empirical evidence that a new spectral index predicts the distribution of deciduous shrub and plant production, which influences the interactions between tundra ecosystems and the atmosphere.

Leaf Area Index (LAI) in different type of agroforestry systems based on hemispherical photographs in Cidanau Watershed

Ecological functions of agroforestry systems have perceived benefit to people around Cidanau Watershed, especially in the protection of water quality. The main causes of the problems encountered in the Cidanau Watershed are associated with the human factors, especially encroachment and conversion of forest into farmland. The encroachment has made most forest in Cidanau Watershed become bare land. To preserve the ecological function of agroforestry systems in Cidanau Watershed, monitoring of the condition of the vegetation canopy in agroforestry systems is really needed. High intensity thinning of crown density due to deforestation can change stand leaf area index dramatically. By knowing LAI, we can assess the condition of the vegetation canopy in agroforestry systems. LAI in this research was obtained from Hemispherical Photographs analysis using the threshold method in HemiView Canopy Analysis Software. Our research results indicate that there are six types of agroforestry in Cidanau Watershed i.e. Sengon Agroforestry, Clove Agroforestry, Melinjo Agroforestry, Chocolate Agroforestry, Coffee Agroforestry, and Complex Agroforestry. Several factors potentially contribute to variations in the value of LAI in different types of agroforestry. The simple assumptions about differences ranges of LAI values on six types of agroforestry is closely related to leaf area and plant population density.

The effect of land use change on water quality: A case study in Ciliwung Watershed

Ciliwung is the biggest river in Jakarta. It is 119 km long with a catchment area of 476 km2. It flows from Bogor Regency and crosses Bogor City, Depok City, and Jakarta before finally flowing into Java Sea through Jakarta Bay. The water quality in Ciliwung River has degraded. Many factors affect water quality. Understanding the relationship between land use and surface water quality is necessary for effective water management. It has been widely accepted that there is a close relationship between the land use type and water quality. This study aims to analyze the influence of various land use types on the water quality within the Ciliwung Watershed based on the water quality monitoring data and remote sensing data in 2010 and 2014. Water quality parameters exhibited significant variations between the urban-dominated and forest-dominated sites. The proportion of urban land was strongly positively associated with total nitrogen and ammonia nitrogen concentrations. The result can provide scientific reference for the local land use optimization and water pollution control and guidance for the formulation of policies to coordinate the exploitation and protection of the water resource.

A simple method for developing near real-time nationwide forest monitoring for Indonesia using MODIS near- and shortwave infrared bands

ABSTRACT Regarding to the deforestation and forest degradation issue in Indonesia, it is an urgent need to develop near real-time forest monitoring that can be seen by the public to make sure the transparency of forest resources management. Remote-sensing technology seems to be a powerful tool for monitoring and assessing the changes in forest cover immediately. We explored 250 m multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data sets to assign a change of temporal land surface dynamics based on two indices; normalized difference vegetation index (NDVI) and open area index (OAI). The data sets were filtered by two filtering approaches, median moving window and linear interpolation, in order to reduce the overall noise so as not to lose useful information from the time-series data. Our results indicated that the use of MODIS data as a basic information in the near real-time system offers great promise to detect the forest cover change in Indonesia’s forestland, since about 90.07% of area assigned to be a change area had actually changed. Meanwhile, about 34.97% of the forest cover change was not assigned to be a change area by the system. This result showed the need to evaluate the threshold in detecting forest cover change. Although the threshold issue is quite problematic in forest cover change detection, the results show that the methodology proposed in this study provides sufficient and useful information in forest monitoring; this includes the location, time and trajectories of the changes. The results of the detection system developed in this article will be available for potential users.

A voxel-based model of LiDAR point cloud for estimating forest canopy closure

Within UNFCCC framework, forest monitoring should be capable of detecting emissions from not only deforestation, but also from forest degradation. In fact, determinants of deforestation are relatively more detectable using remotely sensed data than determinants of forest degradation. Forest canopy closure is one important determinant of forest degradation. In this case, loss on forest canopy closure indicates forest degradation. As part of our activities in developing methodology for estimating forest canopy closure, this paper describes our methods on estimating forest canopy closure based on ALS LiDAR point cloud through the development of a three dimensionally explicit voxel-based model of forest canopy using an open-source modelling platform of NetLogo 3D 5.3.1. Window area in South Sumatra, Indonesia was selected as the study site. Estimated canopy closure resulted by our model was compared with the results from commercial software (i.e. LiDAR360). The results of this study suggest that using a simple voxel-based model with 2 parameters within open source platform; it is possible to estimate forest canopy closure based on ALS LiDAR point cloud at relatively small deviation (around 25.04%), as compared to similar commercial software, which algorithm is usually hidden. However, validating the model with ground measured data on canopy closure should be carried out.

Revisiting the validity of Braak’s equation on altitudinal temperature lapse rate using thermal-infrared bands of Landsat 8

Spatial data on temperature are of importance to the studies concerning the roles of climate, including the impacts of climate change on ecosystem functions and ecosystem services. However, most temperature data are available at station level, sparsely and irregularly distributed across space as points, and the accuracies of spatial-interpolation-based surface models decrease with decreasing density of the observation points. Meanwhile, relationship between elevation and temperature has been acknowledged, which basis is grounded in thermodynamics theory by Robert Clausius, and later known as altitudinal temperature lapse rate. Most studies related to altitudinal temperature lapse rate in Indonesia have been using and scaling-up the findings from Cornelis Braak, based on his research in Java during the 1920s. According to Braak, temperature decreases by 0.60°C and 0.55°C as the elevation increases by 100 m asl, for areas below and above 1500 asl, respectively. With regards to climate change, Braak’s findings should be updated, since it determines climatic geo-data, used for strategic geo-planning (e.g. for suitability mapping). Thus, in this respect, the study is aimed at revisiting altitudinal temperature lapse rates in Indonesia using thermal-infrared bands of Landsat 8. With regards to Braak’s observation stations, one window area in Bogor, West Java, Indonesia was selected as the study site. The results suggest that altitudinal temperature lapse rate decreased from 0.0016 to 0.0021° C.m-1, as compared to Braak’s equation, which indicate significant temperature increase. The results also suggest that temperature increase in the window area was about 1.58°C, doubled from temperature increase at global scale of about 0.8°C, which implies to losses of montane and sub montane zones according to Holdridge life zone of about 7 km2 (100%) and 727 km2 (32.53%), respectively; and gain of basal zone of about 734 km2 (211.77%).

Automated Landsat 8 data preprocessing for national forest monitoring system

Precise digital classification for Landsat 8 data of remote sensing images require pre-processing steps. The preprocessing consist of conversion from digital numbers (DN) to top of atmosphere (TOA) reflectance, cloud and cloud shadow masking, topographic correction and image normalization. In general, pre-processing steps were implemented to National scale (Indonesia) excluding topographic correction. The topographic correction algorithm is required to avoid reflectance bias from terrain effects due to shading. The highest mountains in Indonesia were selected as window areas, considering the reflectance bias is produced due to terrain effects. The results showed that algorithm is able to solve overcorrection problems and will be implemented into LAPAN’s system of image pre-processing for National scale. This research is a collaboration between Bogor Agricultural University (IPB) with National Institute of Aeronautics and Space (LAPAN) under Forests2020 Programme, in order to produce Landsat 8 data with the minimal cloud over Indonesia annually and then to automatically digital classification for forest monitoring. The automated system of preprocessing was developed with Perl and Python programming languages.

Mapping tree height in agroforestry system using Landsat 8 data

Agroforestry is a land use management-system represents unique vegetation characteristics among tree vegetation types. Tree height is a vegetation variable used to characterize vertical structure, including mixed vegetation structure in agroforestry. Estimation of tree heights with multispectral imagery is a relatively new application and is dependent on integrating synoptic coverage optical data with samples of height data, often from LiDAR-derived reference data. In this study, multispectral Landsat 8 data, Unmanned Aerial Vehicle (UAV)-based LiDAR height data and a log-linear regression model were used to estimate tree height for agroforestry land use in western part of Java Island, Indonesia. We generated a Canopy Height Model (CHM) directly from height-normalized LiDAR points and used as reference data in modeling the key height variable in the multispectral bands of Landsat 8. The analysis showed that red band was the best band to estimate tree height in agroforestry land use, followed by swir band. The log-linear regression algorithm of red band accurately reproduced the LiDAR-derived height training data using Landsat 8 data with overestimate 1.46 m in estimating tree height < 5 m and underestimate 7.79 m for tree height > 20 m.