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Area and length measurement is very basic task in GIS. Almost every GIS software can do that. Things become harder if we want to calculate polygon volume in GIS. until few years ago, there is no simple and direct way to calculate polygon volume in GIS. Some GIS and Remote sensing software provide built in tool to do that, but these software usually have certain data format that is not easily converted into standard GIS format. Today the game has changed. Some developer has built a simple and intuitive plugin to calculate polygon volume using DEM as reference, and the most awesome aspect is, they made it in Quantum GIS / QGIS environment. This is a life saver because QGIS is a free software!. And here is my experience when I try those new developed plugin to calculate polygon volume in QGIS. ArcGIS doesnt have this luxury.  

Working in GIS using free software like QGIS is a something, but integrate it with the most popular data science language is something else. Almost all of Remote Sensing and GIS software have implemented Python integration these days, but sometimes those features hidden in the user interface. If you are familiar with python, you must be know that python has something GIS software cant provide. And this state we have an urge to why not use both of them?, here is how to install thid party python libraries to QGIS which is sometimes not all of available python libraries already installed in default QGIS python configuration. 

Google Earth Engine is a revolutionary Remote Sensing Analysis platform that changed the remote sensing data processing landscape in extreme way. This platform is powered by Cloud Computing hosted by Google and you know, by the means of cloud computing now you are enabled to process and analyze tons of satellite data without sacrificing your computer hardware. You dont have to procure high end computer to enabling the robust and sophisticated remote sensing and digital image analysis workflow. You just need a browser to run all the complex processing. You only needs a good knowledge of javascript programming to make google earth engine works for you. On the other side, may be you are asking about what if I didnt have enough knowledge of javascript. What I am doing all the time is python programming. Dont worry my friends, now Google Earth Engine is also provides Python engine for you to use. You can run Google Earth Engine in the Google Colaboratory Environment, or you can also do it i...

All remote sensing users must be familiar and used to playing with multispectral bands combination to get certain information from imagery. If you are deeply understand about spectral response curve of surface earth objects, you will know that certain object (vegetation for example) gaves different spectral response compared to another objects. For satellite imagery consist of more than 3 bands (RGB in digital camera), different bands combination in RGB planes gaves different emphasizes to landcover colors in the imagery. Hence they are called multispectral imagery. Most of low to medium resolution imaging satellite currently operational in the earth orbit are multispectral, like landsat, modis, noaa avhrr, and many others.  Multispectral imagery number of bands can range from 4 bands to dozens bands, but not reaching hundreds. When the bands number reached more than one hundred bands it is called hyperspectral imagery and its analysis and image processing workflow would be pretty ...

Agisoft Metashape (formerly Agisoft Photoscan) is a well known Photogrammetric UAV/Drone Processing software. Using this software you can make DEM, DSM, DTM, orthophotos, orthomosaics, and 3D model from Drone / UAV survey. In the latest version, this software expanded its capabilities not only enable to process UAV/Drone Photos, but also Stereo / Tri Stereo Satellite Imagery. This feature is awesome because existing Stereo Satellite imagery processing is only can be facilitated by expensive remote sensing software like ERDAS IMAGINE, ENVI, PCI Geomatica, Socet Set. Different with dedicated remote sensing software, Agisoft Metashape can generate DEM without GCPs. Photogrammetry become more simple, altough if we talks about accuracy, that is another problem. Anyway here is my experiment and experience when I am trying the Agisoft Metashape for stereo satellite imagery processing. 

All of the ArcGIS user may be has been known about arcgis desktop software and right now ArcGIS pro has been coming to replace all the good old days doing GIS in the 2000's and 2010's. But lesser known to common GIS users is that ESRI also develops the webGIS system as the complementer product for the desktop. They are ArcIMS, then they are evolved to ArcGIS Server, evolving again to Portal for ArcGIS and the rise up to the complete GIS system of ArcGIS Enterprise.  ArcGIS enterprise is such a superpower GIS system that they are not only offers seamless integration between Desktop GID and WebGIS, but also its API. ESRI API consist of several API system that dedicated to particular tasks inside a GIS workflow. One of them is ArcGIS API for Python. Just like its name ArcGIS python API is dedicated to handle python based spatial analysis and Portal management thru a python notebook interface. Its python notebook interface is similar to wellknown jupyter notebook. They offers fancy...

The availability of multiple processing levels of satellite imagery products, either in the free or commercialized domain, by multi-national government agencies or private companies has made imagery utilization for many applications easier than ever.  Take Sentinel-2 imagery as an example, it is been delivered to the public as multi-level data products. You can get L1c products that only radiometric and geometrically corrected, or higher level L2a which the data has been processed to reflectance unit, furthermore it is already atmospherically corrected. So we don't talk about Top of Atmosphere reflectance anymore, we talk about surface reflectance.  However, all this easiness should not make us became lazy person alright?. It is important that remote sensing users know how to do atmospheric correction either in the practical or theoretical domain. Without deep knowledge of remote sensing theories, all of your image processing and analysis skills cant be trusted in order to get...

Land Surface Temperature data (LST) is an important data for many kind of geospatial analysis. It could provide initial information about environmental condition like drought event, Urban Heat Island Phenomena, climatic condition, air pollution, and many others. LST can be generated from satellite imagery thermal bands with additional information from Visible Near Infrared Bands. Because there are not many operational satellite imaging in orbit can provide these kind of data. Precisely the sensors that could provides LST only limited to certain sensors at low to mid resolution like MODIS, AVHRR, Landsat 4 to 8, ASTER, and the latest Sentinel-3.  If we talks about how to generate LST from satellite imagery, many researchers have been published numerous methods published in wide range of scientific papers. These methods ranging from simple single channel thermal band LST extraction to more complex methods involved meteorological parameters like water vapor, clouds height, and many ot...

There are many government agencies or NGO have been published a wide range of landcover data. Those data ranging from national level to global level. Commercial vendors also provides wide range of landcover mapping services and products from medium resolution to very high resolution, using wide range of techniques like image classification, segmentation, or "the old but reliable" method visual interpretation. With the widely usage of latest AI technology like Machine Learning and Deep Learning, Landcover mapping even reaching the stage of combination the benefits of Image Classification and Visual Interpretation. These Deep Learning Algorithm could give mapping results in polygon format from raster domain, something that not straightforwardly possible in the previous decades.  One cool thing about these landcover data is most of them are available for free. One of them is European Globcover Dataset. It provides landcover data at medium resolution in global scale. I don't ...

How to Add Google Maps and Imagery in ArcGIS Desktop or ArcGIS Pro Without using additional Tools Until few years ago, adding google maps in ArcGIS is not something easy to do, you needs some plugin or additional tool to do that. Sometimes the plugin is not available for free. This problem has changed in recent years due to the improvement of basemap technology. Adding Google maps or other online maps like bing maps, yahoo maps, Here Maps, Mapbox Maps to ArcGIS desktop or ArcGIS pro now is very easy. I have made tutorial about how to do that in video below. 

Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.  

Download global historically enable water surface raster data at 30 meters in geotiff format. This data have few subdataset that can be used for various of spatial analysis like Flood prone area mapping, wetlands mapping and identification, lakes, ponds and Dam identification, drought monitoring and vice versa One benefit of this dataset is this data is compiled from historical data, so if this data said that certain area are permanently inundated, it has very high probability that the area is true permanent water body, and not seasonal water body.  

Covid-19 pandemic has been hit our lives entirely. There are so many negative impact of COVID-19 to almost all of every aspect of daily living, no matter in which place you are live, which country you are reside. The worsening pandemic needs full attention not only for the global governments, but also all the stakeholders, private companies, non-profit organization, everyone should be cooperated and working together to make this pandemic over soon.  Geospatial data is always playing important role on this matter. Take the story of John Snow in England as an example. He was the first person to map the distribution of utilities with the cholera outbreak distribution, and by looking the map made by himself, he can identify the sources of the infection, just by spatial analysis. He show the power of multivariate mapping and spatial analysis to solve previously uncharacterized disease distribution.  Now if we return to the currently happening COVID-19 outbreaks, many agency, compan...

Measure building height is not something easy to do in the field. You going to needs field equipment that is not always easy to carry around. On another occasion may be you needs Digital elevation surface data to helps you correctly measure building height. Even you going to needs finer resolution DSM that so expensive and hard to find. Is there any other way if you want to know building height in very easy way.  Lucky for you, ArcGIS Desktop has been implemented a measurement tool that is not only works in 2D space, but also in 3D space. The tool is called Mensuration Tool. It can measure point, line or polygon in 3D, so you will get Z-dimension information like height or elevation from a source data. Currently it could measure building height from high resolution satellite imagery in an intuitive way. By intuitive I means its precision of measurement will highly depends on how you do the measurement. You can directly measure house or building height using its shadow as reference,...

Environmental issues hugely depends on geospatial data to help get things fixed. Geospatial data can helps in so many ways in every phases of solution formulation related to environmental and disaster issues. A lot of country have launched thousands of satellite platform to helps monitor environment condition on the earth surface. Daily data about various issues are common today. There are many of them so unique, sophisticated, and very useful to support complex analysis to overcome all those environmental and disaster management issues.  One of cool data that recently I found is daily forecast or actual information of fire Hot Spot derived from satellite imagery analysis (MODIS and VIIRS). These data can be accessed freely and easily from free-to-use Open Source GIS package Quantum GIS. By integration with QGIS, you are possible to directly made a forest fire hazard map in daily basis, and advocate the map to the related stakeholders. This is very cool because this is enabling tim...

This tutorial may be wont excite many people, but I would like to helps all the newbie learner of ArcGIS Pro. ArcGIS Pro is a monster but just like ArcGIS Desktop at its prime, we still needs a quite amount of learning time to finally grasp all the offered analysis commands and tools. So this tutorial has been made for that. Hopefully it will helps someone out there.

Many geospatial and GIS experts know about geocoding. Geocoding can be defined as a automatic conversion of address to map coordinates and vice versa. From a strategic point of view, geocoding could improve the mapping process, especially if we have data that didnt have map coordinates, but it still at least have textual location information (i.e city name, country name, zip code etc). Using this location information and a geocoding processor, you can swap the address into coordinates, and finally you can get a point distribution map of your data in geographic space.  I have mentioned geocoding processor above. Now a geocoding processor is a service designed very specific way that its capabilities is only limited to conversion between addresses and coordinates. Geocoding processor could come at least in two way, it is provided by geoocoding service provider or you can make your own geocoding processor.  Currently there are many mapping companies that have provide geocoding ser...

Route planning is one activity that may be almost everyone in this world doing in every day basis. Either plan a trip to certain tourism destination or simply looking for a route to nearest mart, it is a part of daily activities. Take Google Maps routing features as an example. It is being used by many people to plan and guide the trip activities. It is so easy to use even non GIS person can do route analysis without needed for understanding about Geography and GIS.  Plan a trip route is something, but flexibility to use the created route is another matter. You can only uses Google Maps Routing services inside a google maps apps or web. This situation is also similar with other online trip planner tools. Now what if we needs to uses the created route to another apps or devices, may be you want to put the route generated in Google Maps into your GPS handheld, so you can directly navigate your route using your own preferred device. This is also something that easily you can do. With ...

DEM ( Digital Elevation Model ) extraction is one of popular topic in GIS and Remote Sensing. There are so many ways and methods to produce a DEM. You can use survey data in points format then interpolate it to became raster DEM, or using contour lines to generate DEM. Almost every GIS and Remote Sensing can do this kind of analysis. The better approach and yielding a better result is using Photogrammetry techniques. For photogrammetry, you needs satellite imagery or aerial photographs that has image overlap between flight line that later can be opened together in a photogrammetric software, and then from the overlaps, height and elevation information could be reconstructed and sampled together in their respective grids, and finally a DEM has been created.  Recent development of survey technology has introduce us to LIDAR technology that enabling us to get a DEM in superfine resolution and covering large area. UAV technology is also progressed so much even photogrammetry survey can...

Tulisan ini berangkat dari postingan saya dua tahun yang lalu di SINI dan di SINI . Setelah lewat dua tahun, saya semakin memahami konsep, proses dan metode teknis dari INSAR. Kunci pentingnya sudah saya kemukakan di dua postingan di atas, yaitu koherensi. Koherensi yang bagus hanya dapat diperoleh jika, (1) selisih waktu perekaman citra SAR nya sesingkat mungkin (terutama untuk frekuensi C-Band)  (2) perpendicular baseline lengthnya cukup panjang tapi juga tidak terlalu panjang (100- 1000 m) Kemarin, saya akhirnya mengetahui proses download data SAR yang lengkap dari ESA (akan saya posting tutorialnya di youtube channel geo2004). Dari situ saya memperoleh informasi tentang adanya tandem mission antara satelit ERS-1 dan ERS-2 pada tahun 1995-1997, yang memungkinkan untuk memperoleh data SAR interferometri dengan selisih waktu 1 hari. Saya kemudian mendownload satu pair untuk wilayah Yogyakarta (direkam pada tanggal 21 mei 1996 dan 22 mei 1996), dan memprosesnya di ESA SNAP. ...

This article is a continuation from my previous post about how to perform Wind Direction and Speed Estimation from SAR data. In previous post that you can also read from this blog, I have shown how to directly measure Wind Speed and Wind Direction using ENVISAT-ASAR data. And you can also perform this kind of analysis using other kind SAR Sensors like ALOS PALSAR, Sentinel-1 or ERS.  Now for this post, I will talks about post-processed wind speed and wind direction data generated from Sentinel-1 SAR data. Why Post-Processed ? because ESA already publish wind data from Sentinel-1 into Sentinel-1 OCN Product. So if you are using this OCN product, you dont have to generate Wind Speed and Wind Direction by yourself.  The video tutorial below demonstrate how to get those wind parameters data from Sentinel-1 OCN Product, using ESA-SNAP Software. Hopefully it will find some use to you out there. 

SAR (Synthetic Aperture Radar) data can have many information about earth surface that cant be obtained from Optical or thermal imagery. One of them is Ocean Winds data. Using SAR data, you can perform wind speed and direction estimation at the ocean area by analyzing the winds streaks that well recorded in SAR data. This wind speed and direction is an essential data in Oceanography or Marine Geomorphology because this data can be used for coastal dynamic analysis. On the other hand, direct wind measurement in the field sometimes costly and hard to do.  I am going to deliver this topic into two parts. First is about how to measure wind speed and direction using SAR Data, and second is how to get post processed wind direction and speed data from publicly available SAR data.  Now about how to directly measure wind speed and direction from SAR data, I already made a tutorial about how to do it in ESA-SNAP. ESA SNAP is a remote sensing software developed by European Space Agency (...

SAR data can be used for many types of applications. Distinct characteristics compared to optical imagery makes SAR data stand out in its own way. One of the applicable use of SAR data is for maritime transport monitoring. SAR data with its unique backscatter mechanism could be used for ship detection and tracking. This is possible because the body of the ship double bouncing the radar backscatter when it is returned to the sensor, so the recorded energy would be doubling out and makes the ships look so bright in the SAR images compared to the water background that has low pixels value. And because radar SAR imagery is geocoded to a known coordinate system, finding the ship's coordinates at a certain time is also quite easy.  Now if we have operational SAR satellites that have a short temporal revisit (one day for example), the ship's detection, and tracking would be better because we could monitor the ship's position daily. We don't have this kind of service right now ...

Sentinel Satellite Imagery is known for the popularity on geospatial data sources in recent years. First, it is free to use and free to download, second, it is ranging from medium resolution to high resolution, third, it comes from Optical Remote Sensing Spectrum to Microwave SAR Spectrum. It is all gives a comprehensive and holistic data usage experience for the users. The data access and download support are also easy and straightforward. You can access it from Copernicus SciHub, USGS earth explorer, ASF Data Facility, and many geoportals developed by EU Agencies or Private companies. Sentinel data is also stored in AWS (Amazon Web Services) and has declared as an open repository/buckets in AWS, so you can also use the data in a cloud computing environment. This is why many users have been using the sentinel data in clouds (for example, Google Earth Engine Users have been used the AWS repo for the last few years, and many geospatial algorithms or automated processing code have been d...

Mengidentifikasi Tahun Penerbitan dan Tahun Pembuatan Peta Rupabumi Indonesia (RBI) sangat mudah kalau kita punya peta cetaknya. Ya jelas, karena informasi tersebut tercantum di informasi tepi petanya. Dengan mengetahui tahun penerbitan dan pembuatan Peta RBI, kita jadi dapat mengetahui kapan Peta RBI terakhir di-update atau diperbaharui. Terlebih Peta RBI telah ditetapkan menjadi Informasi Geospasial Dasar (IGD) berdasarkan Undang - Undang Nomor 4 Tahun 2011 Tentang Informasi Geospasial, sehingga Peta RBI menjadi acuan dan peta dasar dalam pembuatan peta tematik.  Peta RBI sendiri selain diterbitkan dan dipublikasikan dalam bentuk Peta Cetak (format digital JPEG-nya saat ini juga telah tersedia di Portal Tanah Air Indonesia), juga dipublikasikan sebagai data spasial vektor yang dapat diunduh di dalam Portal Tanah Air Indonesia. Melalui publikasi dalam bentuk data spasial ini, Peta RBI dapat lebih mudah digunakan sebagai peta dasar atau IGD untuk mendukung penyusunan peta tematik t...

Deep learning as one of the branch of machine learning has become trend in latest development of geospatial technology. Using Deep Learning, sophisticated image classification that resulting classification result in polygon format has made possible. Deep learning also open for semantic object detection like detection of feature that has unique characteristics in imagery like pools, bridge, wells and many others. Imagery utilization became boundless if we incorporate Deep Learning into geospatial analysis. ArcGIS Pro as the latest ESRI invention to deal with mainstream GIS business also implement Deep Learning support. With its native integration with Python and Anaconda Framework, Deep Learning made possible in ArcGIS Pro. Many Deep Learning Algorithms has been supported in ArcGIS Pro, ranging from Microsoft CNTK, KERAS, Tensorflow, PyTorch and many others.  Although available natively in ArcGIS Pro, initially ArcGIS Pro is only support classification and segmentation based on pret...

This video tutorial below may be wont interesting for some people, but I made this tutorial with a single goal, helping people that still learning GIS and have a requirement to use GIS data to be analyzed in Google Maps or Google Earth. KML/KMZ is a native format for Google Mapping Products. It can store vector as vector or vector as raster or raster as raster. It is very convenient, although it usage in context of pure geospatial data format is limited.  There are some strategic aspect about KML/KMZ usage. The most important things is you can host your geospatial data for free in Google MyMaps. You can made simple webmap that has basic webgis functionalities, powered by complete and strong basemap and online imagery basemap, and you can do multiuser editing. KML/KMZ data supporting multiuser although it is a file based data. So if you are interested to use this format, and your data is still on shapefile or geodatabase format, ArcGIS has the tool to convert it to KML/KMZ, and vide...

ArcGIS and QGIS are two giants of GIS Software in the modern era. Both of them are used by most of GIS experts around the world. They come with similar features but still there are some distinction about their capabilities. Take the Geospatial data format as the reference, ArcGIS has its own native geospatial data format called file geodatabase. File geodatabase is such a complete format that it is not only capable to store vector data, but it also can store raster data, topology, relationship table, geoprocessing tools, domain reference, custom coordinate system, you name it.  And now about QGIS, it is still a young player in GIS although it is been developed by Open Source Communities for more than 20 years. QGIS born as lightweight GIS software with very limited capabilities, but few years later, it is growth into a giant with a wide range capabilities to tackle complex GIS processing ranging from spatial analysis, database management, to raster simulation. Even there are some f...