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Current imaging technologies are so advanced that photography today is not only about the photo result, but also many additional properties, include the photo location. Embedding location is very common today because almost all imaging enabled devices (smartphone, notebook, tablet, GPS handheld, etc) as abilities to store GPS coordinates into the EXIF metadata inside a photo. Online services like FLICKR or Google Maps also provide ability to host a collection of photos/images that also can display the photos based on its coordinates into an online maps.  Now for GIS users, does the display photos or images based on its location into a GIS software?. The answer is yes you absolutely can. You can do this kind of visualization almost in every GIS software available on the market. ArcGIS and QGIS absolutely can do this. Now if you are interested to do this in GIS environment, I have created a video tutorial about how to do it in ArcGIS below. 

When I first made this blog, I wrote few articles about Panchromatic Sharpening (Pan Sharpening). This article is also can be considered as one of them. In this article, I will share about Gram-Schmidt Pan Sharpening Method. Since a few years ago, Gram Schmidt Pan Sharpening Algorithm has been implemented in ENVI Software, and it becomes one of the best Pan Sharpening methods that could not only preserve spatial details of the Panchromatic band but also maintain the spectral fidelity of multispectral bands.  The goodness is, this algorithm now is also has been implemented in ArcGIS. So finally we can perform a decent Pan Sharpening operation in GIS Software. This will reduce multiple software operations when we need pan-sharpened data. So finally it would be more effective for us, GIS analyst. I won't explain the technical aspect of the algorithm because there are many articles on the internet that already talks about it. I only want to share how to perform pan sharpening using Gra

ENVISAT is the flagship earth observation satellite of the European Union from 2000 to 2010. It brings a huge number of payloads and sensors. The most well-known are AATSR, ASAR, and MERIS. ASAR (Advanced Synthetic Aperture Radar) is the continuation of the ERS1-ERS2 satellites which operate in the nineties to early 2000. The satellite now has been decommissioned due to communication problems, but the data is still considered precious because it is recording the earth phenomena in the last decade.  ENVISAT ASAR data has been available for the public, however, its access right now is quite limited due to EU rules. ENVISAT ASAR data comes in with a wide range of data formats, and all of them are supported to read and process in ESA SNAP Software. So if you have access to ENVISAT ASAR, I advise using ESA SNAP if you want to get maximum benefits of the data. Nonetheless, other SAR Software can also process this data. Recently I have access to the data and I start to play around with the da

Setelah bereksperimen DEM Extraction dengan ALOS-1 PALSAR dan Sentinel 1A dua tahun lalu (ulasan dapat dibaca di postingan INI , saya berkesempatan untuk mengulang eksperimen tersebut dengan menggunakan ESA SNAP versi 6.0 yang sudah banyak pembaharuan. Eksperimen kali ini menggunakan data Sentinel-1 SLC yang diunduh dari ASF Vertex, terdiri dari dua citra, yaitu Sentinel 1-A dan Sentinel 1-B dengan selisih perekaman 6 hari. Tahapan umum yang dilakukan adalah sama dengan apa yang saya tulis dua tahun lalu, perbedaannya adalah, sekarang proses UNWRAPPING berbasis SNAPHU dapat dilakukan secara langsung di software SNAP. Dengan demikian, update dari workflow yang saya lakukan adalah sebagai berikut: Read Products (2 SLC Images) > Sentinel 1 TOPS SPLIT (saya ambil 2 burst) > APPLY ORBIT FILE (untuk kedua data) > SENTINEL 1 TOPS BACK GEOCODING > INTERFEROGRAM FORMATION > SENTINEL 1 TOPS DEBURST > GOLDSTEIN PHASE FILTERING > MULTILOOK > SNAPHU EXPORT > SNAPH

Surface water body data like Lakes, Ponds, Reservoirs, Rivers are important geospatial data for many purposes. This kind of data can be obtained in many ways. They can come from field measurements, GPS-based measurements, or derived from Aerial Photographs or Satellite Imagery. Deriving surface water features from aerial photographs or satellite imagery, in particular, has some strength compared to more conventional mapping methods. Aerial Photographs or Satellite Imagery recording the surface water body in a synoptic way. It s is basically just like the real features that we saw in the field. It is also efficient and time-saving. You can get the data either in vector format or raster format depends on your preferred extraction methods.  Particularly for satellite imagery, these data could come from a wide range of imaging sensors operates in different electromagnetic spectrum (from optical spectrum to radar/microwave spectrum). Different spectrums will expose different characteristics

CAD and GIS are both technology related to mapping and spatial dimensions of earth surface objects. CAD mainly is used in the Architecture or civil engineering domain (microscale) and GIS is used for spatial analysis at the landscape/regional level. Both of the technology are using spatial representation in vector graphics structures. The format for these vector data is various, but DWG is a major format in CAD since decades ago, and Shapefile/SHP is the major and most used spatial data format in GIS for at least since the nineties.  Because both of them are using vector graphics, straight conversion between two mainstream formats is absolutely possible. Modern CAD software mostly supports direct reading of SHP format and vice versa. CAD also has point representation, polyline representation, and polygon representation as well as Shapefile GIS format. There are no many differences between those two formats. The only difference is CAD DWG format usually didn't store tabular informat

Selamat sore teman – teman GIS.ID, sudah agak lama ya ga ada kulgram, kali ini saya berbagi sedikit materi GIS, dengan topik aplikasi linear referencing untuk pemetaan berbasis jarak. Saya nggak akan banyak menguraikan dasar teoritis linear referencing, otak saya nggak nyampe, yang penting tahu cara makainya, kelebihan, kekurangan dan asumsi yang mendasari. Sebelumnya, perlu untuk kita review kembali, bahwa dimensi data spasial itu kan sebenarnya ada empat ya, X, Y, Z dan terakhir dan paling jarang disentuh adalah M alias Measurement. Nah linear referencing memanfaatkan dimensi M ini untuk geospatial problem solving. Secara spesifik linear referencing system (LRS) bekerja untuk topology data Polyline dengan Events layer dapat berupa point atau line. Dan sebagaimana dimensi Z, Unit untuk dimensi M ini sangat fleksibel, bisa jarak (geodesic/cartesian), waktu, biaya/cost, energi, dan lain lain. Linear referencing, Namanya juga linear, prinsip dasarnya adalah melakukan ak

Treating Raster data like vector data where we can edit or modify the data in pixel level has been a dream. This kind workflow actually can be done in older GIS or Remote Sensing software like ILWIS, but in QGIS this functionality has just been released. You can edit or modify the pixel values using few options, pixel by pixel, based on region selection, based on rectangle selection or few more selection options. Commonly alteration of raster pixel values only can be done thru map algebra or raster calculator.  It is all with a single goals, to make the raster data are easier to manage and update. But this functionality has a weakness, it gives people chance to manipulate pixels value that previously hard to do without raster calculator/algebra. Everything always come with pros and cons, that is just natural. \ So for you who looking for a way to edit raster directly on a pixel by pixel basis or using more batch selection method, tutorial below is designed to helps you with that kind o

ArcGIS have so many of hidden features and GIS easter eggs for your information. One of them is demonstrated on the video below. A hidden trick to delete or remove the Online Basemap Attribution/Services credit. This is important because sometimes the basemap attribution is so long and disrupt the map composition, so there are urgency to have a method to remove them.  Yes this practice is considered as non ethics because attribution is important and we should give a proper credits to those that made the data. But if you are still insist to know, here it is. Btw this method is not only working on ArcGIS Desktop, it is working too for ArcGIS Pro. 

GeoJSON (Geo Javascript Object Notation) is an extension to standard JSON format that widely used in API Development. One thing that differentiating between JSON and GeoJSON is, GeoJSON incorporates Geographic Coordinates in the data. This geographic information enables GeoJSON to be displayed into online webmap and served into webgis apps or mobile gis apps.  In term of geospatial data,   GeoJSON supports the following geometry types:  Point ,  LineString ,  Polygon ,  MultiPoint ,  MultiLineString , and  MultiPolygon . Geometric objects with additional properties are  Feature  objects. Sets of features are contained by  FeatureCollection  objects.  Compared to other geospatial data format, GeoJSON is easy to maintain, simple, and plain just like XML, so it can be edited using certain parser or renderer dedicated to GeoJSON. Almost every Mapping API Framework like Mapbox, ArcGIS, Leaflet, Open Layers, or many others already support GeoJSON as one of the output format of their standard