Evaluates changes in pixel values over time using the Landsat-based detection of trends in disturbance and recovery (LandTrendr) method and generates a change analysis raster containing the model results.
للحصول على معلومات عن خوارزمية LandTrendr، راجع موضوع كيفية عمل تحليل التغييرات باستخدام LandTrendr.
ملاحظة:
لا يتم دعم دالة البيانات النقطية هذه إلا من خلال الاقتران بدالة اكتشاف التغيير باستخدام تحليل التغيير. استخدم طبقة الإخراج لدالة تحليل LandTrendr كإدخال في دالة اكتشاف التغيير باستخدام تحليل التغيير. لإنتاج إخراج مجموعة بيانات نقطية، قم بربط دالة تحليل LandTrendr بدالة اكتشاف التغيير باستخدام تحليل التغيير باستخدام محرر الدوال، واحفظ هذا كقالب دالة بيانات نقطية، واستخدمه كإدخال في أداة المعالجة الجغرافية إنشاء بيانات نقطية من دالة بيانات نقطية.
ملاحظات
لا يمكن استخدام دالة البيانات النقطية هذه إلا في صورة إدخال لدالة البيانات النقطية اكتشاف التغيير باستخدام تحليل التغيير. لإنتاج إخراج مجموعة بيانات نقطية، قم بربط دالة تحليل LandTrendr بدالة اكتشاف التغيير باستخدام تحليل التغيير في قالب دالة بيانات نقطية، واستخدم القالب كإدخال في أداة المعالجة الجغرافية إنشاء بيانات نقطية من دالة بيانات نقطية. ينتج من ذلك بيانات نقطية تحتوي على معلومات تتعلق بالوقت الذي تغيرت فيه قيم البكسل.
This tool extracts changes in an observed feature, so the ideal input multidimensional imagery should capture a consistent observation throughout time and should not include atmospheric or sensor interference, clouds, or cloud shadow. The best practice is to use data that has been normalized and can be masked using a QA band—for example, Landsat Collection 1 Surface Reflectance products with a cloud mask.
The tool performs analysis on one image per year, and the number of yearly slices must be equal to or greater than the value specified in the Minimum Number of Observations parameter. It is recommended that you have at least six years of data.
If you have monthly, weekly, or daily data, it is recommended that you select several images from each year (preferably from the same season), remove clouds and cloud shadow, and combine the images to generate a single image that captures the observation well. If monthly, weekly, or daily data is provided as the input multidimensional raster, the tool will identify one slice for analysis based on the date closest to that provided in the Snapping Date parameter.
A feature in a landscape will often take time to recover from a nonpermanent change such as a forest fire or insect infestation. To control the rate of recovery recognized by the model, set the Recovery Threshold parameter. A distinct segment cannot have a recovery rate that is faster than 1/recovery threshold.
The recovery from a change in landscape can occur in the positive or negative direction. For example, when a landscape experiences forest loss, a time series of vegetation index values shows a drop in index values, and the recovery shows a gradual increase in vegetation index values, or a positive recovery trend. Specify the direction of recovery trend with the Recovery Has Increasing Trend parameter.
المعلمات
| المعلمة | الوصف |
|---|---|
البيانات النقطية | طبقة البيانات النقطية متعددة الأبعاد لـ Landsat الخاصة بالإدخال. |
اسم نطاق المعالجة | The image band name to use for segmenting the pixel value trajectories over time. Choose the band name that will best capture the changes in the feature you want to observe. |
تاريخ الانطباق | The date used to identify a slice for each year in the input multidimensional dataset. The slice with the date closest to the snapping date will be used. This parameter is required if the input dataset contains sub-yearly data. |
الحد الأقصى لعدد المقاطع | The maximum number of segments to be fitted to the time series for each pixel. The default is 5. |
تم تجاوز عدد النقاط القممية | The number of additional vertices beyond max_num_segments + 1 that can be used to fit the model during the initial stage of identifying vertices. Later in the modeling process, the number of additional vertices will be reduced to max_num_segments + 1. The default is 2. |
حد الإدخال الخاص | The threshold to use for dampening spikes or anomalies in the pixel value trajectory. The value must range between 0 and 1 in which 1 means no dampening. The default is 0.9. |
حد الاسترداد | The recovery threshold value in years. If a segment has a recovery rate that is faster than 1/recovery threshold, the segment is discarded and not included in the time series model. The value must range between 0 and 1. The default is 0.25. |
الحد الأدنى لعدد الملاحظات | The minimum number of valid observations required to perform fitting. The number of years in the input multidimensional dataset must be equal to or greater than this value. The default is 6. |
حد قيمة P | The p-value threshold for a model to be selected. After the vertices are detected in the initial stage of the model fitting, the tool will fit each segment and calculate the p-value to determine the significance of the model. On the next iteration, the model will decrease the number of segments by one and recalculate the p-value. This will continue and, if the p-value is smaller than the value specified in this parameter, the model will be selected and the tool will stop searching for a better model. If no such model is selected, the tool will select a model with a p-value smaller than the lowest p-value × best model proportion value. The default is 0.01. |
أفضل نسبة نموذج | The best model proportion value. During the model selection process, the tool will calculate the p-value for each model and identify a model that has the most vertices while maintaining the smallest (most significant) p-value based on this proportion value. A value of 1 means the model has the lowest p-value but may not have a high number of vertices. The default is 1.25. |
منع الاسترداد لمدة عام | Specifies whether segments that exhibit a one year recovery will be excluded.
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يتضمن الاسترداد اتجاه زيادة | Specifies whether the recovery has an increasing (positive) trend.
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إخراج نطاقات أخرى | Specifies whether other bands will be included in the results.
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